Arrhythmia detection using deep convolutional neural network with long duration ECG signals

• Published in: Computers in biology and medicine Vol. 102; pp. 411 - 420
• Main Authors: Yıldırım, Özal, Pławiak, Paweł, Tan, Ru-San, Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-11-2018; Elsevier Limited
• Subjects: Algorithms; Arrhythmia; Artificial intelligence; Artificial neural networks; Cardiac arrhythmia; cardiac arrhythmias; Cardiovascular disease; Cardiovascular diseases; Classification; Cloud computing; Convolutional neural networks; Coronary artery disease; Data processing; Deep learning; Design; ECG classification; Echocardiography; EKG; Electrocardiography; Electronic devices; Feature extraction; Fragments; Health care; Health risks; Heart; Heart diseases; Machine learning; Mobile computing; Morphology; Neural networks; Principal components analysis; Signal analysis; Wavelet transforms; Deep learning; Convolutional neural networks; cardiac arrhythmias; ECG classification
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2018.09.009

This article presents a new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis. Cardiovascular disease prevention is one of the most important tasks of any health care system as about 50 million people are at risk of heart disease in the world. Although automatic analysis of ECG signal is very popular, current methods are not satisfactory. The goal of our research was to design a new method based on deep learning to efficiently and quickly classify cardiac arrhythmias. Described research are based on 1000 ECG signal fragments from the MIT - BIH Arrhythmia database for one lead (MLII) from 45 persons. Approach based on the analysis of 10-s ECG signal fragments (not a single QRS complex) is applied (on average, 13 times less classifications/analysis). A complete end-to-end structure was designed instead of the hand-crafted feature extraction and selection used in traditional methods. Our main contribution is to design a new 1D-Convolutional Neural Network model (1D-CNN). The proposed method is 1) efficient, 2) fast (real-time classification) 3) non-complex and 4) simple to use (combined feature extraction and selection, and classification in one stage). Deep 1D-CNN achieved a recognition overall accuracy of 17 cardiac arrhythmia disorders (classes) at a level of 91.33% and classification time per single sample of 0.015 s. Compared to the current research, our results are one of the best results to date, and our solution can be implemented in mobile devices and cloud computing.
•New approach based on long-duration (10 s) ECG signal fragments based on one lead is proposed.•Involves 17 ECG classes (normal sinus rhythm, 15 cardiac arrhythmias, pacemaker rhythm).•1D-CNN is employed.•Obtained overal accuracy of 91.33%.•Can be used in tele-medicine especially in mobile devices and cloud computing due to its low computational complexity.