[1] Sujadevi VG, Soman KP, Vinayakumar R. Real-time detection of atrial fibrillation from short time single lead ECG traces using recurrent neural networks. Int. Symp. Intell. Syst. Technol. Appl., 2017, p. 212–21.
[2] Wang J. A deep learning approach for atrial fibrillation signals classification based on convolutional and modified Elman neural network. Futur Gener Comput Syst 2020;102:670–9.
[3] Hagiwara Y, Fujita H, Oh SL, Tan JH, San Tan R, Ciaccio EJ, et al. Computer-aided diagnosis of atrial fibrillation based on ECG signals: a review. Inf Sci (Ny) 2018;467:99–114.
[4] Hammad M, Zhang S, Wang K. A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication. Futur Gener Comput Syst 2019;101:180–96. https://doi.org/10.1016/j.future.2019.06.008.
[5] Sinha SK, Noh Y, Reljin N, Treich GM, Hajeb-Mohammadalipour S, Guo Y, et al. Screen-printed PEDOT: PSS electrodes on commercial finished textiles for electrocardiography. ACS Appl Mater Interfaces 2017;9:37524–8.
[6] Petmezas G, Haris K, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, et al. Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets. Biomed Signal Process Control 2021;63:102194.
[7] Erdenebayar U, Kim H, Park J-U, Kang D, Lee K-J. Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-term Normal Electrocardiogram Signal. J Korean Med Sci 2019;34.
[8] Xu X, Wei S, Ma C, Luo K, Zhang L, Liu C. Atrial fibrillation beat identification using the combination of modified frequency slice wavelet transform and convolutional neural networks. J Healthc Eng 2018;2018:1–8.
[9] Faust O, Shenfield A, Kareem M, San TR, Fujita H, Acharya UR. Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Comput Biol Med 2018;102:327–35.
[10] Messner E, Zöhrer M, Pernkopf F. Heart sound segmentation—an event detection approach using deep recurrent neural networks. IEEE Trans Biomed Eng 2018;65:1964–74.
[11] Herna A, Paul T, Abrams D, Aziz PF, Blom NA, Chen J, et al. Arrhythmias in congenital heart disease: a position paper of the European Heart Rhythm Association (EHRA), Association for European Paediatric and Congenital Cardiology (AEPC), and the European Society of Cardiology (ESC) Working Group on Grown-up 2018.
[12] Camm AJ, Kirchhof P, Lip GYH, Schotten U, Savelieva I, Ernst S, et al. Atrial Fibrillation and Cardiovascular Diseases – a European Heart Network paper. Eur Heart J 2010;31:2369–429.
[13] Ekinasti ATC. TA: Analisis dan Ekstraksi Ciri Sinyal Suara Jantung Menggunakan Transformasi Wavelet Diskrit. Institut Bisnis dan Informatika Stikom Surabaya, 2016.
[14] Lin H-Y, Liang S-Y, Ho Y-L, Lin Y-H, Ma H-P. Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals. Irbm 2014;35:351–61. https://doi.org/10.1016/j.irbm.2014.10.004.
[15] Bullinaria JA. Recurrent neural networks 2015.
[16] Singh S, Pandey SK, Pawar U, Janghel RR. Classification of ECG Arrhythmia using Recurrent Neural Networks. Procedia Comput Sci 2018;132:1290–7. https://doi.org/10.1016/j.procs.2018.05.045.
[17] Antczak K. Deep Recurrent Neural Networks for ECG Signal Denoising. ArXiv Prepr ArXiv180711551 2018;abs/1807.1:1–8.
[18] Kim K. Arrhythmia classification in multi-channel ECG signals using deep neural networks. 2018.
[19] KHOIRANI R, Nurmaini S. KLASIFIKASI PENYAKIT JANTUNG ATRIAL FIBRILLATION (AF) DENGAN MENGGUNAKAN METODE RECURRENT NEURAL NETWORK (RNN) PADA KASUS MULTICLASS. Sriwijaya University, 2019.
[20] Darmawahyuni A, Nurmaini S, Caesarendra W, Bhayyu V, Rachmatullah MN, others. Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier. Algorithms 2019;12:118. https://doi.org/10.3390/a12060118.
[21] Lynn HM, Pan SB, Kim P. A deep bidirectional GRU network model for biometric electrocardiogram classification based on recurrent neural networks. IEEE Access 2019;7:145395–405.
[22] Moody G. A new method for detecting atrial fibrillation using RR intervals. Comput Cardiol 1983:227–30.
[23] Laguna P, Mark RG, Goldberg A, Moody GB. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput. Cardiol. 1997, 1997, p. 673–6.
[24] Iyengar N, Peng CK, Morin R, Goldberger AL, Lipsitz LA. Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am J Physiol Integr Comp Physiol 1996;271:R1078--R1084.
[25] Liu F, Liu C, Zhao L, Zhang X, Wu X, Xu X, et al. An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection. J Med Imaging Heal Informatics 2018;8:1368–73.
[2] Wang J. A deep learning approach for atrial fibrillation signals classification based on convolutional and modified Elman neural network. Futur Gener Comput Syst 2020;102:670–9.
[3] Hagiwara Y, Fujita H, Oh SL, Tan JH, San Tan R, Ciaccio EJ, et al. Computer-aided diagnosis of atrial fibrillation based on ECG signals: a review. Inf Sci (Ny) 2018;467:99–114.
