CNN Learning Based Approach for Cardiac Arrhythmia and Congestive Heart
An electrocardiogram (ECG) pattern classification method has been
proposed to distinguish heart conditions such as arrhythmia (ARR) and
congestive heart failure (CHF) from normal sinus rhythms (NSR) using
deep convolutional neural networks (CNNs) by converting the ECG signals
into RGB images. The results demonstrate an increase in diagnostic
accuracy from 90.63% to 94.12% using a pretrained CNN model by
utilising additional data from the second lead of the ECG.