A GAN-LSTM Architecture for ECG Anomaly Detection

Cardiovascular diseases (CVDs) continue to be a primary cause of global mortality, requiring prompt detection and care to avert serious consequences. Electrocardiograms (ECGs) are vital diagnostic instruments for cardiovascular disorders, yet traditional diagnostic methods encounter considerable obstacles, such as class imbalance and substantial heterogeneity in the available data. This study introduces a novel ECG classification technique utilizing GAN and LSTM. Generative Adversarial Networks (GANs) are employed to produce additional synthetic ECG samples, addressing class imbalance in the training dataset. The suggested design integrates LSTM units into the GAN generator to collect and maintain temporal signal patterns, while anomaly detection is performed using a CNN-LSTM model. Experimental findings indicate a significant enhancement in classification performance, achieving an accuracy of up to 94.74%. The findings indicate that the proposed GAN-LSTM model significantly improves ECG anomaly detection and classification, offering substantial potential for the rapid and precise identification of arrhythmias.