[4] Hammad M, Zhang S, Wang K. A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication. Futur Gener Comput Syst 2019;101:180–96. https://doi.org/10.1016/j.future.2019.06.008.
[5] Sinha SK, Noh Y, Reljin N, Treich GM, Hajeb-Mohammadalipour S, Guo Y, et al. Screen-printed PEDOT: PSS electrodes on commercial finished textiles for electrocardiography. ACS Appl Mater Interfaces 2017;9:37524–8.
[6] Petmezas G, Haris K, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, et al. Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets. Biomed Signal Process Control 2021;63:102194.
[7] Erdenebayar U, Kim H, Park J-U, Kang D, Lee K-J. Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-term Normal Electrocardiogram Signal. J Korean Med Sci 2019;34.
[8] Xu X, Wei S, Ma C, Luo K, Zhang L, Liu C. Atrial fibrillation beat identification using the combination of modified frequency slice wavelet transform and convolutional neural networks. J Healthc Eng 2018;2018:1–8.
[9] Faust O, Shenfield A, Kareem M, San TR, Fujita H, Acharya UR. Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Comput Biol Med 2018;102:327–35.
[10] Messner E, Zöhrer M, Pernkopf F. Heart sound segmentation—an event detection approach using deep recurrent neural networks. IEEE Trans Biomed Eng 2018;65:1964–74.
[11] Herna A, Paul T, Abrams D, Aziz PF, Blom NA, Chen J, et al. Arrhythmias in congenital heart disease: a position paper of the European Heart Rhythm Association (EHRA), Association for European Paediatric and Congenital Cardiology (AEPC), and the European Society of Cardiology (ESC) Working Group on Grown-up 2018.
[12] Camm AJ, Kirchhof P, Lip GYH, Schotten U, Savelieva I, Ernst S, et al. Atrial Fibrillation and Cardiovascular Diseases – a European Heart Network paper. Eur Heart J 2010;31:2369–429.
[13] Ekinasti ATC. TA: Analisis dan Ekstraksi Ciri Sinyal Suara Jantung Menggunakan Transformasi Wavelet Diskrit. Institut Bisnis dan Informatika Stikom Surabaya, 2016.
[14] Lin H-Y, Liang S-Y, Ho Y-L, Lin Y-H, Ma H-P. Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals. Irbm 2014;35:351–61. https://doi.org/10.1016/j.irbm.2014.10.004.
[15] Bullinaria JA. Recurrent neural networks 2015.
[16] Singh S, Pandey SK, Pawar U, Janghel RR. Classification of ECG Arrhythmia using Recurrent Neural Networks. Procedia Comput Sci 2018;132:1290–7. https://doi.org/10.1016/j.procs.2018.05.045.
[17] Antczak K. Deep Recurrent Neural Networks for ECG Signal Denoising. ArXiv Prepr ArXiv180711551 2018;abs/1807.1:1–8.
[18] Kim K. Arrhythmia classification in multi-channel ECG signals using deep neural networks. 2018.
[19] KHOIRANI R, Nurmaini S. KLASIFIKASI PENYAKIT JANTUNG ATRIAL FIBRILLATION (AF) DENGAN MENGGUNAKAN METODE RECURRENT NEURAL NETWORK (RNN) PADA KASUS MULTICLASS. Sriwijaya University, 2019.
[20] Darmawahyuni A, Nurmaini S, Caesarendra W, Bhayyu V, Rachmatullah MN, others. Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier. Algorithms 2019;12:118. https://doi.org/10.3390/a12060118.
[21] Lynn HM, Pan SB, Kim P. A deep bidirectional GRU network model for biometric electrocardiogram classification based on recurrent neural networks. IEEE Access 2019;7:145395–405.
[22] Moody G. A new method for detecting atrial fibrillation using RR intervals. Comput Cardiol 1983:227–30.
[23] Laguna P, Mark RG, Goldberg A, Moody GB. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput. Cardiol. 1997, 1997, p. 673–6.
[24] Iyengar N, Peng CK, Morin R, Goldberger AL, Lipsitz LA. Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am J Physiol Integr Comp Physiol 1996;271:R1078--R1084.
[25] Liu F, Liu C, Zhao L, Zhang X, Wu X, Xu X, et al. An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection. J Med Imaging Heal Informatics 2018;8:1368–73.
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Affiliations
Ghina Auliya
Computer Engineering Department
Jannes Effendi
Affiliation not stated
Detection of Atrial Fibrillation Based on Long Short-Term Memory
Abstract
Atrial fibrillation is a quivering or irregular heartbeat (arrhythmia) that can lead to blood clots, stroke, heart failure, and even sudden cardiac death. This study used several public datasets of electrocardiogram (ECG) signals, including MIT-BIH Atrial Fibrillation, China Physiological Signal Challenge 2018, MIT-BIH Normal Sinus Rhythm based on QT-Database, and Fantasia Database. All datasets were divided into 3 cases with the experiment windows size 10, 5, and 2 seconds for two classes, namely Normal and Atrial Fibrillation. The recurrent neural networks method is appropriate for processing sequential data such as ECG signals, and k-fold Cross-Validation can help evaluate models effectively to achieve high performance. Overall, LSTM performance achieved accuracy, sensitivity, specificity, precision, F1-score, is 94.56% 94.67%, 94.67%, 94.43%, and 94.51%.