Comparative evaluation of deep learning models for cardiovascular disease diagnosis and classification

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the need for accurate and computationally efficient diagnostic tools. Electrocardiography (ECG) is widely used for the diagnosis of CVD and is a noninvasive, cost-effective method. However, manual interpretation requires substantial time and can lead to human error. In this study, we propose and systematically compare low-complexity Artificial Intelligence (AI)–driven Deep Learning (DL) models for multi-class ECG-based CVD diagnosis and classification. Using the publicly available PTB Diagnostic ECG (PTB-ECG) dataset, which comprises multiple CVD categories, we evaluate CNN, LSTM, MLP, CNN-MLP, and ConvLSTM architectures, focusing on balancing diagnostic performance and computational complexity. Discrete Wavelet Transform (DWT)–based feature extraction and data-level imbalance handling are employed to enhance efficiency and robustness. Experimental results show that the LSTM model achieves the best overall performance, with an accuracy of 99.98%, an F1 score of 98%, and a precision of 100%, while maintaining low computational complexity, measured in Real Multiplications Per Symbol (RMpS). These findings demonstrate that high diagnostic accuracy can be achieved without high computational complexity, supporting the feasibility of real-time and resource-constrained clinical deployment.