Deep Learning Based Atrial Fibrillation Detection Using Combination of Dimensionality Reduction Techniques and RR Interval Features
,
Abstract
Purpose: Accurate detection of Atrial Fibrillation (AF) has great significance in the field of medical science which can reduce the rate of mortality and morbidity. The present study focuses on Electrocardiography (ECG) signal classification using dimensionality reduction techniques combined with R wave to R wave interval (RR interval) features.
Materials and Methods: In the first approach, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), and Probabilistic Principal Component Analysis (PPCA) are performed independently on denoised ECG signal using Discrete Wavelet Transform (DWT) for the classification of ECG signal. In the second approach, the dimensionality reduction techniques combined with RR interval features are used for the classification of ECG signal.
Results: Machine Learning (ML) algorithms such as Decision Tree (DT), Support Vector Machine (SVM), and Deep Learning (DL) algorithms such as Long Short Term Memory (LSTM) and Bi-Directional LSTM (BiLSTM) are used for classification purposes.
Conclusion: The proposed methodology provided an overall accuracy of 93.65% with PCA and LSTM classifier and an overall accuracy of 99.45% with PCA combined with RR interval features and LSTM classifier. The developed technology has potential applications in many practical solutions.
[ 1] Virani, Salim S., Alvaro Alonso, Hugo J. Aparicio, Emelia J. Benjamin, Marcio S. Bittencourt, Clifton W. Callaway, April P. Carson et al. "Heart disease and stroke statistics—2021 update: a report from the American Heart Association." Circulation 143, no. 8 (2021): e254-e743; https://doi.org/10.1161/CIR.0000000000000950.
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[ 3] Shi, Jingjing, Chao Chen, Hui Liu, Yinglong Wang, Minglei Shu, and Qing Zhu. "Automated Atrial Fibrillation Detection Based on Feature Fusion Using Discriminant Canonical Correlation Analysis." Computational and Mathematical Methods in Medicine 2021 (2021); https://doi.org/10.1155/2021/6691177.
[ 4] Petmezas, Georgios, Kostas Haris, Leandros Stefanopoulos, Vassilis Kilintzis, Andreas Tzavelis, John A. Rogers, Aggelos K. Katsaggelos, and Nicos Maglaveras. "Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets." Biomedical Signal Processing and Control 63 (2021): 102194; https://doi.org/10.1016/j.bspc.2020.102194.
[ 5] Radhakrishnan, Tejas, Jay Karhade, S. K. Ghosh, P. R. Muduli, R. K. Tripathy, and U. Rajendra Acharya. "AFCNNet: Automated detection of AF using chirplet transform and deep convolutional bidirectional long short term memory network with ECG signals." Computers in Biology and Medicine 137 (2021): 104783; https://doi.org/10.1016/j.compbiomed.2021.104783.
[ 6] Liang, Yongbo, Shimin Yin, Qunfeng Tang, Zhenyu Zheng, Mohamed Elgendi, and Zhencheng Chen. "Deep learning algorithm classifies heartbeat events based on electrocardiogram signals." Frontiers in Physiology 11 (2020); https://doi: 10.3389/fphys.2020.569050.
[ 7] Ghosh, Samit Kumar, Rajesh K. Tripathy, Mario RA Paternina, Juan J. Arrieta, Alejandro Zamora-Mendez, and Ganesh R. Naik. "Detection of atrial fibrillation from single lead ECG signal using multirate cosine filter bank and deep neural network." Journal of medical systems 44 (2020): 1-15; https://doi:10.1007/s10916-020-01565-y.
[ 8] Ahmed, Nuzhat, and Yong Zhu. "Early detection of atrial fibrillation based on ECG signals." Bioengineering 7, no. 1 (2020): 16; https://doi.org/10.3390/bioengineering7010016
[ 9] Shankar, M. Gowri, and C. Ganesh Babu. "An exploration of ECG signal feature selection and classification using machine learning techniques." Int. J. Innov. Technol. Explor. Eng. Regul 9 (2020): 797-804; https://doi:10.35940/ijitee.C8728.019320.
[ 10] Kleyko, Denis, Evgeny Osipov, and Urban Wiklund. "A Comprehensive Study of Complexity and Performance of Automatic Detection of Atrial Fibrillation: Classification of Long ECG Recordings Based on the PhysioNet Computing in Cardiology Challenge 2017." Biomedical Physics & Engineering Express 6, no. 2 (2020): 025010; https://doi.org/10.1088/2057-1976/ab6e1e.
