The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea
阻塞性睡眠呼吸暂停中心率变异性的不同方面

Abstract 抽象的

Introduction 介绍

Obstructive sleep apnea (OSA) is closely associated with neurocognitive, behavioral, psychophysiological states, and cardiovascular outcomes (1–3). It is estimated that globally ~1 billion adults have mild to severe sleep apnea. Some countries have a prevalence over 50% and it still is increasing. The consequent health and financial burden can be minimized by effective diagnosis and treatment (4). To that effect, recently, the role of cardiovascular autonomic dysfunction has received increasing attention as an independent risk factor for clinical complications in OSA (5). Heart rate variability (HRV) has been generally accepted as a non-invasive tool to quantify cardiovascular autonomic modulation under varying healthy and pathogenic conditions (6, 7). HRV measures the variation between beat-to-beat intervals over a time series (6). It is an integrated reflection of central-peripheral neural feedback mechanisms to the heart via mediating sympathovagal inflow and outflow (8). Previous studies suggested that in conjunction with brain imaging, HRV analysis has been used to investigate the connection between autonomic cardiac modulation and sleeping brain activity (9).
阻塞性睡眠呼吸暂停 (OSA) 与神经认知、行为、心理生理状态和心血管结局密切相关 ( 1 – 3 )。据估计，全球约有 10 亿成年人患有轻度至重度睡眠呼吸暂停。一些国家的患病率超过 50%，并且仍在增加。通过有效的诊断和治疗可以最大限度地减少由此产生的健康和经济负担（ 4 ）。为此，最近，心血管自主神经功能障碍作为 OSA 临床并发症的独立危险因素受到越来越多的关注 ( 5 )。心率变异性 (HRV) 已被普遍认为是量化不同健康和致病条件下心血管自主调节的非侵入性工具 ( 6 , 7 )。 HRV 测量时间序列中逐次心跳间隔之间的变化 ( 6 )。它是中枢-外周神经反馈机制通过介导交感迷走神经流入和流出对心脏的综合反映（ 8 ）。先前的研究表明，HRV 分析与脑成像相结合，已被用来研究自主心脏调节与睡眠大脑活动之间的联系 ( 9 )。

Currently, HRV analysis, including time-domain, frequency-domain, and non-linear analysis, is used to explore the activities of sympathetic and parasympathetic nervous systems (6, 10). Time-domain analysis quantifies the magnitudes of variation. The most relevant time-domain parameters are described in Table 1. For example, the standard deviation of normal-to-normal intervals (SDNN), a global HRV metric, is frequently used as a prognostic indicator of cardiovascular risk in different populations (11). Frequency-domain analysis is used for partitioning the rhythms of electrocardiography (ECG) signals into different frequencies (12, 13). This analysis helps gain a better understanding of cardiac control as ECG frequencies could be related to intrinsic elements modulated by the cardiac autonomic system alone. Power spectral density (PSD) is the standard method employed to estimate the distribution of the HRV signal power over frequency. Table 2 shows the main frequency-domain parameters typically computed from the PSD of HRV (14). High frequency (HF) components mainly present parasympathetic activity. However, there is a disagreement with regards to the low frequency (LF) components. Some studies suggested that LF, when expressed in normalized units, is a quantitative marker of sympathetic modulation, but other studies view LF as a reflection of both sympathetic and vagal activity mainly mediated by the baroreflex. Thus, the LF/HF ratio is considered a detection index for either sympathovagal balance or sympathetic modulations (15). Apart from the conventional PSD, other frequency-domain methods are also used to analyze the frequency content of the HRV, such as high order spectral analysis and wavelet analysis. Non-linear HRV captures dynamic sequences of the heartbeat time series related to randomness and self-similarity (10, 16). It is suggested that non-linear fluctuations result from interactions of electrophysiological, hemodynamic, and humoral variables, as well as by autonomic and central nervous regulation (17). Pathologically monotonous and erratic HRV patterns are associated with negative outcomes in cardiac patients (18). OSA patients show a reduced dynamic complexity (19, 20). The clinical relevance of non-linear HRV in OSA still needs to be the established. Table 3 summarizes the reported non-linear parameters and methods in current studies on HRV (6, 14, 21). However, this not by any means an exhaustive list.
目前，HRV分析，包括时域、频域和非线性分析，用于探索交感神经和副交感神经系统的活动( 6 , 10 )。时域分析量化变化的幅度。最相关的时域参数如表 1所示。例如，正常与正常区间的标准差 (SDNN)（一种全局 HRV 指标）经常被用作不同人群心血管风险的预后指标 ( 11 )。频域分析用于将心电图 (ECG) 信号的节律划分为不同的频率 ( 12 , 13 )。该分析有助于更好地了解心脏控制，因为心电图频率可能与仅由心脏自主系统调节的内在元素相关。功率谱密度 (PSD) 是用于估计 HRV 信号功率随频率分布的标准方法。表 2显示了通常根据 HRV ( 14 ) 的 PSD 计算得出的主要频域参数。高频（HF）成分主要表现为副交感神经活动。然而，对于低频（LF）分量存在分歧。一些研究表明，当以标准化单位表达时，LF 是交感神经调节的定量标记，但其他研究认为 LF 反映了主要由压力反射介导的交感神经和迷走神经活动。因此，低频/高频比被认为是交感迷走神经平衡或交感神经调制的检测指标（ 15 ）。 除了传统的PSD之外，还使用其他频域方法来分析HRV的频率内容，例如高阶谱分析和小波分析。非线性 HRV 捕获与随机性和自相似性相关的心跳时间序列的动态序列 ( 10 , 16 )。有人认为，非线性波动是由电生理、血流动力学和体液变量的相互作用以及自主神经和中枢神经调节引起的（ 17 ）。病理上单调且不稳定的 HRV 模式与心脏病患者的负面结果相关 ( 18 )。 OSA 患者的动态复杂性降低 ( 19 , 20 )。 OSA 中非线性 HRV 的临床相关性仍需确定。表 3总结了当前 HRV 研究中报道的非线性参数和方法 ( 6,14,21 )。然而，这绝不是一份详尽的清单。

Heart rate and blood pressure oscillations are characterized by parasympathetic predominance and sympathetic inhibition in normal subjects during non-rapid eye movement (NREM) sleep (22). In contrast, sympathetic predominance and parasympathetic withdrawals are found during similar rapid eye movement (REM) sleep and wakefulness. As a result, there is a reduction of heart rate and blood pressure during NREM sleep and an increase during REM sleep. However, patients with OSA manifest a heterogeneous pathophysiology (e.g., upper airway anatomical collapsibility, loop gain, arousal threshold, and upper airway gain) and characteristics (e.g., recurrent apnea and hypopnea, nocturnal hypoxemia, frequent awakenings, and daytime sleepiness) (23). Consequently, hypoxia and arousal in OSA are thought to potentially be the main factors leading to certain hemodynamic instability, causing fluctuations in heart rate that contribute to the changes in HRV. Previous studies have shown the detrimental effect of OSA on HRV either during wakefulness or sleep (24–26), suggesting a relationship between OSA severity and cardiovascular autonomic modulations using conventional HRV analysis. Additionally, (27). suggested that prolonged alterations in autonomic function existed even in snoring subjects. Those findings highlighted the potential cumulative impacts of OSA on HRV. On the other hand, Idiaquez et al. (28) found independent pathophysiological mechanisms may underlie the modulation of neurobehavioral changes and HRV in OSA despite sharing common cerebral control regions and mediated pathways. Although the HRV time window is more related to mathematics, physics and statistics, its determination in OSA-related events (e.g., sleep apnea, arousal, and periodic limb movement) is crucial in reflecting the relationship between autonomic changes and OSA-related physiological changes. Furthermore, it allows for the discovery of how the cardiovascular, respiratory, autonomic, and central nervous systems interact with each other in OSA. The HRV time window is also particularly important in coupling analysis such as synchronization and ensemble symbolic coupling, potentially revealing direction and strength of dynamic cardiovascular transition (29, 30).
正常受试者在非快速眼动 (NREM) 睡眠期间心率和血压波动的特点是副交感神经占主导地位和交感神经受到抑制 ( 22 )。相比之下，在类似的快速眼动 (REM) 睡眠和清醒状态下，会出现交感神经优势和副交感神经退缩。因此，NREM 睡眠期间心率和血压会降低，而 REM 睡眠期间心率和血压会增加。然而，OSA 患者表现出异质的病理生理学（例如，上气道解剖塌陷、循环增益、觉醒阈值和上气道增益）和特征（例如，反复呼吸暂停和呼吸不足、夜间低氧血症、频繁觉醒和白天嗜睡）（ 23 ）。因此，OSA 中的缺氧和觉醒被认为可能是导致某些血流动力学不稳定的主要因素，导致心率波动，进而导致 HRV 变化。先前的研究表明，OSA 在清醒或睡眠期间对 HRV 产生有害影响 ( 24 – 26 )，这表明使用传统 HRV 分析，OSA 严重程度与心血管自主调节之间存在关系。另外，( 27 )。表明即使在打鼾的受试者中也存在自主神经功能的长期改变。这些发现强调了 OSA 对 HRV 的潜在累积影响。另一方面，伊迪亚克斯等人。 ( 28 ) 发现独立的病理生理学机制可能是 OSA 中神经行为变化和 HRV 调节的基础，尽管共享共同的大脑控制区域和介导途径。 虽然HRV时间窗与数学、物理和统计学的关系较多，但其在OSA相关事件（如睡眠呼吸暂停、觉醒和周期性肢体运动）中的确定对于反映自主神经变化和OSA相关生理变化之间的关系至关重要。此外，它还可以发现 OSA 中心血管、呼吸、自主神经和中枢神经系统如何相互作用。 HRV 时间窗在同步和整体符号耦合等耦合分析中也特别重要，可能揭示动态心血管转换的方向和强度 ( 29 , 30 )。

Taken together, HRV could provide a static and a dynamic perspective to observe the changes in connectivity between central and cardiac autonomic modulation during sleep and its persistent influence during daytime. This review focuses on neuro-cardiac autonomic regulatory mechanisms and the multifaceted applications of HRV in OSA as a potential additional clinical diagnostic tool.
总而言之，HRV 可以提供静态和动态的视角来观察睡眠期间中枢和心脏自主调节之间的连接变化及其在白天的持续影响。本综述重点关注神经心脏自主调节机制以及 HRV 作为潜在的附加临床诊断工具在 OSA 中的多方面应用。

Time-Window Analysis Technology of HRV
HRV时间窗分析技术

HRV is usually measured over a short-term (5–15 min) or long-term period (1–24 h). Long-term measurements are generally used to assess mortality and adverse prognosis of patients, but short-term measurements have been shown to be sufficiently stable and applicable for screening. However, 5-min recordings only had strong correlation with HF (31). Li et al. (32) assessed short-term analysis to be suitable for estimation of autonomic status and tracking dynamic changes but long-term changes to be better as an autonomic function assessor and prognostic indicator. The issue is that the cardiovascular system is in constant flux and thus HRV parameters constantly fluctuate at rest or during various conditions (33–35). The selection of the time window is thus a crucial aspect in HRV analysis (36, 37).
HRV 通常在短期（5-15 分钟）或长期（1-24 小时）内测量。长期测量通常用于评估患者的死亡率和不良预后，但短期测量已被证明足够稳定且适用于筛查。然而，5 分钟的录音仅与 HF 有很强的相关性（ 31 ）。李等人。 ( 32 )评估短期分析适合估计自主神经状态和跟踪动态变化，但长期变化更适合作为自主神经功能评估器和预后指标。问题是心血管系统处于不断变化中，因此 HRV 参数在休息时或各种条件下不断波动 ( 33 – 35 )。因此，时间窗口的选择是 HRV 分析中的一个关键方面 ( 36 , 37 )。

Most studies use short-term time windows with their analytic techniques; 2–5 min with Fast Fourier Transform (FFT) or autoregression, or 1–2 min with multiple trigonometric regressive spectral (MTRS) analysis (6, 38, 39). New techniques such as short time Fourier transform or Wigner-Ville transforms (WVT) are able to return instant power spectral profiles (40, 41). Short-term windows have the advantages of being easy to perform, easy to control for confounding factors, require the least data processing and describe dynamic HRV changes in short time periods (32). However, the constant flux of HRV values means that it may not be stable and that it cannot measure long RRI fluctuations, especially the ultra-LF (6, 37). Ultra-short term HRV has shown potential for diagnostic capability within a short timespan immediately after an apneic event (e.g., arousals). However, it is only able to measure time-domain parameters and no frequency-domain parameters, severely limiting its informational output and, like short-term HRV, the constant flux may mean it is unstable (42).
大多数研究都使用短期时间窗及其分析技术；快速傅立叶变换 (FFT) 或自回归需要 2–5 分钟，或者多重三角回归谱 (MTRS) 分析需要 1–2 分钟 ( 6 , 38 , 39 )。诸如短时傅立叶变换或维格纳-维尔变换（ WVT ）之类的新技术能够返回即时功率谱分布（ 40、41 ）。短期窗口的优点是易于执行、易于控制混杂因素、需要最少的数据处理并描述短时间内的动态 HRV 变化 ( 32 )。然而，HRV 值的恒定变化意味着它可能不稳定，并且无法测量长时间的 RRI 波动，尤其是超低频 ( 6 , 37 )。超短期 HRV 已显示出在呼吸暂停事件（例如，觉醒）后的短时间内具有诊断能力的潜力。然而，它只能测量时域参数而不能测量频域参数，严重限制了其信息输出，并且与短期 HRV 一样，恒定通量可能意味着它不稳定 ( 42 )。

