Electrocardiogram Signal Classification Using Lightweight DNN for Mobile Devices

Abstract

Mobile users can use a mobile sensor to record ECG data, and on-device ECG classification can provide a more efficient diagnosis than standard care. Deep Neural Networks (DNNs) have excelled in artificial intelligence (AI)-based applications to extract patterns from the complex waveform to analyze the ECG waveform. Despite their superior performance, their high computational complexity severely limits their use in resource-constrained mobile phones. This study aimed to analyze various lightweight deep learning models for ECG classification on edge devices while maintaining comparable performance. Five DNNs, including SqueezeNet, ShuffleNet, MobileNet, EfficientNet, and NASNet, designed explicitly for resource-constrained mobile, are tested and compared for ECG classification. Time-series ECG signals are converted to a 2D scalogram using the continuous wavelet transform. These generated scalograms are classified into three categories: cardiac arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythms (NSR). A comprehensive comparison is presented using accuracy analysis and the required trainable parameters among these models. The experimental analysis of PhysioNet's dataset shows that ShuffleNet has the fewest learnable parameters, with a validation accuracy of 87.50%. In contrast, SqueezeNet performed better, with 93.75% validation accuracy and 724K learnable parameters.