[ 11] Wu, Xiaodan, Yumeng Zheng, Chao-Hsien Chu, and Zhen He. "Extracting deep features from short ECG signals for early atrial fibrillation detection." Artificial Intelligence in Medicine 109 (2020): 101896; https://doi.org/10.1016/j.artmed.2020.101896.
[ 12] Wang, Jibin, Ping Wang, and Suping Wang. "Automated detection of atrial fibrillation in ECG signals based on wavelet packet transform and correlation function of random process." Biomedical Signal Processing and Control 55 (2020): 101662; https://doi.org/10.1016/j.bspc.2019.101662.
[ 13] Li, Mingchun, Gary He, and Baofeng Zhu. "Atrial Fibrillation Detection Based on the Combination of Depth and Statistical Features of ECG." In Proceedings of the 2019 3rd International Conference on Graphics and Signal Processing, pp. 105-112. 2019; https://doi.org/10.1145/3338472.3338485.
[ 14] Hagiwara, Yuki, Hamido Fujita, Shu Lih Oh, Jen Hong Tan, Ru San Tan, Edward J. Ciaccio, and U. Rajendra Acharya. "Computer-aided diagnosis of atrial fibrillation based on ECG signals: A review." Information Sciences 467 (2018): 99-114; https://doi.org/10.1016/j.ins.2018.07.063.
[ 15] Faust, Oliver, Alex Shenfield, Murtadha Kareem, Tan Ru San, Hamido Fujita, and U. Rajendra Acharya. "Automated detection of atrial fibrillation using long short-term memory network with RR interval signals." Computers in biology and medicine 102 (2018): 327-335; https://d oi.org/10.1016/j.compbiomed.2018.07.001.
[ 16] Maji, U., M. Mitra, and S. Pal. "Automatic detection of atrial fibrillation using empirical mode decomposition and statistical approach." Procedia Technology 10 (2013): 45-52;
https://doi.org/10.1016/j.protcy.2013.12.335.
[ 17] Dash, S., K. H. Chon, S. Lu, and E. A. Raeder. "Automatic real time detection of atrial fibrillation." Annals of biomedical engineering 37, no. 9 (2009): 1701-1709; https://doi:10.1007/s10439-009-9740-z.
[ 18] Clifford, Gari D., Chengyu Liu, Benjamin Moody, H. Lehman Li-wei, Ikaro Silva, Qiao Li, A. E. Johnson, and Roger G. Mark. "AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017." In 2017 Computing in Cardiology (CinC), pp. 1-4. IEEE, 2017;
https://doi.10.22489/CinC.2017.065-469.
[ 19] Martis, Roshan Joy, U. Rajendra Acharya, and Hojjat Adeli. "Current methods in electrocardiogram characterization." Computers in biology and medicine 48 (2014): 133-149;
https://doi.org/10.1016/j.compbiomed.2014.02.012.
[ 20] Rao,SK Shrikanth, Krithika, K., M. Akhila, and Roshan Joy Martis. "Deep Learning Based Atrial Fibrillation Detection Using Effective Denoising Methods and Dimensionality Reduction Techniques." In 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), pp. 01-07. IEEE; https:// doi.10.1109/R10-HTC53172.2021.9641550
[ 21] Rizwan, Muhammed, Bradley M. Whitaker, and David V. Anderson. "AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning." Physiological measurement 39, no. 12 (2018): 124007; https://doi.org/10.1088/1361-6579/aaf35b.
[ 22] Shaffer, Fred, and J. P. Ginsberg. "An overview of heart rate variability metrics and norms." Frontiers in public health 5 (2017): 258; https://doi.org/10.3389/fpubh.2017.00258.
[ 23] Mali, Barbara, Sara Zulj, Ratko Magjarevic, Damijan Miklavcic, and Tomaz Jarm. "Matlab-based tool for ECG and HRV analysis." Biomedical Signal Processing and Control 10 (2014): 108-116;
https://doi.org/10.1016/j.bspc.2014.01.011.
[ 24] Smith, Anne-Louise, Harry Owen, and Karen J. Reynolds. "Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox." Journal of clinical monitoring and computing 27, no. 5 (2013): 569-576; https://doi.org/10.1007/s10877-013-9471-4.
[ 25] Smith, Anne-Louise, Harry Owen, and Karen J. Reynolds. "Heart rate variability indices for very short-term (30 beat) analysis. Part 2: validation." Journal of clinical monitoring and computing 27, no.5 (2013): 577-585; https://doi: 10.1007/s10877-013-9473-2.
