A novel 2-D convolutional neural network using transformed 2-D grayscale ECG images achieved 97.42% accuracy in classifying five different arrhythmia types without preprocessing or feature extraction.
Arrhythmia is irregular changes of normal heart rhythm and effective manual identifying of them require a lot of time and depends on experience of clinicians. This paper proposes deep learning-based novel 2-D convolutional neural network (CNN) approach for accurate classification of five different arrhythmia types. The performance of the proposed architecture is tested on Electrocardiogram (ECG) signals that are taken from MIT-BIH arrhythmia benchmark database. ECG signals was segmented into heartbeats and each of the heartbeats was converted into 2-D grayscale images as an input data for CNN structure. The accuracy of the proposed architecture was found as 97.42% on the training results revealed that the proposed 2-D CNN architecture with transformed 2-D ECG images can achieve highest accuracy without any preprocessing and feature extraction and feature selection stages for ECG signals.
İzci et al. (Tue,) conducted a other in Cardiac Arrhythmia. 2-D convolutional neural network (CNN) using 2-D grayscale ECG images was evaluated on Classification accuracy of five different arrhythmia types. A novel 2-D convolutional neural network using transformed 2-D grayscale ECG images achieved 97.42% accuracy in classifying five different arrhythmia types without preprocessing or feature extraction.