Multi-Classifier Deep Learning based System for ECG Classification Using Fourier Transform

The threat of heart disease has increased throughout history due to the difficulty of diagnosing it and the need for experienced doctors. Heart disease can be caused by various of factors such as age, high blood pressure, and diabetes. This paper investigates the arrythmia of Electrocardiogram (ECG) by comparing and evaluating different classifiers to predict the abnormalities of a heartbeat. We have developed a system that predicts heartbeat arrhythmia using a multi-classifier deep learning model. By using MIT-BIH Arrythmia Database, we were able to achieve an accuracy of 97.4%. Compared to other models that we have implemented; this model recorded the highest performance.