Longer time windows are commonly analyzed with FFT or autoregression, as they are commonly divided into 1–5-min periods and averaged to provide a mean for the total time segment (6, 36, 37). Alternatively, the entire time window is used as a single data segment, which yields similar results for LF and HF over 24 h (43). Its primary advantage is in collecting stable and reflective ECG data over an extended period of time. Any singular 5-min window can vary wildly from another, and thus measuring HRV over a whole day allows for better estimations of fluctuations (32). However, long-term HRV analysis is financially expensive and labor intensive, on top of requiring more considerations about filtering and analysis (6, 37).
较长的时间窗口通常使用 FFT 或自回归进行分析，因为它们通常分为 1-5 分钟的时间段并进行平均以提供总时间段的平均值 ( 6 , 36 , 37 )。或者，整个时间窗口用作单个数据段，这在 24 小时内对 LF 和 HF 产生相似的结果 ( 43 )。其主要优点是可以在较长时间内收集稳定且反映性的心电图数据。任何单个 5 分钟窗口都可能与另一个窗口有很大差异，因此测量一整天的 HRV 可以更好地估计波动 ( 32 )。然而，长期 HRV 分析不仅需要更多考虑过滤和分析，而且成本高昂且劳动力密集（ 6、37 ）。

Li et al. (32) suggest three main uses of HRV analysis: evaluating autonomic function in specific populations, describing changes in autonomic function, and prognosis. To evaluate autonomic function in specific conditions such as myocardial infarction (MI), hypertension and Parkinson's, short-term analysis may be used (44–47). Long-term analysis can be used for daytime or sleep analysis, or a full 24-h analysis and is thus more suitable for assessing OSA autonomic status, in line with what most studies use. Although HRV analyses of different window lengths are closely correlated, they do not always align (48–50). Studies in this particular area are particularly lacking and require further investigation. In describing change in autonomic function, both short-term and long-term analysis can be used over a period of hours or months, whereas short-term can measure changes in minutes. In this regard, measuring changes due to apneic episodes is a useful application of short-term analysis. However, this type of short-term analysis likely already falls under an overnight long-term analysis (32). Many OSA studies use overnight HRV with 5-min time windows. Still, more studies are needed directly comparing the two with respect to OSA. Using HRV as a prognostic indicator is usually done via long-term analysis. Many studies assessing mortality have used overnight or 24-h HRV analyses to obtain a reliable prognosis and use 5-min windows within these time periods to compare HRV (49–55).
李等人。 ( 32 )提出了HRV分析的三个主要用途：评估特定人群的自主神经功能、描述自主神经功能的变化和预后。为了评估特定情况下的自主功能，例如心肌梗塞（MI）、高血压和帕金森病，可以使用短期分析（ 44 – 47 ）。长期分析可用于白天或睡眠分析，或完整的 24 小时分析，因此更适合评估 OSA 自主状态，与大多数研究使用的方法一致。尽管不同窗口长度的 HRV 分析密切相关，但它们并不总是一致 ( 48 – 50 )。这一特定领域的研究尤其缺乏，需要进一步调查。在描述自主神经功能的变化时，短期和长期分析都可以在几小时或几个月内使用，而短期可以测量几分钟内的变化。在这方面，测量呼吸暂停发作引起的变化是短期分析的一个有用应用。然而，这种类型的短期分析可能已经属于隔夜长期分析（ 32 ）。许多 OSA 研究使用夜间 HRV，时间窗口为 5 分钟。尽管如此，还需要更多的研究来直接比较两者的 OSA 情况。使用 HRV 作为预后指标通常是通过长期分析来完成的。许多评估死亡率的研究都使用过夜或 24 小时 HRV 分析来获得可靠的预后，并使用这些时间段内的 5 分钟窗口来比较 HRV ( 49 – 55 )。

It is clear that the majority studies use long-term HRV analysis for the assessment of OSA, mostly with time-frequency domains. However, whether this is the best use of HRV is not clear as there is a lack of studies reporting on this particular aspect. To further this point, there is no agreement on a single method with which to analyze HRV in sleep apnea as a wide variety of time windows within an overnight sleep study are analyzed in the literature. Studies aimed at short-term changes potentially analyze 2-min epochs around apneas and hypopneas or arousal-free windows or look at the first and last 10-min segments during SDB and stable breathing during NREM, for example (56–58). Long-term analysis aimed studies sometimes look at averaged consecutive 5-min windows in different sleep stages (stage 2 is commonly used as a reflection of NREM sleep) or stable 5-min intervals from each sleep stage or the first 5-min segment of each sleep stage, to name a few (59). The standardization of time window approach to provide a regulated and agreed upon methodology of time window analysis that presents comparable and valuable ECG changes in OSA during an overnight sleep study is an area in pressing need of further study. Although time window analysis is a potent area of research to solidify first, the current use of HRV has shown promise and accuracy in many areas, from prognosis to sleep stage detection.
显然，大多数研究使用长期 HRV 分析来评估 OSA，其中大部分采用时频域。然而，这是否是 HRV 的最佳用途尚不清楚，因为缺乏关于这一特定方面的研究报告。为了进一步说明这一点，由于文献中分析了夜间睡眠研究中的各种时间窗口，因此对于分析睡眠呼吸暂停中的 HRV 的单一方法尚未达成一致。例如，针对短期变化的研究可能会分析呼吸暂停和呼吸不足或无觉醒窗口周围的 2 分钟时间段，或者查看 SDB 期间的第一个和最后 10 分钟段以及 NREM 期间的稳定呼吸 ( 56 – 58 )。长期分析旨在研究有时会观察不同睡眠阶段的平均连续 5 分钟窗口（第 2 阶段通常用作 NREM 睡眠的反映）或每个睡眠阶段的稳定 5 分钟间隔或睡眠阶段的前 5 分钟片段。仅举几例（ 59 ）的每个睡眠阶段。时间窗方法的标准化提供了一种受监管且商定的时间窗分析方法，该方法可以在夜间睡眠研究期间呈现 OSA 中可比较且有价值的心电图变化，这是迫切需要进一步研究的领域。尽管时间窗分析是一个需要首先巩固的有效研究领域，但目前 HRV 的使用在从预后到睡眠阶段检测的许多领域都显示出了前景和准确性。

Technical Features of HRV Measurements
HRV 测量的技术特点

There are some important technical features that affect HRV analysis. In this respect, ECG sampling rate could be critical to the accuracy and reliability of the HRV time series. Two hundred and fifty hertz or higher are recommended, however, given the minor relative errors among various ECG sampling rates, over 100 Hz are acceptable in time-domain, frequency-domain, and non-linear HRV analysis (60–62). Concerning the extraction of RR intervals, there is a big variety of algorithms aimed at detecting the R peaks (63), being the Pan and Tompkins the most well-known one (64). However, artifacts and ectopic beats are usually present in ECG recordings, which can result in non-normal RR intervals, thus affecting HRV analysis. This issue is addressed by detecting and correcting non-normal beats. The detection of non-normal beats can be performed using different automatic methods: time and morphological approaches, methods based on the morphological transformation, wavelet-based approaches, empirical mode decomposition methods, and neural network approaches (65). Conversely, deletion, interpolation (zero-degree, linear interpolation, and cubic spline methods), and adaptive approaches are used to correct non-normal beats (65). However, these methods can also cause measurement errors in the HRV signal, which demands more research efforts on the development of correction methods.
有一些重要的技术特征会影响 HRV 分析。在这方面，心电图采样率对于 HRV 时间序列的准确性和可靠性至关重要。不过，考虑到各种 ECG 采样率之间的相对误差较小，建议使用 250 赫兹或更高的频率，在时域、频域和非线性 HRV 分析中，超过 100 Hz 是可以接受的 ( 60 – 62 )。关于 RR 区间的提取，有多种旨在检测 R 峰值的算法 ( 63 )，其中最著名的是 Pan 和 Tompkins 算法 ( 64 )。然而，ECG 记录中通常存在伪影和异位搏动，这可能导致 RR 间期不正常，从而影响 HRV 分析。此问题已通过检测和纠正非正常节拍得到解决。非正常心跳的检测可以使用不同的自动方法来执行：时间和形态学方法、基于形态学变换的方法、基于小波的方法、经验模态分解方法和神经网络方法（ 65 ）。相反，删除、插值（零度、线性插值和三次样条方法）和自适应方法用于纠正非正常心跳（ 65 ）。然而，这些方法也会导致HRV信号的测量误差，这需要更多的研究工作来开发校正方法。

Influence of Sleep Structure on HRV
睡眠结构对 HRV 的影响

According to the American Academy of Sleep Medicine (AASM), sleep is categorized into non-rapid eye movement (NREM) stages N1, N2, N3, into stage rapid eye movement sleep (REM), and into stage Wake by visual electroencephalogram (EEG), electrooculogram (EOG), and chin electromyogram (EMG) scoring (66). Collectively, studies have reported a general trend in HRV during healthy sleep; LF and the LF/HF ratio are high in Wake and decrease in NREM sleep, peaking once more during REM sleep, while HF follows the opposite trend (67–71). This corresponds to muscle sympathetic and parasympathetic activity observed in sleep (72, 73). Opposingly, Ako et al. (74) reported decreasing LF and LF/HF ratio during NREM and an increase during REM but no differentiation of HF during the NREM and REM stages in healthy sleep. However, Abdullah et al. (75) reported a strong correlation between EEG delta, sigma, and beta bands with HRV parameters (LF, HF, LF/HF ratio). Jurysta et al. (76) and Köhler and Schönhofer (77) reported negative correlations between cardiac vagal predominance and delta sleep EEG and abnormalities in the respective power bands. In contrast, Yang et al. (71) reported a negative relation between cardiac sympathetic regulation and depth of sleep, but not vagal regulation. The repeatability of the measurements in HRV parameter patterns in relation to the sleep stages, however, certifies the suggested physiological activity seen during sleep.
根据美国睡眠医学会（AASM）的规定，睡眠根据视觉脑电图（EEG）分为非快速眼动睡眠（NREM）阶段N1、N2、N3，快速眼动睡眠（REM）阶段和唤醒阶段）、眼电图（EOG）和下巴肌电图（EMG）评分（ 66 ）。总的来说，研究报告了健康睡眠期间 HRV 的总体趋势； LF 和 LF/HF 比率在清醒时较高，在 NREM 睡眠中下降，在 REM 睡眠期间再次达到峰值，而 HF 则遵循相反的趋势 ( 67 – 71 )。这对应于睡眠中观察到的肌肉交感神经和副交感神经活动（ 72、73 ）。相反，Ako 等人。 ( 74 ) 报告称，在健康睡眠的 NREM 和 REM 阶段，LF 和 LF/HF 比率在 NREM 期间降低，在 REM 期间增加，但 HF 没有差异。然而，阿卜杜拉等人。 ( 75 )报道了EEG delta、sigma和beta带与HRV参数（LF、HF、LF/HF比率）之间的强相关性。朱里斯塔等人。 ( 76 ) 以及 Köhler 和 Schönhofer ( 77 ) 报道了心脏迷走神经优势与 Delta 睡眠脑电图以及各自功率带异常之间的负相关性。相比之下，杨等人。 ( 71 )报道心脏交感神经调节和睡眠深度之间存在负相关，但迷走神经调节则不然。然而，与睡眠阶段相关的 HRV 参数模式测量的可重复性证明了睡眠期间所建议的生理活动。

