8/29/2023 0 Comments Rem sleep versus deep sleepAll finger events were assigned to the epoch in which the event commenced. Each 30-second epoch was reviewed, and each valid finger event was marked. All finger movements were manually scored by the primary researcher blind to the other PSG channels, and independently of the sleep technologist who scored the sleep. A period of at least 3 s of stable waveform at reduced amplitude was needed before a new finger event was marked. All events were marked, irrespective of duration, as they were to be subsequently categorised by duration. Events were counted if they were noticeably different (at least 200% greater in amplitude) from the waveform immediately preceding it. As the present study used a piezo-electric sensor rather than EMG to detect movement, a study-specific scoring system was devised based on some of these principles. The phasic and sustained events were summed to calculate the relevant twitch densities. Rivera‐García, Ramírez‐Salado, Corsi‐Cabrera, Calvo 13 defined any waveforms exceeding baseline by 500% as facial muscle contractions, which were categorised as phasic (1–100ms) or sustained (>100ms). Stoyva 19 identified only discrete bursts of finger activity (rather than sustained background tonus) above 20μV on the EMG and scored each burst by its duration in seconds, with any fraction of a second counted as one full second, and ignored all activity associated with awakenings and within 5 s before or after gross body movements. Several methods for scoring sleep twitches have been reported. All epochs of PSG sleep data were scored blind to finger movement data using Profusion Compumedics software (version 3.0) by a trained sleep technologist according to the AASM manual for scoring sleep. Sleep and finger twitches were measured simultaneously by PSG during nine-hour sleep opportunities (23:00–08:00). PSG electrodes were connected to each participant and a finger sensor was taped to the participants’ distal index finger joint, using adhesive electrode tape. The aim of the present study was, therefore, to determine whether finger twitching measured by a more practical and less invasive finger-mounted device would be greater in REM sleep than in NREM sleep (REM-NREM effect) and be greater in late REM sleep than in early REM sleep (time-of-night effect). However, only one study of finger twitching during sleep in adult humans was identified: Stoyva 19 examined finger activity using electrodes attached to the forearms of deaf and hearing participants during sleep, and reported that activity was greater in REM sleep than in NREM sleep, and greater in later REM sleep episodes than in the first REM sleep episode. 20 To detect REM sleep, measurement of finger twitches would appear to be more practical and less invasive to sleep than the measurement of twitches in the face or chin. Similar differences in eye, throat, wrist and ankle fine muscle activity between REM sleep and NREM sleep were reported by Baldridge, Whitman, Kramer. 6, 13– 19 Studies of sleep twitches in adult humans have focused mainly on facial and chin muscles: Rivera‐García, Ramírez‐Salado, Corsi‐Cabrera, Calvo 13 reported greater facial muscle contractions during late REM sleep than early REM sleep, and De Gennaro, Ferrara, Bertini 15 reported the same temporal pattern for chin muscles. Researchers have consistently reported elevated levels of twitching in the distal muscles of both animals and humans during REM sleep, and particularly late REM sleep. Twitches are short-duration, involuntary muscle contractions, which when observed in the paws, tails, ears and whiskers of sleeping pets are often attributed to the acting out of dreams. The purpose of this study was to assess the potential for a simple finger-mounted device to measure finger twitches and thereby differentiate periods of REM and NREM sleep. In 2017, Fitbit 11 introduced the capability to differentiate light, deep and REM sleep in wrist-worn actigraphy devices, but with a per-epoch accuracy of 69% compared with PSG, 12 the search for an accurate, cost-effective ambulatory system to measure REM sleep continues. 7, 8 Several alternative techniques have been investigated to measure REM sleep, 9, 10 with limited success. 6 The gold standard for objective measurement of REM sleep is polysomnography (PSG) which is expensive and has practical limitations. 2 REM sleep is suggested to be important for memory consolidation 3 and development, 4 and abnormal REM sleep patterns are symptomatic of chronic disorders e.g., depression 5 and narcolepsy. Since the discovery of rapid eye movements (REMs), 1 sleep has been dichotomised as non-REM (NREM) sleep, which shows the restfulness normally associated with sleep, or REM sleep, characterised by REMs, skeletal muscle inhibition and cortical activation similar to wakefulness.
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