Raw Waveform-Based Custom Scalogram CRNN in Cardiac Abnormality Diagnosis

Abstract

Cardiovascular disease is a significant cause of death worldwide, emphasizing the crucial need for timely detection and diagnosis of heart abnormalities. This study presents a new approach that utilizes deep learning models to diagnose cardiac issues by analyzing raw phonocardiogram (PCG) signals. The proposed method introduces a novel technique called custom scalogram-based convolutional recurrent neural network (CS-CRNN). Diverging from conventional techniques, this model directly handles the raw PCG signals. These signals undergo a transformation into scalogram images within the initial layer of the CRNN architecture, without incorporating any learnable parameters. The results obtained from the CS-CRNN model are compared with traditional feature-based recurrent neural network (RNN) models. The comparison demonstrates comparable performance in both binary classification (normal and abnormal categories) and multiclass classification (5 categories). The CS-CRNN model directly handles raw PCG data and employs data augmentation to enhance performance on small datasets. It achieves an accuracy of 99.6% for binary classification and 98.6% and 99.7% before and after optimization for multiclass classification on the augmented dataset. The results show that the CS-CRNN model offers comparable performance to traditional methods, making it a promising tool for diagnosing cardiac abnormalities.

Keywords

• Phonocardiogram signals
• cardiovascular disease
• recurrent neural networks
• wavelet scattering transform
• custom scalogram-based CRNN
• Bayesian optimization