Influence of Sleep Apnea on HRV During Daytime
睡眠呼吸暂停对白天 HRV 的影响

OSA seems to have long-term effect on HRV even during wakefulness with the absence of sleep apnea. Limited data regarding its underlying mechanisms during daytime or ambulatory wake state is reported. It is assumed that autonomic dysfunction plays a key role in persistent OSA related outcomes, leading to a blunted diurnal HRV pattern. Using 10-min ECG segments and muscle sympathetic nerve activity (MSNA) recordings during daytime, Narkiewicz et al. found that the magnitude of cardiovascular variability is associated with the severity of OSA. There was reduced RR variance, increased sympathetic tone and decreased parasympathetic tone in moderate-to-severe OSA populations compared to matched controls (25). Balachandran et al. (78) found significantly different LF, HF, and LF/HF between mild OSA without any symptoms and healthy controls in waking condition. Similarly, Hilton et al. (79) found that at daytime amount of HF power as marker of vagal activity is negatively correlated with the apnea-hypopnea index (AHI) and %HF and LF/HF were shown to be different in OSA patients compared to controls. Respiratory sinus arrhythmia (RSA) is a natural physiological phenomenon reflecting cardiopulmonary coupling characterized by periodic increases and decreases with heartbeat synchronized with respiration, whereby heartbeat increases during inspiration and decreases during expiration. Consequently, normal respiration HRV is different than deep respiration HRV and apneic respiration HRV due to the inspiration-expiration pattern (80). Given the altering effect of respiration on HRV, Khoo et al. (81) developed two modified spectral HRV measures (the modified LF/HF and the average gain relating respiration to RR changes) to show cardiac autonomic alternations in OSA and non-OSA during in relaxed wakefulness and stage 2 sleep compared to standard spectral metrics. They found that the modified spectral HRV measures are more sensitive than the traditional measures, suggesting a respiration–correlated component should be considered in HRV analysis. In addition, Wang et al. (24) suggested that autonomic dysfunction was related to OSA severity. However, they mainly evaluated gender differences in frequency-domain HRV measures, rather than with different levels of severity of OSA, showing significantly higher LF in male patients from wakefulness to sleep state. Park et al. (82) examined the correlation between severity of OSA and overnight HRV during wakefulness in moderate/severe OSA. They found increased total power (TP), LF, LF/HF, and HRV triangular index in the severe group compared to the moderate one. Comparably, Qin et al. (83) found a significant relationship between 5-min HRV measures during wakefulness prior to sleep onset and OSA severity in a large international clinical cohort, suggesting reduced time-domain and non-linear HRV measurements in severe OSA compared to other AHI groups. Moreover, their findings demonstrated that OSA seems to play a significant role in obese patients, showing a shift to sympathetic predominance only in obese patients with more severe OSA with increased LF and higher LF/HF compared to obese patients without OSA. There are also hints that OSA therapy normalizes autonomic balance not only during sleep but also at daytime. Glos et al. (84) found that both continuous positive airway pressure (CPAP) as well as mandibular advancement therapy (MAD) therapy led to increased vagal output to the heart, indicated by increased HRV HF components calculated from 5-min short-time recordings under conditions of controlled breathing at daytime.
即使在没有睡眠呼吸暂停的清醒状态下，OSA 似乎也会对 HRV 产生长期影响。据报道，有关其在白天或动态清醒状态下的基本机制的数据有限。据认为，自主神经功能障碍在持续性 OSA 相关结果中发挥着关键作用，导致昼间 HRV 模式减弱。 Narkiewicz 等人在白天使用 10 分钟心电图片段和肌肉交感神经活动 (MSNA) 记录。发现心血管变异的程度与 OSA 的严重程度相关。与匹配对照相比，中度至重度 OSA 人群的 RR 方差降低，交感神经张力增加，副交感神经张力降低 ( 25 )。巴拉钱德兰等人。 ( 78 )发现没有任何症状的轻度 OSA 与清醒状态下的健康对照之间的 LF、HF 和 LF/HF 存在显着差异。同样，希尔顿等人。 ( 79 ) 发现，白天作为迷走神经活动标志的高频功率量与呼吸暂停低通气指数 (AHI) 呈负相关，并且 OSA 患者的 %HF 和 LF/HF 与对照组不同。呼吸性窦性心律失常（RSA）是一种反映心肺耦合的自然生理现象，其特征是心跳与呼吸同步而周期性增加和减少，即吸气时心跳增加，呼气时心跳减少。因此，由于吸气-呼气模式( 80 )，正常呼吸HRV不同于深呼吸HRV和呼吸暂停呼吸HRV。鉴于呼吸对 HRV 的影响，Khoo 等人。 ( 81 ) 开发了两种修改后的频谱 HRV 测量（修改后的 LF/HF 以及将呼吸与 RR 变化相关的平均增益），以显示与标准频谱指标相比，在轻松清醒和第 2 阶段睡眠期间 OSA 和非 OSA 的心脏自主神经变化。他们发现，修改后的光谱 HRV 测量比传统测量更敏感，这表明在 HRV 分析中应考虑呼吸相关成分。此外，王等人。 ( 24 ) 表明自主神经功能障碍与 OSA 严重程度相关。然而，他们主要评估频域 HRV 测量的性别差异，而不是不同程度的 OSA 严重程度，结果显示男性患者从清醒到睡眠状态的 LF 显着较高。帕克等人。 ( 82 ) 研究了中度/重度 OSA 患者清醒时 OSA 严重程度与夜间 HRV 之间的相关性。他们发现，与中度组相比，重度组的总功率 (TP)、LF、LF/HF 和 HRV 三角指数有所增加。相比之下，秦等人。 ( 83 ) 在大型国际临床队列中发现，入睡前清醒期间的 5 分钟 HRV 测量值与 OSA 严重程度之间存在显着关系，表明与其他 AHI 组相比，严重 OSA 的时域和非线性 HRV 测量值减少。此外，他们的研究结果表明，OSA 似乎在肥胖患者中发挥着重要作用，与没有 OSA 的肥胖患者相比，仅在患有更严重 OSA 的肥胖患者中，交感神经优势发生转变，且 LF 增加，LF/HF 更高。还有迹象表明，OSA 疗法不仅可以在睡眠期间而且可以在白天使自主神经平衡正常化。格洛斯等人。 ( 84 ) 发现持续气道正压通气 (CPAP) 以及下颌前移治疗 (MAD) 治疗都会导致迷走神经向心脏的输出增加，这通过在以下条件下通过 5 分钟短时记录计算得出的 HRV HF 成分增加来表明：白天控制呼吸。

Influence of Sleep Apnea on HRV During Sleep
睡眠呼吸暂停对睡眠期间 HRV 的影响

The normalizing effect of OSA therapy on HRV during sleep has also been suggested. Earlier studies report higher sympathetic activity during wake and sleep, but this has normalized, perhaps because of CPAP (73, 85). This is supported by Noda et al.'s (86) study reporting that managed OSA and better sleep quality was associated with a decreased LF. Since then, Abdullah et al. (75) reported an increase in LF and LF/HF in Stages 2 and 3 in sleep apnea compared with healthy patients. This corresponds with results from Dingli et al. (56) and Jurysta et al. (87), which showed an increase in sympathetic and decrease in parasympathetic activity during NREM apnea episodes. Bonnet and Arand (67) reported EEG arousal during Stage 2 and associated HRV changes. Palma et al. reported OSA with hypoxia patients had increased LF and LF/HF during N1 and N2 and REM compared to OSA without hypoxia patients and controls. They also reported that OSA with and without hypoxia had lower HF during NREM and REM in compared to controls (88). In contrast, Jurysta et al. reported no changes in LF/HF and RRI between healthy and OSA subjects. They did however suggest that sympathetic and vagal surges during apneic episodes may suppress the normal shifts between stages of sleep (76). Trimer et al. reported higher LF and LF/HF in moderate OSA subjects compared to normal subjects. Mild OSA subjects also failed to show the linear HRV difference between sleep stages present in non-OSA subjects (20). Kesek et al. studied the relationship between OSA severity and HRV in 387 women and found that high AHI was associated with low variation of sympathetic activity between REM and NREM, suggesting a depressed sympathetic drive and a disability increasing it during REM. These results differ from others, but the study was in healthy women only and gender differences in HRV have been reported (89). Reynolds et al. found a positive correlation between apnea severity and LF in wakefulness and REM sleep, but LF was lower in those with a higher BMI during REM sleep in 105 OSA patients. The suggestion is thus that there is possible autonomic dysfunction in obese apnea patients (90). On the contrary, Oh et al. (91) conducted a 27-participant study and concluded that OSA during REM sleep is not a major contributor of autonomic dysfunction. However, the study was conducted on a small cohort and requires repeated testing to confirm results. In addition, Lado et al. (92) found significant differences in spectral HRV in all three types of intervals (normal breathing, borderline episodes, and sleep apnea) among non-OSA control, mild, and severe OSA subjects during sleep, suggesting that patients with OSA have reduced HRV during sleep even without the presence of sleep apnea (Figure 1). In addition, Szollosi et al. (58) compared HRV patterns between OSA and central sleep apnea (CSA), finding higher very low frequency (VLF) percentage, lower LF percentage and HF percentage in CSA, while no significant changes during normal breathings between patients with OSA and CSA. Their results suggested that CSA and OSA have different autonomic modulation, respectively. Overall, the research presented shows increased sympathetic activity during apneic sleep with episodic surges in comparison to healthy sleep, reflected via increased LF and LF/HF parameters in HRV.
还提出了 OSA 治疗对睡眠期间 HRV 的正常化作用。早期研究报告称，在清醒和睡眠期间交感神经活动较高，但这种情况已经正常化，这可能是由于 CPAP 的作用 ( 73 , 85 )。 Noda 等人 ( 86 ) 的研究报告支持了这一点，即管理 OSA 和更好的睡眠质量与 LF 降低相关。从那时起，阿卜杜拉等人。 ( 75 ) 报告称，与健康患者相比，睡眠呼吸暂停第 2 阶段和第 3 阶段的 LF 和 LF/HF 有所增加。这与 Dingli 等人的结果相对应。 （ 56 ）和Jurysta等人。 ( 87 )，显示在 NREM 呼吸暂停发作期间交感神经活动增加，副交感神经活动减少。 Bonnet 和 Arand ( 67 ) 报告了第二阶段的脑电图唤醒和相关的 HRV 变化。帕尔马等人。据报道，与无缺氧的 OSA 患者和对照相比，缺氧的 OSA 患者在 N1、N2 和 REM 期间 LF 和 LF/HF 增加。他们还报告说，与对照组相比，有或没有缺氧的 OSA 在 NREM 和 REM 期间的 HF 较低 ( 88 )。相比之下，Jurysta 等人。报告健康受试者和 OSA 受试者之间的 LF/HF 和 RRI 没有变化。然而，他们确实表明呼吸暂停发作期间交感神经和迷走神经的激增可能会抑制睡眠阶段之间的正常转变（ 76 ）。三聚体等人。据报道，与正常受试者相比，中度 OSA 受试者的 LF 和 LF/HF 更高。轻度 OSA 受试者也未能表现出非 OSA 受试者睡眠阶段之间的线性 HRV 差异 ( 20 )。凯塞克等人。 研究了 387 名女性的 OSA 严重程度和 HRV 之间的关系，发现高 AHI 与 REM 和 NREM 之间交感神经活动的低变化相关，这表明在 REM 期间交感神经驱动力减弱，并且交感神经驱动力减弱。这些结果与其他结果不同，但该研究仅针对健康女性，并且已经报道了 HRV 的性别差异（ 89 ）。雷诺兹等人。研究发现，在 105 名 OSA 患者中，呼吸暂停严重程度与清醒和快速眼动睡眠期间的 LF 呈正相关，但在快速眼动睡眠期间体重指数较高的患者中，LF 较低。因此，这表明肥胖呼吸暂停患者可能存在自主神经功能障碍（ 90 ）。相反，Oh 等人。 ( 91 ) 进行了一项 27 名参与者的研究，得出的结论是，快速眼动睡眠期间的 OSA 并不是自主神经功能障碍的主要原因。然而，该研究是在一小群人中进行的，需要反复测试才能确认结果。此外，拉多等人。 ( 92 )发现非 OSA 控制者、轻度和重度 OSA 受试者在睡眠期间所有三种类型的间隔（正常呼吸、边缘发作和睡眠呼吸暂停）的频谱 HRV 存在显着差异，这表明 OSA 患者在睡眠期间 HRV 降低。即使没有睡眠呼吸暂停，也能入睡（图 1 ）。此外，Szollosi 等人。 ( 58 ) 比较了 OSA 和中枢性睡眠呼吸暂停 (CSA) 之间的 HRV 模式，发现 CSA 患者的极低频 (VLF) 百分比较高、LF 百分比和 HF 百分比较低，而 OSA 和 CSA 患者在正常呼吸期间没有显着变化。他们的结果表明，CSA 和 OSA 分别具有不同的自主调节功能。 总体而言，该研究表明，与健康睡眠相比，呼吸暂停睡眠期间交感神经活动增加并出现间歇性激增，这通过HRV 中 LF 和 LF/HF 参数的增加反映出来。

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Figure 1 图1

Depicts an example of the changes in beat-to-beat intervals (BBI) in an obstructive sleep apnea (OSA) subject with (upper) and without (middle) the presence of apneic events and a healthy subject (bottom) during stage 3 sleep in the supine position.
描绘了阻塞性睡眠呼吸暂停 (OSA) 受试者（上）和不存在（中）呼吸暂停事件以及健康受试者（下）在第 3 阶段睡眠期间的心跳间隔 (BBI) 变化的示例处于仰卧位。

In seeing the relation of parameters to apneic sleep, there appears to be potential in using HRV as a cost-effective tool for the detection of apnea. Some studies report that cardiac changes visibly precede EEG changes with a range of 10 beats to 5 min in apneic episodes (67, 76). Penzel et al. (93) reported that it was possible to classify apnea via HRV with 100% accuracy when comparing to normal subjects and 90% when comparing normal and apneic minute intervals in 35 samples. Roche et al. (94) reached sensitivities of 83 and 89.7% and specificities of 98.1 and 96.5% when using SDNN as a marker in the detection of OSA in groups of 91 and 52 patients, respectively. Then using wavelet decomposition parameters in 147 patients, Roche et al. (95) reached a sensitivity of 92.4% and specificity of 90.1%. Karasulu et al. (96) found a 90.4% sensitivity and 50% specificity when using a VLF cut-off of 9.12, 80 and 76.2% when SDNN was higher than 83 and 73.3 and 85.7% with an SDNN cut-off of 62 in 87 patients. Offering a variant to these results, Abdullah et al. combined EEG and HRV at 64% correct classification accuracy, HRV alone at 56% accuracy and EEG alone at 62% accuracy (75). However, this study was conducted on a small population and thus requires further study in order to improve upon the application to classification. Gil et al. (97) used decreases in amplitude fluctuations of photoplethysmography (PPG) with an accuracy of 80%, sensitivity of 87.5% and specificity of 71.4%. Babaeizadeh and Zhou (98) created a novel method of ECG-derived respiration (EDR) combined with HRV for an accuracy of 88% and correct classification of 71%. Similarly, Lyons et al. (99) developed an ECG-derived respiratory power index (RPI) as an estimate for AHI to identify severe OSA in commercial drivers (Figure 2).
从参数与呼吸暂停睡眠的关系来看，使用 HRV 作为检测呼吸暂停的经济有效的工具似乎有潜力。一些研究报告称，呼吸暂停发作时心脏变化明显先于脑电图变化，范围为 10 次心跳至 5 分钟 ( 67 , 76 )。彭泽尔等人。 ( 93 ) 报道称，与正常受试者相比，通过HRV 对呼吸暂停进行分类的准确度为 100%，而在 35 个样本中比较正常和呼吸暂停分钟间隔时，准确度为 90%。罗氏等人。 ( 94 ) 使用SDNN作为检测91名和52名患者组中OSA的标记物时，敏感性分别达到83%和89.7%，特异性分别为98.1%和96.5%。 Roche 等人随后对 147 名患者使用小波分解参数。 ( 95 )的敏感性达到92.4%，特异性达到90.1%。卡拉苏鲁等人。 ( 96 ) 在 87 名患者中发现，当 SDNN 高于 83 时，使用 VLF 截止值 9.12、80 和 76.2% 时，灵敏度为 90.4%；当 SDNN 截止值 62 时，灵敏度为 73.3%；特异性为 85.7%。 Abdullah 等人提供了这些结果的变体。结合 EEG 和 HRV 的正确分类准确度为 64%，单独使用 HRV 的准确度为 56%，单独使用 EEG 的准确度为 62% ( 75 )。然而，这项研究是在小群体中进行的，因此需要进一步研究以改进分类的应用。吉尔等人。 ( 97 )使用光电体积描记法(PPG)振幅波动的减小，准确度为80%，敏感性为87.5%，特异性为71.4%。 Babaeizadeh 和 Zhou ( 98 ) 创建了一种心电图衍生呼吸 (EDR) 与 HRV 相结合的新方法，准确度为 88%，正确分类为 71%。 同样，莱昂斯等人。 ( 99 ) 开发了一种心电图衍生的呼吸功率指数 (RPI) 作为 AHI 的估计值，以识别商业驾驶员中的严重 OSA（图 2 ）。

