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Open AccessArticle

P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

1
Department of Basic and Applied Sciences, Arab Academy for Science, Technology and Maritime Transport, Cairo P.O. Box 2033, Egypt
2
Department of Electronics and Communications, Arab Academy for Science, Technology and Maritime Transport, Cairo P.O. Box 2033, Egypt
3
Department of Computer Engineering, Arab Academy for Science, Technology and Maritime Transport, Cairo P.O. Box 2033, Egypt
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(3), 976; https://doi.org/10.3390/app10030976
Received: 11 December 2019 / Revised: 15 January 2020 / Accepted: 16 January 2020 / Published: 3 February 2020
(This article belongs to the Special Issue Electrocardiogram (ECG) Signal and Its Applications)
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. View Full-Text
Keywords: electrocardiogram; P-wave; atrial disorder; fully convolutional network electrocardiogram; P-wave; atrial disorder; fully convolutional network
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Costandy, R.N.; Gasser, S.M.; El-Mahallawy, M.S.; Fakhr, M.W.; Marzouk, S.Y. P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images. Appl. Sci. 2020, 10, 976.

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