[ 26] Martis, Roshan Joy, U. Rajendra Acharya, K. M. Mandana, Ajoy Kumar Ray, and Chandan Chakraborty. "Application of principal component analysis to ECG signals for automated diagnosis of cardiac health." Expert Systems with Applications 39, no. 14 (2012): 11792-11800;
https://doi.org/10.1016/j.eswa.2012.04.072.
[ 27] Ke, Calvin, Rajeev Gupta, Denis Xavier, Dorairaj Prabhakaran, Prashant Mathur, Yogeshwar V. Kalkonde, Patrycja Kolpak et al. "Divergent trends in ischaemic heart disease and stroke mortality in India from 2000 to 2015: a nationally representative mortality study." The Lancet Global Health 6, no. 8 (2018): e914-e923; https://doi.org/10.1016/S2214-109X(18)30242-0.
[ 28] Martis, Roshan Joy, U. Rajendra Acharya, and Lim Choo Min. "ECG beat classification using PCA, LDA, ICA and discrete wavelet transform." Biomedical Signal Processing and Control 8, no. 5 (2013): 437-448; https://doi.org/10.1016/j.bspc.2013.01.005.
[ 29] Sadaghiyanfam, Safa, and Mehmet Kuntalp. "Analysing the Performance of LDA (Linear Discriminant Analysis) Feature for Diagnosing PAF (Paroxysmal Atrial Fibrillation) Patients." In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), pp. 1-4. IEEE, 2019; https://doi:10.1109/EBBT.2019.8741871.
[ 30] Plesinger, Filip, Petr Nejedly, Ivo Viscor, Josef Halamek, and Pavel Jurak. "Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG." Physiological measurement 39, no. 9 (2018): 094002; https://doi.org/10.1088/1361-6579/aad9ee.
[ 31] Shao, Minggang, Guangyu Bin, Shuicai Wu, Guanghong Bin, Jiao Huang, and Zhuhuang Zhou. "Detection of atrial fibrillation from ECG recordings using decision tree ensemble with multi-level features." Physiological measurement 39, no. 9 (2018): 094008; https://doi.org/10.1088/1361-6579/aadf48.
[ 32] Sánchez, FA Rivera, and JA González Cervera. "ECG classification using artificial neural networks." In Journal of Physics: Conference Series, vol. 1221, no. 1, p. 012062. IOP Publishing, 2019;
https://doi.org/10.1088/1742-6596/1221/1/012062.
[ 33] Shi, Jingang, Iman Alikhani, Xiaobai Li, Zitong Yu, Tapio Seppänen, and Guoying Zhao. "Atrial fibrillation detection from face videos by fusing subtle variations." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 8 (2019): 2781-2795; https://doi:10.1109/TCSVT.2019.2926632.
[ 34] Zhao, Yunxiang, Jinyong Cheng, Ping Zhang, and Xueping Peng. "ECG classification using deep CNN improved by wavelet transform." Computers, Materials & Continua 64, no. 3 (2020): 1615-1628; https://doi:10.32604/cmc.2020.09938.
[ 35] Mousavi, Sajad, Fatemeh Afghah, and U. Rajendra Acharya. "HAN-ECG: An interpretable atrial fibrillation detection model using hierarchical attention networks." Computers in Biology and Medicine 127 (2020): 104057; https://doi.org/10.1016/j.compbiomed.2020.104057.
[ 36] Alsaleem, Mona, and Md Saiful Islam. "POSTER: Atrial Fibrillation Detection Using a Double-Layer Bi-Directional LSTM Neural Networks." In 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), pp. 266-267. IEEE, 2020; https://doi:10.1109/SMART-TECH49988.2020.00071.
[ 37] Hsieh, Chaur-Heh, Yan-Shuo Li, Bor-Jiunn Hwang, and Ching-Hua Hsiao. "Detection of Atrial Fibrillation Using 1D convolutional neural network." Sensors 20, no. 7 (2020): 2136; https://doi.org/10.3390/s20072136.
[ 38] Nguyen, Quang H., Binh P. Nguyen, Trung B. Nguyen, Trang TT Do, James F. Mbinta, and Colin R. Simpson. "Stacking segment-based CNN with SVM for recognition of atrial fibrillation from single-lead ECG recordings." Biomedical Signal Processing and Control 68 (2021): 102672;
https://doi.org/10.1016/j.bspc.2021.102672.
