Cardiac Arrhythmia Detection from 2D ECG Images by Using Deep Learning Technique

• Main Authors: Izci, Elif, Ozdemir, Mehmet Akif, Degirmenci, Murside, Akan, Aydin
• Format: Conference Proceeding
• Language: English
• Subjects: Arrhythmia Detection; Convolutional Neural Networks; Deep Learning; ECG Images; Electrocardiogram
• ISSN: 2687-7783
• DOI: 10.1109/TIPTEKNO.2019.8895011

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.