title: Electrocardiogram-derived respiration in screening of sleep-disordered breathing.
abstract: Methods for assessment of sleep-disordered breathing (SDB), including sleep apnea, range from a simple questionnaire to complex multichannel polysomnography. Inexpensive and efficient electrocardiogram (ECG)-based solutions could potentially fill the gap and provide a new SDB screening tool. In addition to the heart rate variability (HRV)-based SDB screening method that we reported a year ago, we have developed a novel method based on ECG-derived respiration (EDR). This method derives the respiratory waveform by (a) measuring peak-to-trough QRS amplitude in a single-channel ECG, (b) removing outlier introduced by noise and artifacts, (c) interpolating the derived values, and (d) filtering values within the respiration rates of 5 and 25 cycles per minute. Each 30 seconds of the respiratory waveform is then classified as normal, SDB, or indeterminate epoch. The previously reported HRV-based method, applied at the same time, is based on power spectrum of heart rate over a sliding 6-minute time window to classify the middle 30-second epoch. We then combined the EDR- and HRV-based techniques to optimize the classification of each epoch. The combined method further improved the accuracy of SDB screening in an independent test database with annotated SDB epochs. The development database was from PhysioNet (n = 25 polysomnograms). The test database was from Sleep Health Centers in Boston (n = 1907 polysomnogram) where the SDB epochs (n = 1,538,222 epochs) were scored using American Academy of Sleep Medicine criteria. The first test was to classify every epoch in the evaluation data set. The combined EDR and HRV method classified 78% of the epochs as either normal or SDB and 22% as indeterminate, with a total accuracy of 88% for scored epochs (not indeterminate). The second test was to evaluate the SDB status for each patient. The algorithm correctly classified 71% of patients with either moderate-to-severe SDB or mild-to-no SDB. We believe that the ECG-based methods provide an efficient and inexpensive tool for SDB screening in both home and hospital settings and make SDB screening feasible in large populations.
authors: Advanced Algorithm Research Center, Philips Healthcare, Andover, MA 01810, USA. saeed.babaeizadeh@philips.com
authors: Saeed
authors: S
authors: Babaeizadeh
authors: Sophia H
authors: SH
authors: Zhou
authors: Stephen D
authors: SD
authors: Pittman
authors: David P
authors: DP
authors: White
mesh_terms: D000328:Adult; D000368:Aged; D016208:Databases, Factual; D004562:Electrocardiography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012119:Respiration; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000503:physiopathology