[ 39] Fayyazifar, Najmeh. "An accurate CNN Architecture for Atrial Fibrillation Detection Using Neural Architecture Search." In 2020 28th European Signal Processing Conference (EUSIPCO), pp. 1135-1139. IEEE, 2021; https://doi.10.23919/Eusipco47968.2020.9287496.
[ 2] Rao, SK Shrikanth, MaheshKumar H. Kolekar, and Roshan Joy Martis. "Frequency Domain Features Based Atrial Fibrillation Detection Using Machine Learning And Deep Learning Approach." In 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp. 1-6. IEEE, 2021; https://doi: 10.1109/CONECCT52877.2021.9622533.
[ 3] Shi, Jingjing, Chao Chen, Hui Liu, Yinglong Wang, Minglei Shu, and Qing Zhu. "Automated Atrial Fibrillation Detection Based on Feature Fusion Using Discriminant Canonical Correlation Analysis." Computational and Mathematical Methods in Medicine 2021 (2021); https://doi.org/10.1155/2021/6691177.
[ 4] Petmezas, Georgios, Kostas Haris, Leandros Stefanopoulos, Vassilis Kilintzis, Andreas Tzavelis, John A. Rogers, Aggelos K. Katsaggelos, and Nicos Maglaveras. "Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets." Biomedical Signal Processing and Control 63 (2021): 102194; https://doi.org/10.1016/j.bspc.2020.102194.
[ 5] Radhakrishnan, Tejas, Jay Karhade, S. K. Ghosh, P. R. Muduli, R. K. Tripathy, and U. Rajendra Acharya. "AFCNNet: Automated detection of AF using chirplet transform and deep convolutional bidirectional long short term memory network with ECG signals." Computers in Biology and Medicine 137 (2021): 104783; https://doi.org/10.1016/j.compbiomed.2021.104783.
[ 6] Liang, Yongbo, Shimin Yin, Qunfeng Tang, Zhenyu Zheng, Mohamed Elgendi, and Zhencheng Chen. "Deep learning algorithm classifies heartbeat events based on electrocardiogram signals." Frontiers in Physiology 11 (2020); https://doi: 10.3389/fphys.2020.569050.
[ 7] Ghosh, Samit Kumar, Rajesh K. Tripathy, Mario RA Paternina, Juan J. Arrieta, Alejandro Zamora-Mendez, and Ganesh R. Naik. "Detection of atrial fibrillation from single lead ECG signal using multirate cosine filter bank and deep neural network." Journal of medical systems 44 (2020): 1-15; https://doi:10.1007/s10916-020-01565-y.
[ 8] Ahmed, Nuzhat, and Yong Zhu. "Early detection of atrial fibrillation based on ECG signals." Bioengineering 7, no. 1 (2020): 16; https://doi.org/10.3390/bioengineering7010016
[ 9] Shankar, M. Gowri, and C. Ganesh Babu. "An exploration of ECG signal feature selection and classification using machine learning techniques." Int. J. Innov. Technol. Explor. Eng. Regul 9 (2020): 797-804; https://doi:10.35940/ijitee.C8728.019320.
[ 10] Kleyko, Denis, Evgeny Osipov, and Urban Wiklund. "A Comprehensive Study of Complexity and Performance of Automatic Detection of Atrial Fibrillation: Classification of Long ECG Recordings Based on the PhysioNet Computing in Cardiology Challenge 2017." Biomedical Physics & Engineering Express 6, no. 2 (2020): 025010; https://doi.org/10.1088/2057-1976/ab6e1e.
[ 11] Wu, Xiaodan, Yumeng Zheng, Chao-Hsien Chu, and Zhen He. "Extracting deep features from short ECG signals for early atrial fibrillation detection." Artificial Intelligence in Medicine 109 (2020): 101896; https://doi.org/10.1016/j.artmed.2020.101896.
[ 12] Wang, Jibin, Ping Wang, and Suping Wang. "Automated detection of atrial fibrillation in ECG signals based on wavelet packet transform and correlation function of random process." Biomedical Signal Processing and Control 55 (2020): 101662; https://doi.org/10.1016/j.bspc.2019.101662.
[ 13] Li, Mingchun, Gary He, and Baofeng Zhu. "Atrial Fibrillation Detection Based on the Combination of Depth and Statistical Features of ECG." In Proceedings of the 2019 3rd International Conference on Graphics and Signal Processing, pp. 105-112. 2019; https://doi.org/10.1145/3338472.3338485.
