Reiter Andrew M, Roach Gregory D, Sargent Charli, Lack Leon
Appleton Institute for Behavioural Science, Central Queensland University, Goodwood, SA 5034, Australia.
College of Education, Psychology and Social Work, Flinders University of South Australia, Adelaide, SA 5001, Australia.
Nat Sci Sleep. 2020 Jan 23;12:49-56. doi: 10.2147/NSS.S233439. eCollection 2020.
Abnormal rapid eye movement (REM) sleep is often symptomatic of chronic disorders, however polysomnography, the gold standard method to measure REM sleep, is expensive and often impractical. Attempts to develop cost-effective ambulatory systems to measure REM sleep have had limited success. As elevated twitching is often observed during REM sleep in some distal muscles, the aim of this study was to assess the potential for a finger-mounted device to measure finger twitches, and thereby differentiate periods of REM and non-REM (NREM) sleep.
One night of sleep data was collected by polysomnography from each of 18 (3f, 15m) healthy adults aged 23.2 ± 3.3 (mean ± SD) years. Finger movement was detected using a piezo-electric limb sensor taped to the index finger of each participant. Finger twitch densities were calculated for each stage of sleep.
Finger twitch density was greater in REM than in NREM sleep ( < 0.001). Each sleep stage had a unique finger twitch density, except for REM and stage N1 sleep which were similar. Finger twitch density was greater in late REM than in early REM sleep ( = 0.005), and there was a time-state interaction: the difference between finger twitch densities in REM and NREM sleep was greater in late sleep than in early sleep ( = 0.007).
Finger twitching is more frequent in REM sleep than in NREM sleep and becomes more distinguishable as sleep progresses. Finger twitches appear to be too infrequent to make definitive 30-second epoch determinations of sleep stage. However, an algorithm informed by measures of finger twitch density has the potential to detect periods of REM sleep and provide estimates of total REM sleep time and percentage.
异常快速眼动（REM）睡眠通常是慢性疾病的症状，然而多导睡眠图作为测量REM睡眠的金标准方法，成本高昂且往往不切实际。开发具有成本效益的便携式系统来测量REM睡眠的尝试取得的成功有限。由于在某些远端肌肉的REM睡眠期间经常观察到抽搐加剧，本研究的目的是评估一种手指佩戴设备测量手指抽搐的潜力，从而区分REM睡眠和非REM（NREM）睡眠阶段。
通过多导睡眠图收集了18名（3名女性，15名男性）年龄在23.2±3.3（平均±标准差）岁的健康成年人每人一晚的睡眠数据。使用粘贴在每个参与者食指上的压电肢体传感器检测手指运动。计算每个睡眠阶段的手指抽搐密度。
REM睡眠中的手指抽搐密度高于NREM睡眠（<0.001）。除了REM睡眠和N1睡眠阶段相似外，每个睡眠阶段都有独特的手指抽搐密度。REM睡眠后期的手指抽搐密度高于早期（=0.005），并且存在时间-状态交互作用：REM睡眠和NREM睡眠中手指抽搐密度的差异在睡眠后期比早期更大（=0.007）。
REM睡眠中的手指抽搐比NREM睡眠更频繁，并且随着睡眠进展变得更易于区分。手指抽搐似乎过于不频繁，无法明确地在30秒的时间段内确定睡眠阶段。然而，一种基于手指抽搐密度测量的算法有潜力检测REM睡眠阶段，并提供总REM睡眠时间和百分比的估计值。
