Automatic segmentation of long-term ECG signals corrupted with broadband noise based on sample entropy

Biomedical signals are nonstationary in nature, namely, their statistical properties are time-dependent. Such changes in the underlying statistical properties of the signal and the effects of external noise often affect the performance and applicability of automatic signal processing methods that require stationarity. A number of methods have been proposed to address the problem of finding stationary signal segments within larger nonstationary signals. In this framework, processing and analysis are applied to each resulting locally stationary segment separately. The method proposed in this paper addresses the problem of finding locally quasi-stationary signal segments. Particularly, our proposed algorithm is designed to solve the specific problem of segmenting semiperiodic biomedical signals corrupted with broadband noise according to the various degrees of external noise power. It is based on the sample entropy and the relative sensitivity of this signal regularity metric to changes in the underlying signal properties and broadband noise levels. The assessment of the method was carried out by means of experiments on ECG signals drawn from the MIT-BIH arrhythmia database. The results were measured in terms of false alarms based on the changepoint detection bias. In summary, the results achieved were a sensitivity of 97%, and an error of 16% for records corrupted with muscle artifacts.