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Figure 2 图2

Shows an exemplary illustration of the respiratory power index (RPI) and electrocardiograph-derived respiration (EDR) methods in an OSA patient. Overnight electrocardiograph recordings are processed and cut into limited time segments. EDR signals are calculated via ECG respiration embeddings such as QRS complex (A) or respiratory sinus arrhythmia (RSA) (C). Spectrograms of both embeddings are also generated (B,D). These spectrograms are normalized and averaged to amplify the respiration-based component and mask non-respiration-related power (E). The power is calculated at each step with two selection events (F). A respiratory flow shows corresponding events to the power spectrum (G). The number of detected apneic events is the RPI.
显示 OSA 患者的呼吸功率指数 (RPI) 和心电图衍生呼吸 (EDR) 方法的示例说明。隔夜心电图记录被处理并切割成有限的时间段。 EDR 信号通过ECG 呼吸嵌入计算，例如 QRS 复合波(A)或呼吸性窦性心律失常 (RSA) (C) 。还生成两个嵌入的频谱图(B,D) 。这些频谱图经过归一化和平均，以放大基于呼吸的分量并掩盖与呼吸无关的功率(E) 。在每个步骤中通过两个选择事件(F)计算功效。呼吸流量显示与功率谱（G）相对应的事件。检测到的呼吸暂停事件的数量就是 RPI。

There are thus a variety of tools and combinations that appear to have potential in the detection and classification of apnea.
因此，有多种工具和组合似乎在呼吸暂停的检测和分类方面具有潜力。

Collectively this body of studies points to the potential of diagnosing OSA via HRV parameters reflecting sympathetic hyperactivity during sleep, particularly during apneic episodes. However, more research needs to be done in this area as there are conflicting reports on the accuracy of using HRV alone, as compared to coordination with other measurements such as EEG, PPG, or EDR. Showing promise in the application of this idea, Le et al. (100) have made wearable device sensor technology predicting apneic episodes 1–5 min before onset with accuracy of 83.6, 80, 76.2, 66.9, and 61.1%, respectively, that could have many applications.
总的来说，这些研究表明通过反映睡眠期间交感神经过度活跃的 HRV 参数来诊断 OSA 的潜力，特别是在呼吸暂停发作期间。然而，在这一领域还需要进行更多的研究，因为与与 EEG、PPG 或 EDR 等其他测量相协调相比，单独使用 HRV 的准确性存在相互矛盾的报告。 Le 等人在应用这一想法方面表现出了希望。 ( 100 ) 已经开发出可穿戴设备传感器技术，可以在呼吸暂停发作前 1-5 分钟预测呼吸暂停发作，准确率分别为 83.6%、80%、76.2%、66.9% 和 61.1%，具有多种应用前景。

HRV Changes During Arousal
唤醒期间 HRV 变化

Arousal interrupts sleep continuity to cause sleep fragmentation, which may contribute to cognitive impairment, excessive daytime sleepiness and adverse cardiovascular outcomes in OSA (101–103). Quantification of arousal would improve understanding of the underlying mechanism and relationship between arousal and OSA related outcomes (e.g., daytime sleepiness and functioning) (104, 105). Currently, EEG arousal is defined as the abrupt increase in high-frequency EEG activities lasting 3–15 s, following at least 10 s of sleep during NREM sleep. Additionally, increased chin EMG activity is needed during REM sleep according to the AASM criteria (106, 107). However, even if the concept of arousal should be extended, there currently is no agreement on the classification of arousal (108). Arousal could be divided into several states on the basis of specific causes. Two main types of arousal, physiologic (spontaneous or secondary to various stimuli), and pathologic (induced by sleep hypopnea and apnea, upper airway resistance syndrome or periodic limb movement) are commonly accepted (108, 109). Some studies tried to classify arousal manually based on whether an arousal is associated with a physiological event such as cortical arousal, respiratory arousal, cardiac arousal, movement arousal, snoring arousal, or SpO2 arousal (110, 111). It is reported that autonomic arousal does not have visual recognition in the way EEG arousal does. It is plausible that some peripheral stimulations may not be sufficient to lead to cortical visual EEG arousals but can cause cardiovascular perturbation (e.g., heart rate and blood pressure changes) (112–114).
唤醒会中断睡眠连续性，导致睡眠碎片化，这可能会导致 OSA 中的认知障碍、白天过度嗜睡和不良心血管结局 ( 101 – 103 )。唤醒的量化将提高对唤醒和 OSA 相关结果（例如，白天嗜睡和功能）之间的潜在机制和关系的理解（ 104、105 ）。目前，脑电图唤醒被定义为在 NREM 睡眠期间至少 10 秒的睡眠后，持续 3-15 秒的高频脑电图活动突然增加。此外，根据 AASM 标准，在快速眼动睡眠期间需要增加下巴肌电图活动 ( 106 , 107 )。然而，即使唤醒的概念应该扩展，目前对于唤醒的分类也没有达成一致（ 108 ）。根据具体原因，唤醒可以分为几种状态。唤醒的两种主要类型，生理性（自发的或继发于各种刺激）和病理性（由睡眠呼吸不足和呼吸暂停、上气道阻力综合征或周期性肢体运动引起）被普遍接受（ 108、109 ）。一些研究尝试根据唤醒是否与生理事件（例如皮质唤醒、呼吸唤醒、心脏唤醒、运动唤醒、打鼾唤醒或 SpO2唤醒）相关来对唤醒进行手动分类（ 110、111 ）。据报道，自主唤醒并不像脑电唤醒那样具有视觉识别功能。一些外周刺激可能不足以导致皮层视觉脑电图唤醒，但可能引起心血管扰动（例如、心率和血压变化）（ 112 – 114 ）。

In this case, autonomic arousal may be a new entity of arousals in OSA during sleep, possibly undetectable by EEG (115). Thirty percent of respiratory event termination causes are still undetermined. Some research indicates that it might be related to apnea-related autonomic arousal, which tends to be ignored due to its non-visible nature compared with other types of arousal in polysomnography (PSG) (116, 117). As a result, PSG would underestimate arousal severity if only visible EEG arousal counts. The occurrence of arousal induced by different causes varies in NREM and REM sleep (116). The underlying mechanisms between the central nervous system (CNS) and autonomic nervous system (ANS) in arousal is poorly understood. Arousal may be a contributor in cardiac alternations such as heart rate changes and blood pressure fluctuation. HRV changes accordingly since heart rate is accelerated and decelerated immediately pre- and post-arousal. Animal studies confirmed that transient arousal from NREM sleep is associated with acute cardiac sympathetic activation and parasympathetic withdrawal (118). The presence of arousal somehow immediately leads to wakefulness that differs in autonomic changes from rested wakefulness in other conditions (118).
在这种情况下，自主唤醒可能是睡眠期间 OSA 中唤醒的新实体，可能无法通过 EEG 检测到（ 115 ）。百分之三十的呼吸事件终止原因仍未确定。一些研究表明，它可能与呼吸暂停相关的自主唤醒有关，与多导睡眠图 (PSG) 中的其他类型的唤醒相比，由于其不可见的性质，它往往被忽视 ( 116 , 117 )。因此，如果仅计算可见的 EEG 唤醒计数，PSG 就会低估唤醒的严重程度。在 NREM 和 REM 睡眠中，不同原因引起的觉醒的发生情况有所不同（ 116 ）。中枢神经系统（CNS）和自主神经系统（ANS）在唤醒方面的潜在机制尚不清楚。觉醒可能是心脏交替的一个因素，例如心率变化和血压波动。由于心率在觉醒前后立即加速和减速，HRV 也会相应变化。动物研究证实，NREM 睡眠中的短暂唤醒与急性心脏交感神经激活和副交感神经撤退有关（ 118 ）。唤醒的存在会以某种方式立即导致觉醒，其自主神经变化与其他条件下的休息觉醒不同（ 118 ）。

Daytime cardiac vagal modulation improves due to the reduction of the frequency of arousals, suggesting arousal may trigger cardiac vagal inhibition. OSA is strongly related to hypertension, which is mainly attributed to sympathetic hyperactivity and/or vagal withdrawal causing a surge in heart rate and blood pressure during apnea-arousal episodes (119, 120). Study of autonomic arousals may help understanding why OSA patients with daytime sleepiness are associated with a higher risk of developing adverse CV outcomes such as hypertension and cardiac sudden death (121). It is reported that patients with co-morbid OSA and insomnia have a significantly higher number of arousals during sleep than OSA alone (122). Bennett et al. (123) found significant correlations between the autonomic arousal index based on pulse transit time analysis and pretreatment objective sleepiness (r = 0.49) and nCPAP responsive objective sleepiness (r = 0.44), suggesting autonomic arousal detection should be taken into account as a sleep fragmentation index to quantify sleepiness. Bartels et al. tried to define autonomic arousal. They found that lower blood pressure and high heart rate in the 15-s window before short-term cortical arousal and cardiovascular changes shift in the opposite direction after sleep recovery (110).
由于觉醒频率的减少，白天心脏迷走神经调节得到改善，这表明觉醒可能会触发心脏迷走神经抑制。 OSA 与高血压密切相关，这主要是由于交感神经过度活跃和/或迷走神经退缩导致呼吸暂停唤醒期间心率和血压激增 ( 119 , 120 )。对自主神经唤醒的研究可能有助于理解为什么白天嗜睡的 OSA 患者与发生高血压和心源性猝死等不良心血管结局的风险较高相关（ 121 ）。据报道，患有 OSA 和失眠共病的患者在睡眠期间的觉醒次数明显高于单独患有 OSA 的患者 ( 122 )。贝内特等人。 ( 123 ) 发现基于脉搏传导时间分析的自主唤醒指数与治疗前客观嗜睡 ( r = 0.49) 和 nCPAP 反应性客观嗜睡 ( r = 0.44) 之间存在显着相关性，表明自主唤醒检测应被视为睡眠碎片量化睡意的指数。巴特尔斯等人。试图定义自主神经唤醒。他们发现，在短期皮质唤醒之前的 15 秒窗口内，血压较低，心率较高，而睡眠恢复后，心血管变化则向相反方向转变 ( 110 )。

HRV provides insight to the processing of arousal response during sleep and improves the definition of arousal, criteria of detection and scoring, although it is still controversial. It should be included in the assessment of OSA for its useful clinical value. EEG arousal generally does not cause behavioral awakenings. However, arousal threshold measured by esophageal pressure, a gold standard for upper airway resistance syndrome, is invasive in clinical practice. On the other hand, cardiac arousal may reflect a neural response to stimuli. Little is known about the accumulation of persistent hyperarousal conditions in OSA. HRV would be a sensitive physiological index of autonomic arousal requiring more investigation. Further research is needed to understand the connectivity and interaction between the heart, its intrinsic nervous system, and the brain.
HRV 提供了对睡眠期间唤醒反应处理的深入了解，并改进了唤醒的定义、检测标准和评分，尽管它仍然存在争议。因其有用的临床价值，应将其纳入 OSA 评估中。脑电图唤醒通常不会引起行为觉醒。然而，通过食管压力测量的唤醒阈值（上气道阻力综合征的金标准）在临床实践中具有侵入性。另一方面，心脏唤醒可能反映了对刺激的神经反应。对于 OSA 中持续性过度警觉症状的积累知之甚少。 HRV 将是自主神经唤醒的一个敏感的生理指标，需要更多的研究。需要进一步的研究来了解心脏、其内在神经系统和大脑之间的连接和相互作用。

