P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images
Electrocardiogram (ECG) signal analysis is a critical task in diagnosing the presence of any cardiac disorder. There are limited studies on detecting P-waves in various atrial arrhythmias, such as atrial fibrillation (AFIB), atrial flutter, junctional rhythm, and other arrhythmias due to P-wave vari...
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doaj-798eab2ee9a7444187a845d41c7966b02020-11-25T02:20:44ZengMDPI AGApplied Sciences2076-34172020-02-0110397610.3390/app10030976app10030976P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram ImagesRana N. Costandy0Safa M. Gasser1Mohamed S. El-Mahallawy2Mohamed W. Fakhr3Samir Y. Marzouk4Department of Basic and Applied Sciences, Arab Academy for Science, Technology and Maritime Transport, Cairo P.O. Box 2033, EgyptDepartment of Electronics and Communications, Arab Academy for Science, Technology and Maritime Transport, Cairo P.O. Box 2033, EgyptDepartment of Electronics and Communications, Arab Academy for Science, Technology and Maritime Transport, Cairo P.O. Box 2033, EgyptDepartment of Computer Engineering, Arab Academy for Science, Technology and Maritime Transport, Cairo P.O. Box 2033, EgyptDepartment of Basic and Applied Sciences, Arab Academy for Science, Technology and Maritime Transport, Cairo P.O. Box 2033, EgyptElectrocardiogram (ECG) signal analysis is a critical task in diagnosing the presence of any cardiac disorder. There are limited studies on detecting P-waves in various atrial arrhythmias, such as atrial fibrillation (AFIB), atrial flutter, junctional rhythm, and other arrhythmias due to P-wave variability and absence in various cases. Thus, there is a growing need to develop an efficient automated algorithm that annotates a 2D printed version of P-waves in the well-known ECG signal databases for validation purposes. To our knowledge, no one has annotated P-waves in the MIT-BIH atrial fibrillation database. Therefore, it is a challenge to manually annotate P-waves in the MIT-BIH AF database and to develop an automated algorithm to detect the absence and presence of different shapes of P-waves. In this paper, we present the manual annotation of P-waves in the well-known MIT-BIH AF database with the aid of a cardiologist. In addition, we provide an automatic P-wave segmentation for the same database using a fully convolutional neural network model (U-Net). This algorithm works on 2D imagery of printed ECG signals, as this type of imagery is the most commonly used in developing countries. The proposed automatic P-wave detection method obtained an accuracy and sensitivity of 98.56% and 98.78%, respectively, over the first 5 min of the second lead of the MIT-BIH AF database (a total of 8280 beats). Moreover, the proposed method is validated using the well-known automatically and manually annotated QT database (a total of 11,201 and 3194 automatically and manually annotated beats, respectively). This results in accuracies of 98.98 and 98.9%, and sensitivities of 98.97 and 97.24% for the automatically and manually annotated QT databases, respectively. Thus, these results indicate that the proposed automatic method can be used for analyzing long-printed ECG signals on mobile battery-driven devices using only images of the ECG signals, without the need for a cardiologist.https://www.mdpi.com/2076-3417/10/3/976electrocardiogramp-waveatrial disorderfully convolutional network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rana N. Costandy Safa M. Gasser Mohamed S. El-Mahallawy Mohamed W. Fakhr Samir Y. Marzouk |
spellingShingle |
Rana N. Costandy Safa M. Gasser Mohamed S. El-Mahallawy Mohamed W. Fakhr Samir Y. Marzouk P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images Applied Sciences electrocardiogram p-wave atrial disorder fully convolutional network |
author_facet |
Rana N. Costandy Safa M. Gasser Mohamed S. El-Mahallawy Mohamed W. Fakhr Samir Y. Marzouk |
author_sort |
Rana N. Costandy |
title |
P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images |
title_short |
P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images |
title_full |
P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images |
title_fullStr |
P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images |
title_full_unstemmed |
P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images |
title_sort |
p-wave detection using a fully convolutional neural network in electrocardiogram images |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2020-02-01 |
description |
Electrocardiogram (ECG) signal analysis is a critical task in diagnosing the presence of any cardiac disorder. There are limited studies on detecting P-waves in various atrial arrhythmias, such as atrial fibrillation (AFIB), atrial flutter, junctional rhythm, and other arrhythmias due to P-wave variability and absence in various cases. Thus, there is a growing need to develop an efficient automated algorithm that annotates a 2D printed version of P-waves in the well-known ECG signal databases for validation purposes. To our knowledge, no one has annotated P-waves in the MIT-BIH atrial fibrillation database. Therefore, it is a challenge to manually annotate P-waves in the MIT-BIH AF database and to develop an automated algorithm to detect the absence and presence of different shapes of P-waves. In this paper, we present the manual annotation of P-waves in the well-known MIT-BIH AF database with the aid of a cardiologist. In addition, we provide an automatic P-wave segmentation for the same database using a fully convolutional neural network model (U-Net). This algorithm works on 2D imagery of printed ECG signals, as this type of imagery is the most commonly used in developing countries. The proposed automatic P-wave detection method obtained an accuracy and sensitivity of 98.56% and 98.78%, respectively, over the first 5 min of the second lead of the MIT-BIH AF database (a total of 8280 beats). Moreover, the proposed method is validated using the well-known automatically and manually annotated QT database (a total of 11,201 and 3194 automatically and manually annotated beats, respectively). This results in accuracies of 98.98 and 98.9%, and sensitivities of 98.97 and 97.24% for the automatically and manually annotated QT databases, respectively. Thus, these results indicate that the proposed automatic method can be used for analyzing long-printed ECG signals on mobile battery-driven devices using only images of the ECG signals, without the need for a cardiologist. |
topic |
electrocardiogram p-wave atrial disorder fully convolutional network |
url |
https://www.mdpi.com/2076-3417/10/3/976 |
work_keys_str_mv |
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