Investigating the Impact of the Stationarity Hypothesis on Heart Failure Detection using Deep Convolutional Scattering Networks and Machine Learning.

Detection of Cardiovascular Diseases (CVDs) has become crucial nowadays, as the World Health Organization (WHO) declares CVDs as the major leading causes of death in the globe. Moreover, the death rate due to CVDs is expected to rise in the next few upcoming years. One of the most valuable contributions that could be given to the cardiology field is developing a reliable model for early detection of CVDs. This paper presents a new approach aimed to classify ECG signals into: Normal Sinus Rhythm (NSR), Arrhythmia Rhythm (ARR), and Congestive Heart Failure (CHF). The proposed approach has been developed based on the stationarity hypothesis of rhythms within the same patient in ECG signals. The stationarity hypothesis assumes that if arrhythmias are found in one part of a long ECG signal, they are likely to occur in other parts of the same signal as well. In this paper, many contributions have been developed with the aim of enhancing automated detection of CVDs under the inter-patient paradigm, including using WSN in conjunction with different Machine Learning (ML) models and the stationarity hypothesis of ECG signals. A deep convolution Wavelet Scattering Network (WSN) in conjunction with a Linear Discriminant (LD) classifier and stationarity hypothesis was implemented with the aim of improving the classification results under inter-patient paradigm. The model achieved impressive results, with an overall accuracy of 99.61%, precision of 99.65%, sensitivity of 99.35%, specificity of 99.74%, and F1-score of 99.49%, across all the three classes.