Neurons to heartbeats: spiking neural networks for electrocardiogram pattern recognition

Abstract

The electrocardiogram (ECG) is one of the most significant methods of diagnostics for determining heart rhythm disorders. For this study, raw ECG signals from the Physio Bank database are subjected to an important preprocessing step that uses empirical mode decomposition (EMD) on signal denoising and distortion elimination. Establishing functioning spiking neural networks (SNN) involves figuring out the neuron’s state through its activity level, challenging due to its resemblance to the human brain’s data processing, yet appealing due to factors like improved unsupervised learning methods, with ten parameters chosen for the learning algorithm of SNN. A comprehensive set of 15 different time-domain features and 10 Cepstral domain features is precisely extracted to train the SNN classifier. An extensive study is conducted to analyse the learning parameters that affect SNN performance, significantly influencing result accuracy. Through a two-classification process, the differentiation between normal and abnormal ECG patterns can be achieved in this study. A maximum testing accuracy of 91.6667% and a maximum training accuracy of 99.1667% have been attained by the process. These results demonstrate the competency of the system in distinguishing between distinct ECG classes, particularly in identifying normal and abnormal cardiac rhythms through ECG classification.