PAT® (Peripheral Arterial Tone) Technology A New Signal for Health Being

The Peripheral Arterial Tone (PAT) technology is a non-invasive window into the cardiovascular system and autonomic nervous system caused by respiratory disturbances during sleep. The PAT® signal is a proprietary technology used for measuring arterial tone changes in peripheral arterial beds. It is automatically analyzed utilizing a clinically validated algorithm along with heart rate, and oxygen saturation to identify respiratory events. Using specific signal patterns, the algorithm provides two indices used in determining the degree of sleep apnea – Apnea-Hypopnea Index (AHI) and Respiratory Disturbance Index (RDI). The PAT® Signal is measured from the fingertip by recording finger arterial pulsatile volume changes.

PAT infographic

Used by over 1 million patients worldwide
The finger has a unique physiology:
  • High vascular density
  • Convenient site for measurement
  • Tremendous blood flow variability (1-100ccm/100g/sec)
  • Reflection of sympathetic nervous system activation, such that terminates any apnea episode
The termination of sleep disordered breathing events is associated with an increase in heart rate, blood pressure, and sympathetic activation. This increase in sympathetic activation results in peripheral vasoconstriction. Peripheral Arterial Tonometry (PAT) measures arterial pulse volume changes in the finger as a result of vasomotion (vasoconstriction and vasodilatation).

PAT is clinically validated to have up to 90% correlation to a PSG test

Polysomnography (PSG) is considered the “gold standard” for sleep assessment of Obstructive Sleep Apnea (OSA). Numerous validations studies demonstrate a high degree of correlation in RDI and AHI during simultaneous recording of WatchPAT and PSG with R= 0.85-0.96. Moreover, the RDI and AHI scores are highly reproducible, showing correlation between at-home and in-laboratory sleep studies (PSG). The correlation is for both results of sleep apnea severity [RDI (Respiratory Disturbance Index) and AHI (Apnea-Hypopnea Index)], as well as for the recommended CPAP treatment pressure rates based on the results between same-setting WatchPAT and PSG sleep studies. The WatchPAT test is done in the comfort of the patient’s own home. Home sleep tests are also a preferred solution for disabled patients or patients who fear or are unable to take the test at sleep labs from various reasons. The WatchPAT has an excellent reliability with minimal failure rate (< 1%) during data acquisition or data analysis, and minimal technician time when compared with PSG.


  • How does WatchPAT detect Apnea, Hypopnea, and Respiratory Effort Related Arousal (RERA) events?

    WatchPATM utilizes Peripheral Arterial Tone (PAT), a special physiological signal that mirrors changes in the autonomic nervous system (ANS) caused by respiratory disturbances during sleep. The ANS regulates many of our basic functions and it does this without our conscious control. Among its activities are the regulation of blood vessel size and blood pressure, airflow in the lungs, and the heart’s electrical activity and ability to contract. WatchPAT’s automatic algorithm analyzes the PAT signal amplitude along with the heart rate and oxygen saturation to identify and classify 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. The snoring sensor enables the clinician to determine if the respiratory events are obstructive and the body position sensor enables the clinician to determine if there is a positional component to the sleep apnea

  • How does WatchPAT detect rapid eye movement (REM) from the finger?

    Rapid eye movement (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, REM sleep stage is differentiated from non-REM sleep. In addition, it is differentiated from the wake state by WatchPAT’s advanced actigraphy algorithms. This is clinically important to prevent over-treatment of apnea if predominantly REM-related.

  • How does WatchPAT detect sleep architecture?

    WatchPAT’s sleep / wake detection is based on data recorded by the built-in actigraph. The propriety software's automatic actigraph algorithm discriminates between sleep and wake states in normal subjects and those with Obstructive Sleep Apnea (OSA). This algorithm makes WatchPAT superior to all other actigraph devices—most of them fail when used with OSA subjects. WatchPAT's sleep / wake algorithm has been validated and published in peer-reviewed journals. The results show very good agreement between actigraphy and PSG in determining sleep efficiency, total sleep time, and sleep latency (agreement 86% in normal subjects, 86%-mild OSA, 84%-moderate OSA, 80% severe OSA).

  • How does WatchPAT detect mixed apneas and central apneas?

    WatchPAT detects and does not miss any type of apnea – central, mixed and obstructive. However, it does not differentiate between them. Information for differentiating between pure central (which is rare in the general population) and obstructive sleep apnea can be resolved indirectly from observing the concurrent snoring recorded by the WatchPAT – in central apnea there is no snoring.

  • How does WatchPAT differentiate between respiratory arousals and other events like Periodic Limb Movement (PLM) related arousals and spontaneous arousals?

    The algorithms differentiate between respiratory related arousals and Periodic Limb Movement (PLM) related or spontaneous arousals via detection of specific patterns in the PAT signal coupled with the presence or absence of unique dynamics of the pulse oximetry signal.