AFibNet: an implementation of atrial fibrillation detection with convolutional neural network
Abstract Background Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG)...
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doaj-7e0dac5152bf4768861b79864d17851b2021-07-18T11:35:48ZengBMCBMC Medical Informatics and Decision Making1472-69472021-07-0121111710.1186/s12911-021-01571-1AFibNet: an implementation of atrial fibrillation detection with convolutional neural networkBambang Tutuko0Siti Nurmaini1Alexander Edo Tondas2Muhammad Naufal Rachmatullah3Annisa Darmawahyuni4Ria Esafri5Firdaus Firdaus6Ade Iriani Sapitri7Intelligent System Research Group, Faculty of Computer Science, Universitas SriwijayaIntelligent System Research Group, Faculty of Computer Science, Universitas SriwijayaDepartment of Cardiology and Vascular Medicine, Dr. Mohammad Hoesin HospitalIntelligent System Research Group, Faculty of Computer Science, Universitas SriwijayaIntelligent System Research Group, Faculty of Computer Science, Universitas SriwijayaIntelligent System Research Group, Faculty of Computer Science, Universitas SriwijayaIntelligent System Research Group, Faculty of Computer Science, Universitas SriwijayaIntelligent System Research Group, Faculty of Computer Science, Universitas SriwijayaAbstract Background Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R–R intervals to determine the heart rate variability (HRV). An accurate HRV is the gold standard for predicting the AF condition; therefore, a current challenge is to determine whether a DL approach can be used to analyze raw ECG data in a broad range of devices. This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8232 records based on three datasets and tested with 3610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. Result Meanwhile, when tested using unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity at a sample period of 0.02 seconds using the DL Cloud System. To improve the confidence of the AFibNet model, it also validated with 18 arrhythmias condition defined as Non-AF-class. Thus, the data is increased from 11,842 to 26,349 instances for three-class, i.e., Normal sinus (N), AF and Non-AF. The result found 96.36% accuracy, 93.65% sensitivity, and 96.92% specificity. Conclusion These findings demonstrate that the proposed approach can use unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deploymenthttps://doi.org/10.1186/s12911-021-01571-1Cloud deep learning1D-convolutional neural networkAtrial fibrillation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bambang Tutuko Siti Nurmaini Alexander Edo Tondas Muhammad Naufal Rachmatullah Annisa Darmawahyuni Ria Esafri Firdaus Firdaus Ade Iriani Sapitri |
spellingShingle |
Bambang Tutuko Siti Nurmaini Alexander Edo Tondas Muhammad Naufal Rachmatullah Annisa Darmawahyuni Ria Esafri Firdaus Firdaus Ade Iriani Sapitri AFibNet: an implementation of atrial fibrillation detection with convolutional neural network BMC Medical Informatics and Decision Making Cloud deep learning 1D-convolutional neural network Atrial fibrillation |
author_facet |
Bambang Tutuko Siti Nurmaini Alexander Edo Tondas Muhammad Naufal Rachmatullah Annisa Darmawahyuni Ria Esafri Firdaus Firdaus Ade Iriani Sapitri |
author_sort |
Bambang Tutuko |
title |
AFibNet: an implementation of atrial fibrillation detection with convolutional neural network |
title_short |
AFibNet: an implementation of atrial fibrillation detection with convolutional neural network |
title_full |
AFibNet: an implementation of atrial fibrillation detection with convolutional neural network |
title_fullStr |
AFibNet: an implementation of atrial fibrillation detection with convolutional neural network |
title_full_unstemmed |
AFibNet: an implementation of atrial fibrillation detection with convolutional neural network |
title_sort |
afibnet: an implementation of atrial fibrillation detection with convolutional neural network |
publisher |
BMC |
series |
BMC Medical Informatics and Decision Making |
issn |
1472-6947 |
publishDate |
2021-07-01 |
description |
Abstract Background Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R–R intervals to determine the heart rate variability (HRV). An accurate HRV is the gold standard for predicting the AF condition; therefore, a current challenge is to determine whether a DL approach can be used to analyze raw ECG data in a broad range of devices. This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8232 records based on three datasets and tested with 3610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. Result Meanwhile, when tested using unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity at a sample period of 0.02 seconds using the DL Cloud System. To improve the confidence of the AFibNet model, it also validated with 18 arrhythmias condition defined as Non-AF-class. Thus, the data is increased from 11,842 to 26,349 instances for three-class, i.e., Normal sinus (N), AF and Non-AF. The result found 96.36% accuracy, 93.65% sensitivity, and 96.92% specificity. Conclusion These findings demonstrate that the proposed approach can use unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment |
topic |
Cloud deep learning 1D-convolutional neural network Atrial fibrillation |
url |
https://doi.org/10.1186/s12911-021-01571-1 |
work_keys_str_mv |
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