Giora Pillar1, Murray Berall2, Richard B Berry3, Tamar Etzioni1, Yaakov Henkin4, Dennis Hwang5, Ibrahim Marai6,7, Faheem Shehadeh6, Prasanth Manthena8, Anil Rama9, Rebecca Spiegel10, Thomas Penzel11, Riva Tauman12. 1. Sleep Laboratory, Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel. 2. Center of Sleep and Chronobiology, University of Toronto, Toronto, ON, Canada. 3. UF Health Sleep Center, University of Florida, Gainesville, FL, USA. 4. Cardiology Department, Soroka Medical Center, Be'er Sheva, Israel. 5. Kaiser Permanente San Bernardino County Medical Center, Fontana, CA, USA. 6. Cardiology Department, Rambam Medical Center, Haifa, Israel. 7. Baruch Padeh Medical Center and the Azrieli Faculty of Medicine in the Galilee, Poriya, Israel. 8. Sleep clinic, Kaiser Permanente Los Angeles Medical Center, Los Angeles, CA, USA. 9. Sleep Clinic, Kaiser Permanente San Jose Medical Center, San Jose, CA, USA. 10. Department of Neurology and Sleep Center, Stony Brook University Hospital, Stony Brook, NY, USA. 11. Charite Universitätsmedizin Berlin, Sleep Medicine Center, Berlin, Germany. 12. Sleep Disorders Center, Tel Aviv Sourasky Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Abstract
Background: The WatchPAT (WP) device was shown to be accurate for the diagnosis of sleep apnea and is widely used worldwide as an ambulatory diagnostic tool. While it records peripheral arterial tone (PAT) and not electrocardiogram (ECG), the ability of it to detect arrhythmias is unknown and was not studied previously. Common arrhythmias such as atrial fibrillation (AF) or premature beats may be uniquely presented while recording PAT/pulse wave. Purpose: To examine the potential detection of common arrhythmias by analyzing the PAT amplitude and pulse rate/volume changes. Patients and Methods: Patients with suspected sleep disordered breathing (SDB) were recruited with preference for patients with previously diagnosed AF or congestive heart failure (CHF). They underwent simultaneous WP and PSG studies in 11 sleep centers. A novel algorithm was developed to detect arrhythmias while measuring PAT and was tested on these patients. Manual scoring of ECG channel (recorded as part of the PSG) was blinded to the automatically analyzed WP data. Results: A total of 84 patients aged 57±16 (54 males) participated in this study. Their BMI was 30±5.7Kg/m2. Of them, 41 had heart failure (49%) and 17 (20%) had AF. The sensitivity and specificity of the WP to detect AF segments (of at least 60 seconds) were 0.77 and 0.99, respectively. The correlation between the WP derived detection of premature beats (events/min) to that of the PSG one was 0.98 (p<0.001). Conclusion: The novel automatic algorithm of the WP can reasonably detect AF and premature beats. We suggest that when the algorithm raises a flag for arrhythmia, the patients should shortly undergo ECG and/or Holter ECG study.
Background: The WatchPAT (WP) device was shown to be accurate for the diagnosis of sleep apnea and is widely used worldwide as an ambulatory diagnostic tool. While it records peripheral arterial tone (PAT) and not electrocardiogram (ECG), the ability of it to detect arrhythmias is unknown and was not studied previously. Common arrhythmias such as atrial fibrillation (AF) or premature beats may be uniquely presented while recording PAT/pulse wave. Purpose: To examine the potential detection of common arrhythmias by analyzing the PAT amplitude and pulse rate/volume changes. Patients and Methods: Patients with suspected sleep disordered breathing (SDB) were recruited with preference for patients with previously diagnosed AF or congestive heart failure (CHF). They underwent simultaneous WP and PSG studies in 11 sleep centers. A novel algorithm was developed to detect arrhythmias while measuring PAT and was tested on these patients. Manual scoring of ECG channel (recorded as part of the PSG) was blinded to the automatically analyzed WP data. Results: A total of 84 patients aged 57±16 (54 males) participated in this study. Their BMI was 30±5.7Kg/m2. Of them, 41 had heart failure (49%) and 17 (20%) had AF. The sensitivity and specificity of the WP to detect AF segments (of at least 60 seconds) were 0.77 and 0.99, respectively. The correlation between the WP derived detection of premature beats (events/min) to that of the PSG one was 0.98 (p<0.001). Conclusion: The novel automatic algorithm of the WP can reasonably detect AF and premature beats. We suggest that when the algorithm raises a flag for arrhythmia, the patients should shortly undergo ECG and/or Holter ECG study.
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