WatchPAT™ is an innovative diagnostic Home Sleep Apnea Test (HSAT) that utilizes the peripheral arterial signal (PAT™).  It measures up to 7 channels (PAT signal, heart rate, oximetry, actigraphy, body position, snoring and chest motion) via three points of contact. Within one minute post-study, the raw data is downloaded and auto-scored identifying all types of apnea events. WatchPAT provides AHI, AHIc, RDI, and ODI based upon True Sleep Time and Sleep Staging. WatchPAT is clinically validated with an 89% correlation to PSG1.  The PAT signal was included in the 2017 AASM Clinical Practice Guidelines as technically adequate.

Key Features

True Sleep Time

True Sleep Time reduces the risk of misdiagnosis and misclassification that has been reported up to 20% with using total recording time2

< Back to All

Sleep Architecture

WatchPAT’s clinically validated Sleep Architecture provides information on sleep staging including sleep efficiency, sleep latency and REM latency 3-4

< Back to All

Central Sleep Apnea

The Central PLUS Module enables specific identification of Central Sleep Apnea (CSA) and Percent of Sleep Time with Cheyne-Stokes Respiration

< Back to All

Accurate Auto Scoring

Comprehensive report is created in less than a minute

< Back to All

Seven Channels

PAT, HR, Pulse-oximetry, Actigraphy, Body Position, Snoring, Chest Motion

< Back to All
- Itamar™ Medical Implements Broad Range of Actions in Response to COVID-19 Pandemic

- ItamarTM Medical and Clalit Research Institute Establish Research Collaboration to Explore WatchPAT Signals’ Ability to Predict Health Outcomes Using Existing Big Data and Artificial Intelligence

- ItamarTM Medical Announces Closing of Upsized Public Offering of American Depositary Shares and Full Exercise of the Underwriters’ Option to Purchase Additional ADSs

References on this page:

  1. Yalamanchali et al. JAMA Otolaryngnol Head Neck Surg, 2013, Diagnosis of Obstructive Sleep Apnea by Peripheral Arterial Tonometry (Meta-Analysis)
  2. Comparison of AHI using recording time versus sleep time Schutte – Rodin et al., J Sleep Abs supl 2014, p. A373
  3. Hedner J. et al. A Novel Adaptive Wrist Actigraphy Algorithm for Sleep-Wake Assessment in Sleep Apnea Patients. SLEEP, Vol. 27, No. 8, 2004 :1560-1566
  4. Hedner J. et al. Sleep Staging Based on Automimcal Signals: A Multi-Center Validation Study. JCSM. Journal of Sleep Medicine, Vol. 7, No. 3, 2011: 301 – 306