Sleep disordered breathing detection using heart rate variability and R-peak envelope spectrogram.
We report that combining the interbeat heart rate as measured by the RR interval (RR) and R-peak envelope (RPE) derived from R-peak of ECG waveform may significantly improve the detection of sleep disordered breathing (SDB) from single lead ECG recording. The method uses textural features extracted from normalized gray-level cooccurrence matrices of the time frequency plots of HRV or RPE sequences. An optimum subset of textural features is selected for classification of the records. A multi-layer perceptron (MLP) serves as a classifier. To evaluate the performance of the proposed method, single Lead ECG recordings from 7 normal subjects and 7 obstructive sleep apnea patients were used. With 500 randomized Monte-Carlo simulations, the average training sensitivity, specificity and accuracy were 100.0%, 99.9%, and 99.9%, respectively. The mean testing sensitivity, specificity and accuracy were 99.0%, 96.7%, and 97.8%, respectively.
Al-Abed
Mohammad A
MA
Department of Bioengineering, the University of Texas at Arlington, Arlington, TX 76010, USA. mohammad@uta.edu
Manry
Michael
M
Burk
John R
JR
Lucas
Edgar A
EA
Behbehani
Khosrow
K
D000328:Adult; D000704:Analysis of Variance; D001185:Artificial Intelligence; D001698:Biomedical Engineering; D016022:Case-Control Studies; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D004562:Electrocardiography / Q000379:methods* / Q000706:statistics & numerical data; D005260:Female; D005583:Fourier Analysis; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D017286:Polysomnography / Q000379:methods* / Q000706:statistics & numerical data; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000503:physiopathology