[ 14] Hagiwara, Yuki, Hamido Fujita, Shu Lih Oh, Jen Hong Tan, Ru San Tan, Edward J. Ciaccio, and U. Rajendra Acharya. "Computer-aided diagnosis of atrial fibrillation based on ECG signals: A review." Information Sciences 467 (2018): 99-114; https://doi.org/10.1016/j.ins.2018.07.063.
[ 15] Faust, Oliver, Alex Shenfield, Murtadha Kareem, Tan Ru San, Hamido Fujita, and U. Rajendra Acharya. "Automated detection of atrial fibrillation using long short-term memory network with RR interval signals." Computers in biology and medicine 102 (2018): 327-335; https://d oi.org/10.1016/j.compbiomed.2018.07.001.
[ 16] Maji, U., M. Mitra, and S. Pal. "Automatic detection of atrial fibrillation using empirical mode decomposition and statistical approach." Procedia Technology 10 (2013): 45-52;
https://doi.org/10.1016/j.protcy.2013.12.335.
[ 17] Dash, S., K. H. Chon, S. Lu, and E. A. Raeder. "Automatic real time detection of atrial fibrillation." Annals of biomedical engineering 37, no. 9 (2009): 1701-1709; https://doi:10.1007/s10439-009-9740-z.
[ 18] Clifford, Gari D., Chengyu Liu, Benjamin Moody, H. Lehman Li-wei, Ikaro Silva, Qiao Li, A. E. Johnson, and Roger G. Mark. "AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017." In 2017 Computing in Cardiology (CinC), pp. 1-4. IEEE, 2017;
https://doi.10.22489/CinC.2017.065-469.
[ 19] Martis, Roshan Joy, U. Rajendra Acharya, and Hojjat Adeli. "Current methods in electrocardiogram characterization." Computers in biology and medicine 48 (2014): 133-149;
https://doi.org/10.1016/j.compbiomed.2014.02.012.
[ 20] Rao,SK Shrikanth, Krithika, K., M. Akhila, and Roshan Joy Martis. "Deep Learning Based Atrial Fibrillation Detection Using Effective Denoising Methods and Dimensionality Reduction Techniques." In 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), pp. 01-07. IEEE; https:// doi.10.1109/R10-HTC53172.2021.9641550
[ 21] Rizwan, Muhammed, Bradley M. Whitaker, and David V. Anderson. "AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning." Physiological measurement 39, no. 12 (2018): 124007; https://doi.org/10.1088/1361-6579/aaf35b.
[ 22] Shaffer, Fred, and J. P. Ginsberg. "An overview of heart rate variability metrics and norms." Frontiers in public health 5 (2017): 258; https://doi.org/10.3389/fpubh.2017.00258.
[ 23] Mali, Barbara, Sara Zulj, Ratko Magjarevic, Damijan Miklavcic, and Tomaz Jarm. "Matlab-based tool for ECG and HRV analysis." Biomedical Signal Processing and Control 10 (2014): 108-116;
https://doi.org/10.1016/j.bspc.2014.01.011.
[ 24] Smith, Anne-Louise, Harry Owen, and Karen J. Reynolds. "Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox." Journal of clinical monitoring and computing 27, no. 5 (2013): 569-576; https://doi.org/10.1007/s10877-013-9471-4.
[ 25] Smith, Anne-Louise, Harry Owen, and Karen J. Reynolds. "Heart rate variability indices for very short-term (30 beat) analysis. Part 2: validation." Journal of clinical monitoring and computing 27, no.5 (2013): 577-585; https://doi: 10.1007/s10877-013-9473-2.
[ 26] Martis, Roshan Joy, U. Rajendra Acharya, K. M. Mandana, Ajoy Kumar Ray, and Chandan Chakraborty. "Application of principal component analysis to ECG signals for automated diagnosis of cardiac health." Expert Systems with Applications 39, no. 14 (2012): 11792-11800;
https://doi.org/10.1016/j.eswa.2012.04.072.
[ 27] Ke, Calvin, Rajeev Gupta, Denis Xavier, Dorairaj Prabhakaran, Prashant Mathur, Yogeshwar V. Kalkonde, Patrycja Kolpak et al. "Divergent trends in ischaemic heart disease and stroke mortality in India from 2000 to 2015: a nationally representative mortality study." The Lancet Global Health 6, no. 8 (2018): e914-e923; https://doi.org/10.1016/S2214-109X(18)30242-0.
