Computers in Biology and Medicine
| Hi-index | 0.00 |
The time series of instantaneous heart rates (IHR) derived from overnight sleep electrocardiography (ECG) are analyzed by using wavelet decompositions. It is aimed to find a reliable and practical way for detecting the minute by minute occurrence of sleep-disordered breathing (SDB). The two sets of single-lead ECGs extracted from polysomnography recordings are obtained from PhysioNet apnea-ECG database. Wavelet decompositions are implemented to the segments of 6-minutes length IHR signals in which the 4th minute is accepted as deciding minute for SDB. Results obtained from analysis of the first set of the 35 recordings showed that variances of the 5th, 6th and 7th detail components of IHR time series can be used as distinguishing features disclosing the minute-based real time sleep apneas. Second set of the 35 ECG recordings is used for testing this result. For this aim, a nonlinear autoregressive (NARX) type artificial neural network (ANN) classifier is configured and trained, by using the feature vectors obtained from the first data set. Evaluations based on the assessment of whole length of overnight sleep ECGs showed that implementation of the NARX based classification following the feature extraction with wavelet decompositions has success level greater then %96.6 to decide on whether a subject is apneic or non-apneic. The same approach has %83.4 and %82.6 success level, respectively, referring to the minute-based deciding on the first (learning) and second (test) data set.