Daytime Sleepiness and HRV
日间嗜睡和 HRV

On the other end of arousal, daytime sleepiness, a multifactorial psychophysiological state, is one of the predominant symptoms in OSA (124, 125). Currently, the existing findings suggest that daytime sleepiness depends on the quantity and quality of prior sleep. Patients with OSA commonly suffer from reduced sleep quality that is related to fragmented sleep (126). Sleep disturbances caused by arousal are important contributors to sleepiness (123, 127, 128). Moreover, the frequency of arousal has more impact on sleep recovery than the amount of sleep (129). Subjective and objective sleepiness is often assessed by the Epworth sleepiness scale (ESS) and multiple sleep latency test (MSLT) (130, 131). ESS is a measure of a person's general daytime sleepiness, where a score ≥10 could be diagnosed as excessive daytime sleepiness. As a gold standard, the cut-off point of MSLT is still debatable based on the types of patients. According to the AASM, a sleep latency during MSLT of <8 min is defined as sleepiness. However, it is also suggested that mean sleep latency in MSLT <5 min is considered as pathological sleepiness, 5–10 min is suspected sleepiness, and 10–20 min is normal (130).
另一方面，白天嗜睡是一种多因素的心理生理状态，是 OSA 的主要症状之一 ( 124 , 125 )。目前，现有的研究结果表明，白天的困倦程度取决于之前睡眠的数量和质量。 OSA 患者通常会遭受睡眠质量下降的困扰，这与睡眠碎片化有关 ( 126 )。由唤醒引起的睡眠障碍是导致嗜睡的重要因素（123、127、128 ） 。此外，觉醒频率对睡眠恢复的影响比睡眠量更大（ 129 ）。主观和客观嗜睡通常通过 Epworth 嗜睡量表 (ESS) 和多次睡眠潜伏期测试 (MSLT) 进行评估 ( 130 , 131 )。 ESS 是衡量一个人白天总体嗜睡程度的指标，得分≥10 即可诊断为白天过度嗜睡。作为金标准，MSLT 的分界点仍然存在争议，具体取决于患者的类型。根据 AASM，MSLT 期间的睡眠潜伏期为 <8 id=276>130 )。

The prevalence of excessive daytime sleepiness (EDS) in OSA varies from 19 to 87.2% (132–134). However, 50% of individuals with moderate to severe OSA do not report EDS. Lombardi et al. (135) demonstrated that OSA patients with EDS had significantly lower baroreflex sensitivity and significantly higher low-to-high frequency power ratio of HRV during the different stages of nocturnal sleep compared to those without. Furthermore, subjects with EDS have a more blunted parasympathetic and more enhanced sympathetic cardiac drive during all sleep stages suggesting EDS is associated with cardiac autonomic imbalance. Guaita et al. (136) tested whether spectral and non-linear HRV help to differentiate sleep disordered breathing (SDB) patients with and without objective sleepiness, as assessed by the first 3 min of wakefulness during MSLT before sleep onset. Non-linear HRV (Correntropy) failed to detect sleepiness between groups.
OSA 中白天过度嗜睡 (EDS) 的患病率从 19% 到 87.2% 不等 ( 132 – 134 )。然而，50% 的中度至重度 OSA 患者没有报告 EDS。隆巴尔迪等人。 ( 135 )表明，与未患有EDS的OSA患者相比，患有EDS的OSA患者在夜间睡眠的不同阶段具有显着较低的压力反射敏感性和显着较高的HRV低频与高频功率比。此外，患有 EDS 的受试者在所有睡眠阶段的副交感神经更加迟钝，交感心脏驱动更加增强，这表明 EDS 与心脏自主神经失衡有关。瓜伊塔等人。 ( 136 ) 测试了频谱和非线性 HRV 是否有助于区分有或没有客观嗜睡的睡眠呼吸障碍 (SDB) 患者，通过入睡前 MSLT 期间清醒的前 3 分钟进行评估。非线性 HRV（Correntropy）无法检测组间的睡意。

However, some studies show that ESS increases with the severity of OSA (2, 137). EDS is not always related to AHI as a number of patients with moderate-to-severe OSA did not report subjective EDS in this evaluation (124, 135, 138). It raised the question of whether ESS is not adequately sensitive to detect sleepiness or if there are other underlying physiopathological mechanisms causing the development of sleepiness in OSA patients. Montemurro et al. (139) found severe OSA without EDS has higher very low frequency-HRV compared to those with EDS, indicating higher sympathetic heart rate control in sleepy patients. However, Sforza et al. (140) found that both diurnal and nocturnal time domain and frequency domain HRV failed to differ sleepy and non-sleepy elderly with unrecognized OSA according to ESS. Time with SaO2 <90% and total autonomic arousals were not significantly different between these two groups. Similarly, Bisogni et al. (141) reported that there is no correlation between EDS assessed by ESS and sympathetic activation in patients with mild to moderate OSA.
然而，一些研究表明 ESS 随 OSA 的严重程度而增加 ( 2 , 137 )。 EDS 并不总是与 AHI 相关，因为许多中度至重度 OSA 患者在本次评估中没有报告主观 EDS ( 124 , 135 , 138 )。它提出了这样一个问题：ESS 是否对检测困倦不够敏感，或者是否存在其他潜在的病理生理机制导致 OSA 患者出现困倦。蒙特穆罗等人。 ( 139 )发现，与有EDS的患者相比，没有EDS的严重OSA具有更高的极低频HRV，表明困倦患者的交感心率控制更高。然而，斯福尔扎等人。 ( 140 ) 发现，根据 ESS，昼间和夜间的时域和频域 HRV 无法区分困倦和非困倦老年人以及未识别的 OSA。 SaO2 时间 <90 id=288>141) 报道称，ESS 评估的 EDS 与轻度至中度 OSA 患者的交感神经激活之间没有相关性。

HRV as a Risk Marker for Sleepiness Related Accidents
HRV 作为嗜睡相关事故的风险标记

There is little doubt that attentional deficits affect driving capacity. Detection of drowsiness is importance in order to prevent road accidents due to SDB related sleepiness (142). Chua et al. (143) suggested that HRV has a strong association with psychomotor performance measured by psychomotor vigilance tests (PVT) to quantify vigilance performance in drivers. It is in line with previous studies using HRV in machine learning models to predict hypersomnolence in drivers with 90% accuracy (144–146).
毫无疑问，注意力缺陷会影响驾驶能力。为了防止由于 SDB 相关的困倦而导致的道路事故，困倦的检测非常重要 ( 142 )。蔡等人。 ( 143 )表明HRV与通过精神运动警觉测试(PVT)测量的精神运动表现有很强的相关性，以量化驾驶员的警觉表现。这与之前在机器学习模型中使用 HRV 预测驾驶员嗜睡的研究结果一致，准确率达 90% ( 144 – 146 )。

It has been shown that sleepy OSA patients have a higher prevalence of adverse cardiovascular outcomes (e.g., hypertension) than non-sleepy OSA patients (147). Furthermore, excessively sleepy OSA patients are at increased risk of incident cardiovascular disease (CVD) compared to other OSA symptom subtypes (Disturbed Sleep, Minimally Symptomatic, and Moderately Sleepy) (121). However, ESS might not be reliable to evaluate the relationship between sleepiness and cardiovascular risk, a surrogate marker of sympathetic activity. MSLT is too time-consuming and costly to be a screening tool to score EDS. Given the association between sleepy OSA and cardiovascular disease has not been established, improving discrimination of sleepiness in OSA patients and the relationship between the severity of daytime sleepiness and HRV in larger-scale studies is required.
研究表明，困倦的 OSA 患者比非困倦的 OSA 患者有更高的不良心血管结局（例如高血压）发生率 ( 147 )。此外，与其他 OSA 症状亚型（睡眠障碍、轻微症状和中度困倦）相比，过度困倦的 OSA 患者发生心血管疾病 (CVD) 的风险增加 ( 121 )。然而，ESS 可能无法可靠地评估困倦与心血管风险（交感神经活动的替代指标）之间的关系。 MSLT 太耗时且成本太高，无法作为 EDS 评分的筛选工具。鉴于困倦 OSA 与心血管疾病之间的关联尚未确定，需要在更大规模的研究中改善对 OSA 患者困倦的辨别以及日间困倦严重程度与 HRV 之间的关系。

Previous studies have proven that CPAP could reduce daytime sleepiness (148). Less benefit from CPAP was found in OSA patients without symptoms than those with, suggesting treatments should be tailored (149–151). There are still 13% of patients with residual EDS after optimal CPAP treatment (152). They also found that the prevalence of residual excessive sleepiness was higher in moderate OSA than severe OSA, suggesting there is an underlying determinant contributing to EDS other than the severity of intermittent hypoxia and AHI. One of the possible determinants could be autonomic dysfunction during sleep. Abnormal autonomic regulation is also known to have an association with higher cardiovascular events in OSA. A possible relation between EDS and cardiovascular events in patients with OSA should be investigated in future studies (i.e., how autonomic dysfunction relates to the presence of EDS and contributes to its relevant consequences in these population).
先前的研究已证明 CPAP 可以减少白天的嗜睡 ( 148 )。没有症状的 OSA 患者从 CPAP 中获得的益处比有症状的患者要少，这表明治疗应该量身定制 ( 149 – 151 )。在最佳CPAP治疗后仍有13%的患者残留EDS（ 152 ）。他们还发现，中度 OSA 中残留过度嗜睡的发生率高于重度 OSA，这表明除了间歇性缺氧和 AHI 的严重程度之外，还有一个导致 EDS 的潜在决定因素。可能的决定因素之一可能是睡眠期间的自主神经功能障碍。众所周知，自主神经调节异常与阻塞性睡眠呼吸暂停 (OSA) 患者心血管事件的发生率较高有关。在未来的研究中应该调查 EDS 与 OSA 患者心血管事件之间可能的关系（即自主神经功能障碍如何与 EDS 的存在相关以及如何导致这些人群中的相关后果）。

HRV Changes Due to Hypoxia
缺氧导致 HRV 变化

Exposure to hypoxia is a leading cause of oxidative stress, inflammation, and sympathetic hyperactivity (153). Recurrent oxygen desaturation induced by sleep apnea, one of the distinct features of OSA differing from non-OSA, may be associated with elevated sympathetic nervous activity and blood pressure (153). Additionally, Watson et al. (154) found that the severity of hypoxia is related to graded autonomic dysfunction. Both animal and human experiments demonstrated that the failure to restore cardiovascular adjustment capacity can be ascribed to impaired nerves and blunt responses of the autonomic system as a result of intermittent hypoxemia in OSA (155, 156). A systematic review shows that either SpO2 or SaO2 used to assess arterial oxygen saturation is correlated with time-frequency HRV during hypoxia in normal people at rest (157). Botek et al. (158) found lower arterial oxygen saturation (SpO2) in significantly reduced vagal withdrawal (Ln HF) and increased sympathetic-vagal balance, suggesting SpO2 level is related to the reaction of autonomic control to hypoxia. Their aim was to investigate if HRV could be used as a predictor of SpO2 response to hypoxic challenges in subjects normoxic at rest. Nevertheless, it is admitted that changes in detailed HRV parameters are not consistently similar due to the varying experimental protocols (e.g., the duration, severity, and types of hypoxia).
缺氧是氧化应激、炎症和交感神经过度活跃的主要原因（ 153 ）。睡眠呼吸暂停引起的反复性氧饱和度降低是 OSA 不同于非 OSA 的显着特征之一，可能与交感神经活动和血压升高有关 ( 153 )。此外，沃森等人。 ( 154 )发现缺氧的严重程度与分级自主神经功能紊乱有关。动物和人体实验均表明，心血管调节能力未能恢复可归因于 OSA 间歇性低氧血症导致的神经受损和自主系统反应迟钝 ( 155 , 156 )。系统评价表明，用于评估动脉血氧饱和度的 SpO2 或 SaO2 与正常人静息时缺氧期间的时频 HRV 相关 ( 157 )。博泰克等人。 ( 158 )发现迷走神经退缩(Ln HF)显着减少和交感迷走神经平衡增加导致动脉血氧饱和度(SpO2)降低，这表明SpO2水平与自主神经控制对缺氧的反应有关。他们的目的是调查 HRV 是否可以作为 SpO2 对休息时含氧量正常的受试者对低氧挑战反应的预测指标。然而，我们承认，由于不同的实验方案（例如缺氧的持续时间、严重程度和类型），详细 HRV 参数的变化并不总是相似。

OSA generally generates a decrease in HRV during normobaric hypoxia in most reported investigations. However, there are still underlying complex central-peripheral interactions and modulation pathways in vulnerable populations. To address those issues, a growing body of studies have attempted to investigate the hypoxia burden in OSA (159–161). Time-dependent static and dynamic desaturation give more insight to the severity of hypoxia. Acute and chronic hypoxia may lead to different autonomic modulation mechanisms. Hypoxia activated chemoreflex leads to acutely increased short-term sympathetic tone during the occurrence of sleep apnea (54). Furthermore, hypoxia exerted long-lasting chronic effects during the daytime and impaired baroreflex sensitivity (162). Meanwhile whether or not sympathetic hyperactivity induces parasympathetic inhibition is still controversial. The overall reduced HRV with increased sympathetic tone resulted from chronic hypoxia, while a rise in HRV with decreased vagal withdrawal occurred due to the subsequent adaptation and improved tolerance to short-term exposure to repeated hypoxic stress (163). Geovanini et al. demonstrated a vicious circle between hypoxia-induced inflammation and cardiac autonomic abnormality with elevated sympathetic or reduced parasympathetic tone. They also found the values of SDNN, LF, and HF are closely linked to OSA severity while only mean heart rate significantly correlated with augments in neutrophils (164). In an OSA children study, Walter et al. (165) found that OSA may have negative influence on cerebral blood flow due to the attenuated central autonomic control by mediating HRV. Therefore, it is reasonable to believe that different cardiac autonomic modulation responses occur either due to reduced vagal modulation, sympathetic predominance, or even a combination of these responses.
在大多数报告的研究中，OSA 通常会在常压缺氧期间导致 HRV 降低。然而，弱势群体中仍然存在潜在的复杂的中枢-外周相互作用和调节途径。为了解决这些问题，越来越多的研究试图调查 OSA 的缺氧负担 ( 159 – 161 )。随时间变化的静态和动态去饱和度可以更深入地了解缺氧的严重程度。急性和慢性缺氧可能导致不同的自主调节机制。缺氧激活的化学反射导致睡眠呼吸暂停发生期间短期交感神经张力急剧增加（ 54 ）。此外，缺氧在白天会产生长期的慢性影响，并损害压力反射敏感性（ 162 ）。同时，交感神经亢进是否会引起副交感神经抑制仍存在争议。慢性缺氧导致 HRV 整体降低，交感神经张力增加，而 HRV 升高，迷走神经退缩减少，是由于随后的适应和对短期反复缺氧应激的耐受性提高而发生的（ 163 ）。乔瓦尼尼等人。证明缺氧引起的炎症和心脏自主神经异常之间存在恶性循环，伴有交感神经张力升高或副交感神经张力降低。他们还发现 SDNN、LF 和 HF 的值与 OSA 严重程度密切相关，而只有平均心率与中性粒细胞的增加显着相关 ( 164 )。在一项 OSA 儿童研究中，Walter 等人。 ( 165 )发现OSA可能对脑血流产生负面影响，因为HRV介导的中枢自主控制减弱。 因此，有理由相信，不同的心脏自主调节反应的发生是由于迷走神经调节减少、交感神经优势或什至这些反应的组合所致。

