ECG Signal Classification with Deep Learning for Heart Disease Identification

Electrocardiogram (ECG) signal is widely used in medical diagnosis of heart diseases. Automatic extraction of relevant and reliable information from ECG signals has not been an easy task for computerized system. This study proposes to use 12-layer 1-d CNN to classify 1 lead individual heartbeat signal into five classes of heart diseases. The proposed method was tested on MIT/BIH arrhythmia database and results were measured using positive predictive value, sensitivity and F1 score. Our proposed method obtained a positive predictive value of 0.977, sensitivity of 0.976, and F1 score of 0.976. Comparing with the results obtained by other four methods on the same database, our method was found superior on all three measures.