Efficient Deep Learning Model for Heart Disease Classification

Heart disease remains a leading global health challenge, emphasizing the urgent need for precise and efficient diagnostic techniques. This study proposes an advanced approach for the automated classification of heart diseases using ElectroCardioGram (ECG) signals and deep learning. Specifically, it examines the effectiveness of two modified models, namely EfficientNet B0 and EfficientNet B3, to categorize cardiac conditions into six distinct classes. To adapt these models to our specific task, we employ transfer learning by fine-tuning both architectures. The methodology consists of three main stages: signal pre-processing, feature extraction using Short-Time Fourier Transform (STFT) to generate spectrogram representations, and classification. The performance of the proposed approach was evaluated using the Massachusetts Institute of Technology Beth Israel Hospital Arrhythmia database (MIT-BIH database). Notably, EfficientNet B0 achieved the highest accuracy, reaching 99.14% while EfficientNet B3 achieve accuracy equal to 98.62%.