The possibility of an increasing risk in the mortality and morbidity in hypoxic OSA patients with autonomic dysfunction requires further evidence. In addition, both hypoxia and arousal have confounding effects on respiratory-cardiac coupling. Which one is the determinant of cardiac autonomic dysfunction in OSA is controversial in animal and human studies (5). It seems that in prospective animal studies, OSA-induced hypoxia has a persistent impact on daytime hypertension compared to acoustic arousal-induced control models, which exerted nocturnal elevations in blood pressure. However, in humans, the answer to that question is uncertain. Norman et al. suggested that CPAP therapy, which reduced both the intermittent hypoxia and arousals, plays a more important role in improving cardiovascular autonomic function than elimination of nighttime intermittent hypoxia by comparing the results of 24-h ambulatory blood pressure in moderate-to-severe OSA patients who received either CPAP therapy or sham-CPAP with supplemental oxygen (166).
伴有自主神经功能障碍的缺氧 OSA 患者死亡率和发病率风险增加的可能性需要进一步的证据。此外，缺氧和觉醒都会对呼吸-心脏耦合产生混杂影响。哪一项是 OSA 心脏自主神经功能障碍的决定因素在动物和人类研究中存在争议 ( 5 )。在前瞻性动物研究中，与声音唤醒引起的控制模型相比，OSA 引起的缺氧似乎对白天高血压有持续的影响，而声学唤醒引起的控制模型会导致夜间血压升高。然而，对于人类来说，这个问题的答案是不确定的。诺曼等人。通过比较中重度 OSA 患者的 24 小时动态血压结果，表明减少间歇性缺氧和觉醒的 CPAP 治疗在改善心血管自主功能方面比消除夜间间歇性缺氧更重要。接受 CPAP 治疗或假 CPAP 辅助供氧 ( 166 )。

Some studies indicated that certain damages of autonomic function are reversible after eliminating physiological influences (e.g., arousal, hypoxia, and respiratory events) in OSA population with CPAP treatment (81, 167, 168). Thus, HRV maybe become a potential early indicator of the adverse effects of hypoxia on OSA and identifying treatment responses. To date, the effect of nocturnal hypoxia on HRV patterns is unknown and correlation studies of HRV and hypoxia in HRV are limited. Those results may contribute to monitoring the progress of chronic sustained normobaric hypoxia on the cardiovascular and autonomic systems.
一些研究表明，在接受 CPAP 治疗的 OSA 人群中，在消除生理影响（例如觉醒、缺氧和呼吸事件）后，自主神经功能的某些损害是可逆的（ 81、167、168 ）。因此，HRV 可能成为缺氧对 OSA 不利影响和识别治疗反应的潜在早期指标。迄今为止，夜间缺氧对 HRV 模式的影响尚不清楚，HRV 与缺氧的相关性研究也很有限。这些结果可能有助于监测慢性持续常压缺氧对心血管和自主系统的进展。

HRV in Pediatric OSA HRV 在儿科 OSA 中的应用

OSA affects 0.1–13% of children, particularly occurred in pre-school age (169). Pediatric OSA characterize by prolonged partial OSA, which usually occurred in REM sleep, preserved sleep architecture, uncommon OSA-related cortical arousals and recurrent hypoxia (170). Enlarged tonsils and adenoids are the leading causes of OSA in children. Unlike adult OSA manifested with excessive daytime sleepiness and cognitive dysfunction, pediatric OSA is more likely to have negative impact on the development of the central nervous system and cardiovascular system, potentially leading to neurobehavioral deficits (e.g., growth impairment, behavioral, and learning problems) (171). Overt cardiovascular disease is not common in pediatric OSA compared to adults (172), but early evidence shows that pediatric OSA is related to left ventricular hypertrophy (173, 174), abnormal blood pressure fluctuation (175, 176), and reduced systolic and diastolic function (177, 178). HRV analysis is increasingly explored in assessment for cardiovascular autonomic control, the screening and diagnosis of sleep apnea and efficacy of treatments in pediatric OSA during daytime and nighttime due to its feasibility (179, 180). Current findings suggested that altered HRV patterns during daytime and sleep are also found in childhood OSA (181). Not surprising, there are more discrepancies in the results of frequency domain analysis than in time domain analysis due to diverse subject samples and the different methodologies (182, 183). Chaicharn et al. (179) tried to quantify daytime autonomic function in non-OSA and OSA children with spectral HRV analysis, showing OSA children have significant elevated sympathetic tone but normal parasympathetic control, with less reactive response to autonomic tests compared to controls. Liao et al. (182) found autonomic imbalance with increased LF/HF during sleep among groups with different levels of AHI. Similarly, Baharav et al. (183) were able to show sympathetic augmentation with increased LF both during wake before sleep onset and during sleep. By contrast, Kwok et al. (180) demonstrated no changes in most of the important time-domain and HRV measures between non-OSA and OSA children using 1-h ECG data. Impaired baroreflex adaptation is also found in OSA as it is associated with a decrease in nighttime baroreflex gain (184). Autonomic activity may play a key role in pharyngeal compliance of childhood OSA (185). Another application of HRV in childhood OSA is to evaluate treatment response. Muzumdar et al. (186) reported that HRV improved with decreased sympathetic and increased vagal tones after adenotonsillectomy in children with OSA, while no changes showed in HRV in moderate-severe pediatric OSA with 1-year non-invasive ventilation (187). The results of long-term effect of OSA on HRV are debated. Vlahandonis et al. (188) failed to show significant differences in autonomic regulation determined by using HRV analysis among children with habitual snoring, and those with and without OSA regardless of intervention during 4-year follow-up visits. However, Walter et al. (189) found improved HRV in preschool-aged children with resolved OSA, showing decreased LF and HF, while increased HF in those with unresolved OSA during 3-year period. It is noteworthy that age, obesity, sleep stage, and AHI severity are independently correlated with HRV measurements in children (190, 191). Explanations on these results need to be cautious with those confounding factors. Whether or not HRV measures could be the reliable maker of disease severity and risk stratification in children with OSA is still unproven. The clinical implication of cardiac autonomic alternation in pediatric OSA and how it disrupts the maturation of autonomic control and affects the nervous and cardiovascular functioning need further investigation.
OSA 影响 0.1-13% 的儿童，尤其发生在学龄前儿童 ( 169 )。儿童 OSA 的特点是延长部分 OSA（通常发生在快速眼动睡眠中）、保留的睡眠结构、罕见的 OSA 相关皮质唤醒和反复缺氧 ( 170 )。扁桃体和腺样体增大是儿童 OSA 的主要原因。与成人 OSA 表现为白天过度嗜睡和认知功能障碍不同，儿童 OSA 更有可能对中枢神经系统和心血管系统的发育产生负面影响，可能导致神经行为缺陷（例如生长障碍、行为和学习问题） （ 171 ）。与成人相比，儿童 OSA 中明显的心血管疾病并不常见 ( 172 )，但早期证据表明，儿童 OSA 与左心室肥厚 ( 173 , 174 )、血压波动异常 ( 175 , 176 ) 以及收缩压和舒张压降低有关函数(177、178 ) 。由于 HRV 分析的可行性，HRV 分析在评估心血管自主控制、睡眠呼吸暂停的筛查和诊断以及儿童白天和夜间 OSA 的治疗效果方面得到了越来越多的探索 ( 179 , 180 )。目前的研究结果表明，儿童 OSA 中也发现了白天和睡眠期间 HRV 模式的改变 ( 181 )。毫不奇怪，由于不同的受试者样本和不同的方法，频域分析的结果比时域分析的结果存在更多差异（182、183 ） 。柴查恩等人。 ( 179 ) 试图通过频谱 HRV 分析来量化非 OSA 和 OSA 儿童的日间自主神经功能，结果显示 OSA 儿童的交感神经张力显着升高，但副交感神经控制正常，与对照组相比，对自主神经测试的反应性较低。廖等人。 ( 182 ) 发现不同 AHI 水平的群体在睡眠期间存在自主神经失衡，低频/高频增加。同样，巴哈拉夫等人。 （ 183 ）能够在入睡前醒来期间和睡眠期间随着 LF 的增加而表现出交感神经的增强。相比之下，郭等人。 ( 180 ) 使用 1 小时心电图数据证明非 OSA 和 OSA 儿童之间大多数重要的时域和 HRV 测量没有变化。 OSA 中也发现压力反射适应受损，因为它与夜间压力反射增益的减少有关 ( 184 )。自主活动可能在儿童 OSA 的咽部顺应性中发挥关键作用 ( 185 )。 HRV 在儿童 OSA 中的另一个应用是评估治疗反应。穆祖姆达尔等人。 ( 186 ) 报道，OSA 儿童腺样体扁桃体切除术后，HRV 随交感神经减弱和迷走神经张力增加而改善，而中重度儿科 OSA 1 年无创通气后 HRV 无变化 ( 187 )。 OSA 对 HRV 的长期影响的结果存在争议。弗拉汉多尼斯等人。 ( 188 ) 未能显示出通过使用 HRV 分析确定的习惯性打鼾儿童以及患有或不患有 OSA 的儿童的自主神经调节的显着差异，无论在 4 年随访期间是否进行干预。然而，沃尔特等人。 ( 189 ) 发现 OSA 已解决的学龄前儿童的 HRV 有所改善，表现为 LF 和 HF 降低，而 OSA 未解决的儿童在 3 年期间 HF 增加。值得注意的是，儿童的年龄、肥胖、睡眠阶段和 AHI 严重程度与 HRV 测量值独立相关 ( 190 , 191 )。对这些结果的解释需要谨慎对待这些混杂因素。 HRV 测量是否可以作为 OSA 儿童疾病严重程度和风险分层的可靠指标尚未得到证实。儿童 OSA 中心脏自主神经交替的临床意义以及它如何破坏自主神经控制的成熟并影响神经和心血管功能需要进一步研究。

Effect of Age, Ethnicity and Sex on HRV
年龄、种族和性别对 HRV 的影响

Previous studies have demonstrated age-, ethnicity-, and sex-specific differences in HRV in the healthy general population and under certain conditions. It is generally accepted there is an inverse association between age and HRV. However, it is unclear whether the effects of OSA on cardiac autonomic modulation in elderly subjects (>60 yr) are different from those in other age groups (young and mid-aged adults). Trimer et al. compared the differences in HRV among the elderly and the young population with and without OSA. They found the elderly with OSA have significantly lower LF/HF ratio only during wakefulness at night than the young with OSA but not during other sleep stages (192). Sforza et al. (140, 193) suggested age may have more devastating effect on HRV in the elderly, which possibly undermines the application of HRV in those population.
先前的研究已经证明，在健康普通人群和某些条件下，HRV 存在年龄、种族和性别差异。人们普遍认为年龄和 HRV 之间存在负相关关系。然而，尚不清楚 OSA 对老年受试者（> 60 岁）心脏自主调节的影响是否与其他年龄组（青年和中年人）不同。三聚体等人。比较患有和不患有 OSA 的老年人和年轻人的 HRV 差异。他们发现，患有 OSA 的老年人仅在夜间清醒时的 LF/HF 比值显着低于患有 OSA 的年轻人，但在其他睡眠阶段则不然 ( 192 )。斯福尔扎等人。 ( 140 , 193 )表明年龄可能对老年人的 HRV 产生更大的破坏性影响，这可能会破坏 HRV 在这些人群中的应用。

Findings on sex and ethnic differences in HRV are less consistent (194). Nonetheless, reduced HRV is related to higher cardiovascular morbidity and mortality, where decreased cardiac vagal control is considered an important contributor. Currently, a majority of studies report females are characterized by higher vagal control assessed by HF-HRV and lower sympathetic control assessed by LF-HRV (194). Furthermore, women exhibit more complex heart rate dynamics (195). Several studies found no difference between men and women in HF-HRV or that men have a higher HRV (196–200). These results contradict previous findings that women are less likely to develop progressively cardiovascular diseases compared to men (201, 202).
关于 HRV 性别和种族差异的研究结果不太一致 ( 194 )。尽管如此，心率变异性降低与较高的心血管发病率和死亡率相关，其中心脏迷走神经控制降低被认为是一个重要因素。目前，大多数研究报告称，女性的特点是通过 HF-HRV 评估的迷走神经控制较高，通过 LF-HRV 评估的交感神经控制较低（ 194 ）。此外，女性表现出更复杂的心率动态（ 195 ）。多项研究发现男性和女性的 HF-HRV 没有差异，或者男性的 HRV 较高 ( 196 – 200 )。这些结果与之前的研究结果相矛盾，即与男性相比，女性患渐进性心血管疾病的可能性较小（ 201、202 ）。

