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Detection Method

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How WatchPAT Detects Apnea, Hypopnea, & RERA Events

WatchPAT utilizes Peripheral Arterial Tone (PAT), a special physiological signal that mirrors changes in the autonomic nervous system (ANS) caused by respiratory disturbances during sleep. WatchPAT’s automatic algorithm analyzes the PAT signal amplitude along with the heart rate and oxygen saturation to identify breathing problems while you sleep. Using specific signal patterns, the algorithm provides two indices that allow a diagnosis of sleep apnea:

  • AHI (Apnea/Hypopnea Index), which is an index used to calculate sleep apnea severity based on the total number of complete cessations (apneas) and partial obstructions (hypopneas) of breathing per hour of sleep.
  • RDI (Respiratory Disturbance Index) is used to assess severity of sleep apnea by measuring respiratory efforts, or RERAs (Respiratory Effort Related Arousals). A RERA is an arousal from sleep that follows 10 seconds or more of increased respiratory effort but does not meet the criteria for apnea or hypopnea.
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How WatchPAT Detects REM

Rapid eye movement (REM) sleep, one of the two basic states of sleep, is notable for the presence of rapid eye movement, brain activity, dreaming, and the absence of motor function. REM sleep is associated with considerable attenuation of the PAT signal and physiology coupled with specific variations in the PAT amplitude and rate. Based on this specific variability in the PAT and pulse rate signals, WatchPAT easily differentiates REM from NREM sleep.

How WatchPAT Detects Sleep Architecture

The cyclical pattern of NREM and REM sleep is detected by WatchPAT and recorded on its built-in actigraph. The propriety software’s automatic actigraph algorithm discriminates between sleep and wake states in normal subjects and patients with sleep apnea. This algorithm makes WatchPAT superior to any other actigraph devices because most are unable to detect sleep architecture in patients with sleep apnea. WatchPAT’s zzzPAT algorithm is based on 14 features extracted from two time series of PAT amplitudes and inter-pulse periods (IPP). Those features are then further processed to yield a prediction function that determines the likelihood of detecting a deep sleep epoch stage during Non-REM sleep periods.