ECG Analysis for Sleep Apnea Detection

Abstract

Summary Objectives: The objective of our study was to find out whether obstructive sleep apnea (OSA) may be detected on ECGs recorded during sleep. Methods: We have analyzed 70 eight-hour single-channel ECG recordings taken at polysomnographia. The 70 data sets were annotated for definition of regular sleep and phases with sleep apnea. From the 70 data sets, 35 have been used as a learning set. Our analysis is based on spectral components of heart rate variability. Frequency analysis was performed using Fourier and wavelet transformation with appropriate application of the Hilbert transform. Classification is based on four frequency bands: ULF band (0-0.013 Hz), VLF band (0.013-0.0375 Hz), LF band (0.0375-0.06 Hz) and the HF band (0.17-0.28 Hz). Linear discriminant functions were applied using mainly spectral components derived from the records. Classification of cases was based on three variables. Results: For the Testing Set, a sensitivity (Se) for apnea of 92.3% at a specificity (Sp) of 94.6% was achieved. For the minutes allocation on the Learning Set Se was 90.8% at Sp 92.7%. Conclusion: ECG analysis is useful for the detection of sleep apnea and may help to differentiate causes of cardiac arrhythmias.