In terms of interaction associations between HRV and age, sex, and ethnicity, Liao et al. (203) found changes in autonomic function have close associations with age, ethnicity, and gender in a community-based cohort by spectral analysis of HRV. They found that the sympathetic and parasympathetic tone decrease with increasing age in a general population. White populations have a higher LF, HF, and lower HF/LF than black populations, suggesting that white populations show sympathetic predominance in cardiac regulation. Men have a higher LF, and a lower HF/LF ratio than women. Those results demonstrate white and male populations have higher sympathetic activity, which is considered as a major contributor to cardiovascular diseases (e.g., hypertension). In contrast, Sloan et al. (200) reported that there is a higher standard deviation of RR intervals in white subjects compared to black subjects, and in men compared to women with age between 33 and 47 years old. No ethnicity- and sex- special differences were found in HF-HRV. Comparatively, Choi et al. found significant ethnically related differences and age-related differences (in Caucasian Americans but not in African Americans) in short-term daytime spectral HRV. Young African Americans showed a similar HRV profile to older Caucasian Americans, leading Choi et al. (204) to suggest the presence premature autonomic nervous system aging in young African Americans. A few studies related to those correlates on HRV during sleep are available. Hall et al. suggested that ethnicity is associated with HRV during sleep. They found white women have decreased parasympathetic tone and elevated sympathetic tone during NREM stage 2 and REM sleep compared to their African American and Chinese counterparts after controlling for confounding factors such as recording length and respiratory rate (205). Huang et al. (206) have shown heart rate profiles in a larger cohort of adults without sleep apnea in order to develop heart rate phenotypes regarding sleep physiology. They implied that heart rate dipping and spectral HRV metrics could contribute to sleep phenotyping due to their significant correlations to sleep measures (e.g., sleep stage, total sleep time and sleep quality). Interpreting the clinical relevance between ethnicity, sex, and HRV should be approached with caution due to the plethora of confounding factors, such as physiological, psychological, behavioral, and sociodemographic factors. To date, there is limited data reporting on the influence of age, ethnicity, and sex on HRV in the OSA population. It is unclear which OSA phenotypes are most likely to develop cardiovascular diseases and thus, which patients are most likely to benefit from CPAP or other forms of therapy for OSA (207). Those findings in cardiac heterogeneity might lead to a better understanding of the underlying cardiovascular pathophysiology and cardiovascular risk stratification in patients with OSA. Additionally, it would facilitate the development of effective strategies for treatment decision of OSA according to cardiac phenotypic characterization in order to improve treatment efficacy and predict treatment outcomes.
在 HRV 与年龄、性别和种族之间的相互作用关联方面，Liao 等人。 ( 203 ) 通过 HRV 频谱分析发现，在基于社区的队列中，自主神经功能的变化与年龄、种族和性别密切相关。他们发现，一般人群的交感神经和副交感神经张力随着年龄的增长而减弱。白人群体比黑人群体具有更高的 LF、HF 和更低的 HF/LF，这表明白人群体在心脏调节中表现出交感神经优势。男性比女性具有更高的 LF 和更低的 HF/LF 比率。这些结果表明，白人和男性人群具有较高的交感神经活动，这被认为是心血管疾病（例如高血压）的主要原因。相比之下，斯隆等人。 ( 200 ) 报道称，年龄在 33 至 47 岁之间的白人受试者的 RR 区间标准差高于黑人受试者，男性的 RR 区间标准差高于女性。 HF-HRV 没有发现种族和性别的特殊差异。相比之下，Choi 等人。发现短期日间频谱 HRV 存在显着的种族相关差异和年龄相关差异（在白种美国人中，但在非裔美国人中没有）。 Choi 等人指出，年轻的非裔美国人表现出与年长的白人美国人相似的 HRV 特征。 （ 204 ）表明年轻的非裔美国人存在自主神经系统过早衰老的现象。有一些与睡眠期间 HRV 相关的研究。霍尔等人。表明种族与睡眠期间的 HRV 相关。 他们发现，在控制了记录长度和呼吸频率等混杂因素后，与非裔美国人和中国人相比，白人女性在 NREM 2 阶段和 REM 睡眠期间副交感神经张力降低，交感神经张力升高 ( 205 )。黄等人。 ( 206 ) 显示了更大范围的没有睡眠呼吸暂停的成年人的心率概况，以便开发有关睡眠生理学的心率表型。他们暗示，心率下降和频谱 HRV 指标可能有助于睡眠表型，因为它们与睡眠指标（例如睡眠阶段、总睡眠时间和睡眠质量）具有显着相关性。由于存在大量的混杂因素，如生理、心理、行为和社会人口因素，因此应谨慎解释种族、性别和 HRV 之间的临床相关性。迄今为止，关于年龄、种族和性别对 OSA 人群 HRV 影响的数据报道有限。目前尚不清楚哪些 OSA 表型最有可能发展为心血管疾病，因此哪些患者最有可能从 CPAP 或其他形式的 OSA 治疗中受益 ( 207 )。这些心脏异质性的发现可能有助于更好地了解 OSA 患者潜在的心血管病理生理学和心血管风险分层。此外，它将有助于根据心脏表型特征制定有效的 OSA 治疗决策策略，以提高治疗效果并预测治疗结果。

HRV and OSA Comorbidity with Psychiatry Diseases
HRV 和 OSA 与精神疾病的合并症

Psychophysiological disturbances have significant impacts on the autonomic nervous system (ANS) (3, 208–211). Depression and anxiety are considered as psychosocial risk factors for cardiovascular comorbidity (212). HRV analysis has been used to quantify autonomic dysregulation in insomnia, depression, anxiety, and schizophrenia (208, 211). Epidemiological data has shown that 39–58% of patients with insomnia and 5–63% of patients with depression had accompanying OSA diagnoses (213–215). Additionally, it was found that co-morbid OSA and insomnia patients are at a higher risk of developing psychiatric disorders such as anxiety and depression than OSA patients without insomnia (122, 216). Interestingly, OSA and insomnia are more likely to show opposing clinical symptoms related to sleepiness and alertness (215). Nevertheless, increased sympathetic activity and depressed parasympathetic activity were exhibited both in OSA and insomnia (25, 217). It is reported that untreated OSA aggravate insomnia in the disturbed sleep cluster due to hyperarousal (218). Augmentation in heart rate and sympathetic tone, which is thought to be essential to the alertness and motivation, may play a key role in the pathophysiology of insomnia (215). However, interaction mechanisms between OSA and insomnia of autonomic control evaluated by HRV measures remain unclear.
心理生理障碍对自主神经系统 (ANS) 有重大影响 ( 3 , 208 – 211 )。抑郁和焦虑被认为是心血管合并症的心理社会危险因素（ 212 ）。 HRV 分析已用于量化失眠、抑郁、焦虑和精神分裂症中的自主神经失调（ 208、211 ）。流行病学数据显示，39-58% 的失眠患者和 5-63% 的抑郁症患者伴有 OSA 诊断 ( 213 - 215 )。此外，研究发现，与没有失眠的 OSA 患者相比，患有 OSA 和失眠共病的患者患焦虑和抑郁等精神疾病的风险更高 ( 122 , 216 )。有趣的是，OSA 和失眠更有可能表现出与嗜睡和警觉性相关的相反临床症状 ( 215 )。然而，OSA 和失眠症患者均表现出交感神经活性增强和副交感神经活性减弱 ( 25 , 217 )。据报道，未经治疗的 OSA 会因过度觉醒而加重睡眠紊乱群的失眠（ 218 ）。心率和交感神经张力的增加被认为对于警觉性和动机至关重要，可能在失眠的病理生理学中发挥关键作用（ 215 ）。然而，通过 HRV 测量评估 OSA 与自主神经控制失眠之间的相互作用机制仍不清楚。

Reduced global HRV is consistently reported in depression and anxiety disorders. Specifically, depression is characterized by increased cardiac rhythmicity and reduced heart rate variability during both sleep and wakefulness (219). Moreover, changes in HRV parameters are associated with alternations in symptom severity of depression (220). Saad et al. (219) showed that a sleep heart rate profiling algorithm detecting whether individuals with sleep complaints experience depression has an identification accuracy of 79.9%. Similarly, anxiety disorders displayed significantly lower HRV (221). Recently, two reviews highlighted the wide applications of HRV in mental health and psychiatric disorders (221). Likewise in populations under 18 years old, there was evidence implied that a resting state measure of HF-HRV is associated with depressive symptoms in children and adolescents with depression (222). In combination with functional brain imaging, HRV mediated by the prefrontal cortex may provide evidence of heart-brain network response to stressors and stimuli to maintain homeostasis (9). Unfortunately, co-morbid psychiatric symptoms and disorders in OSA are often ignored or misdiagnosed. Only a paucity of studies has been reported to investigate ANS dysregulation in OSA populations concomitant with psychiatric conditions via HRV analysis.
据报道，抑郁症和焦虑症患者的整体 HRV 降低。具体来说，抑郁症的特征是睡眠和清醒期间心律性增加和心率变异性降低（ 219 ）。此外，HRV 参数的变化与抑郁症症状严重程度的变化相关（ 220 ）。萨阿德等人。 （ 219 ）表明，睡眠心率分析算法检测有睡眠抱怨的个体是否患有抑郁症，识别准确度为 79.9%。同样，焦虑症患者的 HRV 显着降低 ( 221 )。最近，两篇评论强调了 HRV 在心理健康和精神疾病中的广泛应用 ( 221 )。同样，在 18 岁以下人群中，有证据表明静息态 HF-HRV 测量值与患有抑郁症的儿童和青少年的抑郁症状相关 ( 222 )。与功能性脑成像相结合，前额叶皮层介导的 HRV 可以提供心脑网络对压力源和刺激的反应以维持体内平衡的证据 ( 9 )。不幸的是，OSA 的共病精神症状和疾病经常被忽视或误诊。据报道，只有很少的研究通过HRV 分析来调查伴有精神疾病的 OSA 人群中 ANS 失调。

Evidence of autonomic dysfunction in OSA with various psychiatric and psychological disorders deepens the understanding of their psychopathology and physiopathology associated with negative cardiovascular outcomes. Correlation studies of OSA and neuropsychiatric diseases in ANS function assessed by HRV are lacking. Furthermore, it would be challenging to diagnose and treat co-morbid psychiatry disorders and OSA. It is known that the administration of drugs for psychiatric treatment aggravates OSA as it potentially reduces upper airway muscle tone to impair airway stability, decreases ventilatory response to hypoxia, increases arousal threshold leading to prolongation of respiratory events and deteriorates oxygen saturation. It seems that HRV analysis could be highly applicable in the exploration of the cardiovascular and psychopathological implications in psychiatric disorders. Investigations in the overlapping conditions in physiological and psychological aspects in OSA patients who have worse clinical outcomes and treatment response are warranted. Quintana et al. (223) provide guidelines and recommendations to advance heart rate variability research in psychiatry. We expect more perspectives and possible application of HRV in OSA in neuropsychiatric alternations could be discussed in future studies.
伴有各种精神和心理疾病的 OSA 自主神经功能障碍的证据加深了对其与不良心血管结局相关的精神病理学和生理病理学的理解。缺乏通过 HRV 评估 ANS 功能的 OSA 和神经精神疾病的相关性研究。此外，诊断和治疗共病精神疾病和 OSA 也具有挑战性。众所周知，用于精神治疗的药物会加重 OSA，因为它可能会降低上呼吸道肌张力，从而损害气道稳定性，降低对缺氧的通气反应，增加唤醒阈值，导致呼吸事件延长，并恶化氧饱和度。 HRV 分析似乎非常适用于探索精神疾病的心血管和精神病理学影响。有必要对临床结果和治疗反应较差的 OSA 患者的生理和心理方面的重叠情况进行调查。金塔纳等人。 ( 223 ) 为推进精神病学心率变异性研究提供指南和建议。我们期望在未来的研究中可以讨论 HRV 在 OSA 中神经精神交替中的更多观点和可能的应用。

HRV and Cardiovascular Mortality and Morbidity
HRV 和心血管死亡率和发病率

Due to HRV being a marker of autonomic innervation of the heart, it has been suggested that increased sympathetic activity during sleep due to OSA may be a link to cardiovascular disease (54). Sympathetic dominance during sleep has been shown in those with ischemic heart disease (53), coronary artery disease (CAD) (55) and post-MI (224). Consequently, HRV parameters are markers for adverse CVD prognoses (49, 51, 52).
由于 HRV 是心脏自主神经支配的标志，因此有人认为 OSA 导致的睡眠期间交感神经活动增加可能与心血管疾病有关 ( 54 )。患有缺血性心脏病 ( 53 )、冠状动脉疾病 (CAD) ( 55 ) 和心肌梗死后 ( 224 ) 的人在睡眠期间表现出交感神经的优势。因此，HRV 参数是不良CVD预后的标志 ( 49,51,52 )。

