Flexible ECG signal modeling and compression using alpha stable functions

In this paper, we propose a flexible modeling technique for the ECG signals. Modeling is achieved by considering the weighted summation of elementary functions representing the waveforms that describe each component of the cardiac cycle. We thus evaluate the benefit brought by α-stable functions with respect to Gaussian functions in terms of modeling precision. Seven records from the MIT-BIH arrhythmia database have been used to assess the performances of the proposed modeling method, including Normal beat, Premature Ventricular Contraction beat, Right Bundle Block Branch beat and Paced beat. When applied on the chosen records, it turns out that α-stable modeling always outperforms Gaussian modeling. Since each waveform is related to a particular physiological event in the ECG cardiac cycle, we also exploit flexibility of choosing α-stable modeling instead of Gaussian one for some event of medical interest in order to solve compression purpose efficiency-quality compromise. The comparison of the α-stable model applied in compression with other techniques proves the efficiency of the proposed method, mainly in term of quality score.