Key result
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.
Why the study?
Manual identification of arrhythmia requires substantial time and relies on clinician experience.
Does a deep learning-based 2-D CNN approach accurately classify arrhythmia types from 2D ECG images?
Does a deep learning-based 2-D CNN approach accurately classify arrhythmia types from 2D ECG images?
A novel 2-D CNN architecture using transformed 2-D ECG images achieved 97.42% accuracy in classifying five arrhythmia types without the need for preprocessing or feature extraction.
May support automated arrhythmia classification from ECG images; leaves open prospective clinical validation before practice adoption.
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.
No takes yet. Share an insight, caveat, or question.
İzci et al. (2019) studied 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.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: