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Volume 22
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10.3390/s22062329
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Article

Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification

by
Bambang Tutuko
1,
Muhammad Naufal Rachmatullah
1,
Annisa Darmawahyuni
1,
Siti Nurmaini
1,*,
Alexander Edo Tondas
2,
Rossi Passarella
1,
Radiyati Umi Partan
3,
Ahmad Rifai
1,
Ade Iriani Sapitri
1 and
Firdaus Firdaus
1
1
Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia
2
Department of Cardiology & Vascular Medicine, Dr. Mohammad Hoesin Hospital, Palembang 30126, Indonesia
3
Faculty of Medicine, Universitas Sriwijaya, Palembang 30139, Indonesia
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(6), 2329; https://doi.org/10.3390/s22062329
Submission received: 28 January 2022 / Revised: 9 March 2022 / Accepted: 14 March 2022 / Published: 17 March 2022
(This article belongs to the Special Issue Advanced Machine Learning Techniques for Biomedical Imaging Sensing and Healthcare Applications)
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Abstract

Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-complex, and T-wave. Such a manual process depends on signal quality and the number of leads. ECG signal classification based on deep learning (DL) has produced an automatic interpretation; however, the proposed method is used for specific abnormality conditions. When the ECG signal morphology change to other abnormalities, it cannot proceed automatically. To generalize the automatic interpretation, we aim to delineate ECG waveform. However, the output of delineation process only ECG waveform duration classes for P-wave, QRS-complex, and T-wave. It should be combined with a medical knowledge rule to produce the abnormality interpretation. The proposed model is applied for atrial fibrillation (AF) identification. This study meets the AF criteria with RR irregularities and the absence of P-waves in essential oscillations for even more accurate identification. The QT database by Physionet is utilized for developing the delineation model, and it validates with The Lobachevsky University Database. The results show that our delineation model works properly, with 98.91% sensitivity, 99.01% precision, 99.79% specificity, 99.79% accuracy, and a 98.96% F1 score. We use about 4058 normal sinus rhythm records and 1804 AF records from the experiment to identify AF conditions that are taken from three datasets. The comprehensive testing has produced higher negative predictive value and positive predictive value. This means that the proposed model can identify AF conditions from ECG signal delineation. Our approach can considerably contribute to AF diagnosis with these results.
Keywords: delineation; electrocardiogram; convolutional neural network; long-short term memory delineation; electrocardiogram; convolutional neural network; long-short term memory

Share and Cite

MDPI and ACS Style

Tutuko, B.; Rachmatullah, M.N.; Darmawahyuni, A.; Nurmaini, S.; Tondas, A.E.; Passarella, R.; Partan, R.U.; Rifai, A.; Sapitri, A.I.; Firdaus, F. Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification. Sensors 2022, 22, 2329. https://doi.org/10.3390/s22062329

AMA Style

Tutuko B, Rachmatullah MN, Darmawahyuni A, Nurmaini S, Tondas AE, Passarella R, Partan RU, Rifai A, Sapitri AI, Firdaus F. Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification. Sensors. 2022; 22(6):2329. https://doi.org/10.3390/s22062329

Chicago/Turabian Style

Tutuko, Bambang, Muhammad Naufal Rachmatullah, Annisa Darmawahyuni, Siti Nurmaini, Alexander Edo Tondas, Rossi Passarella, Radiyati Umi Partan, Ahmad Rifai, Ade Iriani Sapitri, and Firdaus Firdaus. 2022. "Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification" Sensors 22, no. 6: 2329. https://doi.org/10.3390/s22062329

APA Style

Tutuko, B., Rachmatullah, M. N., Darmawahyuni, A., Nurmaini, S., Tondas, A. E., Passarella, R., Partan, R. U., Rifai, A., Sapitri, A. I., & Firdaus, F. (2022). Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification. Sensors, 22(6), 2329. https://doi.org/10.3390/s22062329

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