Several cardiovascular disease studies have reported an increased risk of mortality in relation to altered HRV parameters. Kleiger et al. found that a 24-h SDNN of <50 ms carried a relative risk of mortality 5.3 times higher than an SDNN of over 100 ms. They suggested that increased sympathetic or decreased vagal tone may predispose to VF (51). Zemaityte et al. (53) found that increased LF and decreased HF was related to the degree of deterioration of IHD functional state in overnight HRV analysis. Post-MI there is a lack of NREM vagal activity that is more likely to lead to lethal arrhythmic events and sudden death (224). Kearney et al. (49) reported that those with chronic HF and 10% lower SDNN had a hazard ratio of 1.06. Rich et al. found that EF and decreased HRV were the best predictors of 12-month mortality post-coronary angiography without recent MI. The HRV contribution to mortality was found to be independent of other disease-related variables, and the 12-month mortality was 18 times higher in those with an HRV <50 ms (52). To further this, Mäkikallio et al. (225) found that random elderly patients with altered HRV parameters predicted a 2.5 relative risk of cardiac death and 4.1 for sudden cardiac death. Algra et al. (226) found that low SDNN was correlated with a 2.6-fold risk of sudden death, also adding that low parasympathetic activity is a risk factor for sudden death. The correlation between altered HRV parameters reflective of dysfunctional sympathovagal balance and increased mortality risk is thus well-established in CVD.
多项心血管疾病研究报告称，与 HRV 参数改变相关的死亡风险增加。克莱格等人。发现 24 小时 SDNN <50 id=392>51 ）。泽马蒂特等人。 ( 53 )在隔夜HRV分析中发现，LF增加和HF减少与IHD功能状态恶化程度相关。心肌梗死后，NREM 迷走神经活动缺乏，更有可能导致致命的心律失常事件和猝死 ( 224 )。科尔尼等人。 ( 49 ) 报道称，慢性心力衰竭且 SDNN 降低 10% 的患者的风险比为 1.06。里奇等人。发现 EF 和 HRV 降低是冠状动脉造影后无近期 MI 的 12 个月死亡率的最佳预测因子。研究发现 HRV 对死亡率的影响独立于其他疾病相关变量，HRV <50 的患者 12 个月死亡率高出 18 倍（id=396>52）。为了进一步实现这一点，Mäkikallio 等人。 ( 225 )发现HRV参数改变的随机老年患者预测心源性死亡的相对风险为2.5，心源性猝死的相对风险为4.1。阿尔格拉等人。 ( 226 )发现低SDNN与2.6倍的猝死风险相关，还补充说低副交感神经活动是猝死的危险因素。因此，反映交感迷走神经平衡功能失调的 HRV 参数改变与死亡风险增加之间的相关性在 CVD 中已得到充分证实。

Additionally, CVD and OSA have been shown to be linked (227–234). The Sleep AHEAD study found a greater prevalence of stroke at greater AHI but no association between CHD and OSA (235). However, there were very few patients with CHD and thus the concluded relationship is not a representative analysis of an OSA association with coronary heart disease (CHD). Gottlieb et al. (233) found that OSA was a predictor of incident heart failure with an adjusted hazard ratio of 1.13 per 10 unit increase in AHI. In a meta-analysis of 25,760 subjects, Wang et al. (236). found that severe OSA significantly increases CVD risk, stroke and all-cause mortality with relative risks of 1.79, 2.15, and 1.92, respectively. A positive association was found between moderate OSA and CVD but not with mild OSA. A 10 unit increase in AHI was associated with 17% greater risk of CVD. No correlation was found between CHD and OSA, but again the number of prospective studies relating CHD and OSA were limited and lacked power for definitive conclusion (236). Yaggi et al. (232) found that OSA independently increases risk of stroke and all-cause mortality with a hazard ratio of 1.97 post-adjustment. In a systematic review, Lavie (234) also concluded that sleep apnea is a mortality risk that can be reduced via CPAP, which is especially crucial in younger patients, as they carry a higher mortality risk.
此外，CVD 和 OSA 已被证明是相关的 ( 227 – 234 )。 Sleep AHEAD 研究发现，AHI 越高，中风的患病率越高，但 CHD 和 OSA 之间没有关联 ( 235 )。然而，患有 CHD 的患者很少，因此得出的关系并不是 OSA 与冠心病 (CHD) 关联的代表性分析。戈特利布等人。 ( 233 ) 发现 OSA 是心力衰竭的预测因子，AHI 每增加 10 个单位，调整后的风险比为 1.13。 Wang 等人对 25,760 名受试者进行了荟萃分析。 （ 236 ）。研究发现，严重的 OSA 显着增加 CVD 风险、中风和全因死亡率，相对风险分别为 1.79、2.15 和 1.92。中度 OSA 与 CVD 之间存在正相关关系，但与轻度 OSA 之间不存在正相关关系。 AHI 每增加 10 个单位，CVD 风险就会增加 17%。 CHD 和 OSA 之间没有发现相关性，但有关 CHD 和 OSA 的前瞻性研究数量同样有限，缺乏明确结论的力量 ( 236 )。亚吉等人。 ( 232 ) 发现 OSA 独立增加中风和全因死亡率的风险，调整后的风险比为 1.97。在一项系统综述中，Lavie ( 234 ) 还得出结论，睡眠呼吸暂停是一种死亡风险，可以通过CPAP 来降低，这对于年轻患者尤其重要，因为他们具有更高的死亡风险。

In the linking of OSA and CVD, HRV, and OSA mortality and mortality, the exact physiological pathway through which these are connected is not well-understood. In animal models, Iturriaga (237) proposed that intermittent hypoxia induces carotid body potentiation, and that current evidence indicated that this alters the sympathetic, vascular, and ventilatory response to hypoxia. Whether this is the exact mechanism and whether it increases CVD risk is not definitively known. However, repetitive oxygen desaturation episodes are associated with HRV parameters suggestive of cardiac sympathetic predominance. In a group of CAD patients, those with LVEF >50% had a higher LF:HF ratio than those with LVEF ≤ 35% during cyclic oxygen desaturation episodes but not during control episodes (55). This suggests that hypoxia worsens pre-existing cardiovascular conditions. A few results of the secondary analyses using ECG data from the Sleep Heart Health Study (SHHS) or the Wisconsin Sleep Cohort Study are reported. Bradicich et al. (238) and Wang et al. (24) demonstrated associations between HRV and characteristics of polysomnographic parameters, however, they did not attempt to use HRV as a CVD risk predictor in this part of the SHHS dataset. Sankari et al. (239) suggested beat-to-beat intervals index (RRDI) during sleep is closely correlated to new-diagnosis CVD (hazard ratio of 1.21 per 10-unit increment in RRDI) in OSA patients from the Wisconsin Sleep Cohort, but they did not utilize other linear and non-linear HRV measures to show the further relationship between CV risk and OSA.
在 OSA 与 CVD、HRV 以及 OSA 死亡率和死亡率之间的联系中，这些联系的确切生理途径尚不清楚。在动物模型中，Iturriaga ( 237 ) 提出间歇性缺氧会引起颈动脉体增强，目前的证据表明这会改变交感神经、血管和通气对缺氧的反应。这是否是确切的机制以及它是否会增加 CVD 风险尚不清楚。然而，重复性氧饱和度降低事件与提示心脏交感神经优势的 HRV 参数相关。在一组 CAD 患者中，在循环氧饱和度下降期间，LVEF > 50% 的患者的 LF:HF 比值高于 LVEF ≤ 35% 的患者，但在对照发作期间则不然 ( 55 )。这表明缺氧会使已有的心血管疾病恶化。报告了使用睡眠心脏健康研究 (SHHS) 或威斯康星州睡眠队列研究的心电图数据进行二次分析的一些结果。布拉迪奇等人。 （ 238 ）和王等人。 ( 24 ) 证明了 HRV 与多导睡眠图参数特征之间的关联，但是，他们并没有尝试在 SHHS 数据集的这一部分中使用 HRV 作为 CVD 风险预测因子。桑卡里等人。 ( 239 )表明，在威斯康星州睡眠队列中的 OSA 患者中，睡眠期间的心跳间隔指数 (RRDI) 与新诊断的 CVD 密切相关（RRDI 每 10 单位增量的风险比为 1.21），但他们并没有这样做。利用其他线性和非线性 HRV 测量来显示 CV 风险和 OSA 之间的进一步关系。

However, despite the clear association between OSA and mortality and CVD, more studies need to be done to determine the exact physiological mechanisms by which this occurs, and if OSA is an independent causal factor of increased mortality and CVD risk as suggested. From the current data, altered HRV features such as SDNN are good predictors of cardiovascular mortality. There appears to be a correlation of higher mortality risk and lower SDNN, but the cut-off point varies depending on the populations and the length of ECG segments. Therefore, determination of a clear cut-off value of SDNN requires further investigation (240).
然而，尽管 OSA 与死亡率和 CVD 之间存在明确的关联，但仍需要进行更多研究来确定发生这种情况的确切生理机制，以及 OSA 是否是死亡率和 CVD 风险增加的独立原因。从目前的数据来看，改变的 HRV 特征（例如 SDNN）是心血管死亡率的良好预测因子。较高的死亡风险和较低的 SDNN 似乎存在相关性，但截止点根据人群和心电图片段的长度而变化。因此，确定 SDNN 的明确截止值需要进一步研究（ 240 ）。

Conclusion 结论

With more sophisticated analytical approaches and techniques developing, HRV measures could provide additional electrophysiological information on impaired cardiovascular alternation, which might be related to subclinical cardiovascular outcomes in patients with OSA. It is already known that the determination of time window (ECG segment length and SDB-related events) is critical to HRV analysis, but a standardized analytical approach is lacking. HRV is proving to be accurate in sleep staging and particularly screening and diagnosing OSA. However, a combinatorial method of HRV and EDR provides hidden information on cardiopulmonary coupling, which transfers from heart rate to respiration and improves the accuracy of sleep apnea detection compared to either method alone. The cognitive consequences and the daytime outcomes of ANS alternation during sleep in patients with OSA are unclear. The use of HRV in the prognosis of OSA independent of CVD is also unclear. However, HRV has shown a close association to mortality and co-morbidities. Additionally, overlapping conditions increase progressively in OSA, requiring reliable tools to manage those conditions at an early stage. Further studies are required to explore the implications of integrated cardiac physiology in regulatory networks between the central brain and heart. In particular, following this investigation, several research topics have been found to be of value:
随着更复杂的分析方法和技术的发展，HRV 测量可以提供有关心血管交替受损的额外电生理信息，这可能与 OSA 患者的亚临床心血管结局有关。众所周知，时间窗口（心电图段长度和 SDB 相关事件）的确定对于 HRV 分析至关重要，但缺乏标准化的分析方法。事实证明，HRV 在睡眠分期、特别是筛查和诊断 OSA 方面是准确的。然而，HRV 和 EDR 的组合方法提供了心肺耦合的隐藏信息，该信息从心率转移到呼吸，与单独的任何一种方法相比，提高了睡眠呼吸暂停检测的准确性。 OSA 患者睡眠期间 ANS 交替的认知后果和日间结果尚不清楚。 HRV 在独立于 CVD 的 OSA 预后中的应用尚不清楚。然而，心率变异性与死亡率和合并症密切相关。此外，OSA 中的重叠情况逐渐增加，需要可靠的工具在早期阶段管理这些情况。需要进一步的研究来探索综合心脏生理学对中枢大脑和心脏之间的调节网络的影响。特别是，经过这次调查，发现了几个有价值的研究主题：

• Prospective studies using HRV to accurately predict cardiovascular outcomes in OSA should be as a priority for clinical application of HRV research
  使用 HRV 准确预测 OSA 心血管结局的前瞻性研究应作为 HRV 研究临床应用的优先事项
• Studies investigating cardiac OSA phenotypes on the basis of HRV profiles to facilitate the definition of OSA subtypes and implement tailored treatment approaches in clinical practice
  根据 HRV 图谱调查心脏 OSA 表型的研究，以促进 OSA 亚型的定义并在临床实践中实施量身定制的治疗方法
• New sophisticated methods of HRV analysis to analyze the inevitable instationarities of OSA's transitional nature that prove challenging for current algorithms and models
  新的复杂 HRV 分析方法可分析 OSA 过渡性质不可避免的不稳定性，这对当前算法和模型来说具有挑战性
• Context-dependent analyses of HRV (i.e., age, BMI, gender, sleep stages) to better understand the association between anthropometric and sleep characteristics and autonomic function in OSA
  对 HRV（即年龄、BMI、性别、睡眠阶段）进行上下文相关分析，以更好地了解 OSA 中的人体测量和睡眠特征与自主神经功能之间的关联
• Investigation and standardization of the time window segments analyzed to provide comparable and valuable ECG data in OSA during an overnight sleep study
  对分析的时间窗口段进行调查和标准化，以在夜间睡眠研究期间提供 OSA 中可比较且有价值的心电图数据

HRV is showing promise in clinical application and due to the already large and increasing prevalence of OSA, these further studies are imperative to the advancement of diagnostic and treatment approaches needed to minimize the existing and future health and financial burden.
HRV 在临床应用中显示出前景，并且由于 OSA 的患病率已经很大且不断增加，这些进一步的研究对于推进诊断和治疗方法至关重要，以最大限度地减少现有和未来的健康和经济负担。

Author Contributions 作者贡献

HQ and TP were responsible for the manuscript concept and design. HQ, NS, and TP prepared the manuscript draft. JFK prepared the figures. HQ, NS, TP, MG, NW, JFK, and FV-V contributed to critical revision of the manuscript. All authors contributed to the article and approved the submitted version.
HQ 和 TP 负责稿件概念和设计。 HQ、NS 和 TP 准备了手稿。肯尼迪准备了这些数据。 HQ、NS、TP、MG、NW、JFK 和 FV-V 对手稿进行了重要修订。所有作者都对本文做出了贡献并批准了提交的版本。

Conflict of Interest 利益冲突

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
作者声明，该研究是在不存在任何可能被视为潜在利益冲突的商业或财务关系的情况下进行的。

Acknowledgments 致谢

Footnotes 脚注

References 参考