使用心肺信号估计睡眠阶段：一种新算法在广泛的睡眠呼吸障碍严重程度中的验证。
Estimating sleep stages using cardiorespiratory signals: validation of a novel algorithm across a wide range of sleep-disordered breathing severity.
机构信息
Philips Sleep and Respiratory Care, Monroeville, Pennsylvania.
Philips Sleep and Respiratory Care, Vienna, Austria.
出版信息
J Clin Sleep Med. 2021 Jul 1;17(7):1343-1354. doi: 10.5664/jcsm.9192.
STUDY OBJECTIVES
We have developed the CardioRespiratory Sleep Staging (CReSS) algorithm for estimating sleep stages using heart rate variability and respiration, allowing for estimation of sleep staging during home sleep apnea tests. Our objective was to undertake an epoch-by-epoch validation of algorithm performance against the gold standard of manual polysomnography sleep staging.
METHODS
Using 296 polysomnographs, we created a limited montage of airflow and heart rate and deployed CReSS to identify each 30-second epoch as wake, light sleep (N1 + N2), deep sleep (N3), or rapid eye movement (REM) sleep. We calculated Cohen's kappa and the percentage of accurately identified epochs. We repeated our analyses after stratification by sleep-disordered breathing (SDB) severity, and after adding thoracic respiratory effort as a backup signal for periods of invalid airflow.
RESULTS
CReSS discriminated wake/light sleep/deep sleep/REM sleep with 78% accuracy; the kappa value was 0.643 (95% confidence interval, 0.641-0.645). Discrimination of wake/sleep demonstrated a kappa value of 0.711 and accuracy of 89%, non-REM sleep/REM sleep demonstrated a kappa of 0.790 and accuracy of 94%, and light sleep/deep sleep demonstrated a kappa of 0.469 and accuracy of 87%. Kappa values did not vary by more than 0.07 across subgroups of no SDB, mild SDB, moderate SDB, and severe SDB. Accuracy increased to 80%, with a kappa value of 0.680 (95% confidence interval, 0.678-0.682), when CReSS additionally utilized the thoracic respiratory effort signal.
CONCLUSIONS
We observed substantial agreement between CReSS and the gold-standard comparator of manual sleep staging of polysomnographic signals, which was consistent across the full range of SDB severity. Future research should focus on the extent to which CReSS reduces the discrepancy between the apnea-hypopnea index and the respiratory event index, and the ability of CReSS to identify REM sleep-related obstructive sleep apnea.
研究目的
我们开发了 CardioRespiratory Sleep Staging（CReSS）算法，用于使用心率变异性和呼吸来估计睡眠阶段，从而能够在家庭睡眠呼吸暂停测试期间估计睡眠阶段。我们的目标是对算法性能进行逐epoch 验证，以与手动多导睡眠图睡眠分期的金标准进行比较。
方法
使用 296 份多导睡眠图，我们创建了一个有限的心电和呼吸导联组合，并部署了 CReSS 来识别每个 30 秒的 epoch 是清醒、轻度睡眠（N1+N2）、深度睡眠（N3）还是快速眼动（REM）睡眠。我们计算了 Cohen's kappa 和准确识别 epoch 的百分比。我们按睡眠呼吸障碍（SDB）严重程度分层后，以及在无效气流期间添加胸式呼吸努力作为备用信号后，重复了我们的分析。
结果
CReSS 以 78%的准确率区分清醒/轻度睡眠/深度睡眠/REM 睡眠；kappa 值为 0.643（95%置信区间，0.641-0.645）。清醒/睡眠的区分kappa 值为 0.711，准确率为 89%，非 REM 睡眠/REM 睡眠的 kappa 值为 0.790，准确率为 94%，轻度睡眠/深度睡眠的 kappa 值为 0.469，准确率为 87%。在无 SDB、轻度 SDB、中度 SDB 和重度 SDB 亚组中，kappa 值的变化不超过 0.07。当 CReSS 另外利用胸式呼吸努力信号时，准确性增加到 80%，kappa 值为 0.680（95%置信区间，0.678-0.682）。
结论
我们观察到 CReSS 与手动多导睡眠图睡眠分期的金标准比较之间存在显著一致性，这种一致性在整个 SDB 严重程度范围内都是一致的。未来的研究应重点关注 CReSS 减少呼吸暂停-低通气指数和呼吸事件指数之间差异的程度，以及 CReSS 识别 REM 睡眠相关阻塞性睡眠呼吸暂停的能力。
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