Arrhythmia detection using deep convolutional neural network with long duration ECG signals
Authors:
- Özal Yıldırım,
- Paweł Pławiak,
- Ru-San Tan,
- U. Rajendra Acharya
Abstract
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.
- Record ID
- CUT5198b94f8af749cc87b52cc108638411
- Publication categories
- ;
- Author
- Journal series
- Computers in Biology and Medicine, ISSN 0010-4825, e-ISSN 1879-0534
- Issue year
- 2018
- Vol
- 102
- Pages
- 411-420
- Other elements of collation
- rys.; tab.; wykr.; Bibliografia (na s.) - 418-420; Bibliografia (liczba pozycji) - 90; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 102
- Keywords in English
- cardiac arrhythmias, ECG classification, deep learning, convolutional neural networks
- DOI
- DOI:10.1016/j.compbiomed.2018.09.009 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/pii/S0010482518302713 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 25
- Publication indicators
- Citation count
- 732
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT5198b94f8af749cc87b52cc108638411/
- URN
urn:pkr-prod:CUT5198b94f8af749cc87b52cc108638411
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.