[ 28] Martis, Roshan Joy, U. Rajendra Acharya, and Lim Choo Min. "ECG beat classification using PCA, LDA, ICA and discrete wavelet transform." Biomedical Signal Processing and Control 8, no. 5 (2013): 437-448; https://doi.org/10.1016/j.bspc.2013.01.005.
[ 29] Sadaghiyanfam, Safa, and Mehmet Kuntalp. "Analysing the Performance of LDA (Linear Discriminant Analysis) Feature for Diagnosing PAF (Paroxysmal Atrial Fibrillation) Patients." In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), pp. 1-4. IEEE, 2019; https://doi:10.1109/EBBT.2019.8741871.
[ 30] Plesinger, Filip, Petr Nejedly, Ivo Viscor, Josef Halamek, and Pavel Jurak. "Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG." Physiological measurement 39, no. 9 (2018): 094002; https://doi.org/10.1088/1361-6579/aad9ee.
[ 31] Shao, Minggang, Guangyu Bin, Shuicai Wu, Guanghong Bin, Jiao Huang, and Zhuhuang Zhou. "Detection of atrial fibrillation from ECG recordings using decision tree ensemble with multi-level features." Physiological measurement 39, no. 9 (2018): 094008; https://doi.org/10.1088/1361-6579/aadf48.
[ 32] Sánchez, FA Rivera, and JA González Cervera. "ECG classification using artificial neural networks." In Journal of Physics: Conference Series, vol. 1221, no. 1, p. 012062. IOP Publishing, 2019;
https://doi.org/10.1088/1742-6596/1221/1/012062.
[ 33] Shi, Jingang, Iman Alikhani, Xiaobai Li, Zitong Yu, Tapio Seppänen, and Guoying Zhao. "Atrial fibrillation detection from face videos by fusing subtle variations." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 8 (2019): 2781-2795; https://doi:10.1109/TCSVT.2019.2926632.
[ 34] Zhao, Yunxiang, Jinyong Cheng, Ping Zhang, and Xueping Peng. "ECG classification using deep CNN improved by wavelet transform." Computers, Materials & Continua 64, no. 3 (2020): 1615-1628; https://doi:10.32604/cmc.2020.09938.
[ 35] Mousavi, Sajad, Fatemeh Afghah, and U. Rajendra Acharya. "HAN-ECG: An interpretable atrial fibrillation detection model using hierarchical attention networks." Computers in Biology and Medicine 127 (2020): 104057; https://doi.org/10.1016/j.compbiomed.2020.104057.
[ 36] Alsaleem, Mona, and Md Saiful Islam. "POSTER: Atrial Fibrillation Detection Using a Double-Layer Bi-Directional LSTM Neural Networks." In 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), pp. 266-267. IEEE, 2020; https://doi:10.1109/SMART-TECH49988.2020.00071.
[ 37] Hsieh, Chaur-Heh, Yan-Shuo Li, Bor-Jiunn Hwang, and Ching-Hua Hsiao. "Detection of Atrial Fibrillation Using 1D convolutional neural network." Sensors 20, no. 7 (2020): 2136; https://doi.org/10.3390/s20072136.
[ 38] Nguyen, Quang H., Binh P. Nguyen, Trung B. Nguyen, Trang TT Do, James F. Mbinta, and Colin R. Simpson. "Stacking segment-based CNN with SVM for recognition of atrial fibrillation from single-lead ECG recordings." Biomedical Signal Processing and Control 68 (2021): 102672;
https://doi.org/10.1016/j.bspc.2021.102672.
[ 39] Fayyazifar, Najmeh. "An accurate CNN Architecture for Atrial Fibrillation Detection Using Neural Architecture Search." In 2020 28th European Signal Processing Conference (EUSIPCO), pp. 1135-1139. IEEE, 2021; https://doi.10.23919/Eusipco47968.2020.9287496.
| Files | ||
| Issue | Vol 11 No 4 (2024) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v11i4.16504 | |
| Keywords | ||
| Atrial Fibrillation Electrocardiography Discrete Wavelet Transform Long Short Term Memory Support Vector Machine Decision Tree | ||
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How to Cite
1.
Rao S.K S, Joy Martis R. Deep Learning Based Atrial Fibrillation Detection Using Combination of Dimensionality Reduction Techniques and RR Interval Features. Frontiers Biomed Technol. 2024;11(4):563-575.
