A Hybrid Attention-based LSTM-XGBoost Model for Detection of ECG-based Atrial Fibrillation

Year 2022, Volume: 9 Issue: 3, 199 - 210, 30.09.2022

Furkan Balcı

https://doi.org/10.54287/gujsa.1128006

Cited By: 2

Abstract

Atrial fibrillation (AF) is a frequently encountered heart arrhythmia problem today. In the method followed in the detection of AF, the recording of the Electrocardiogram (ECG) signal for a long time (1-2 days) taken from people who are thought to be sick is analyzed by the clinician. However, this process is not an effective method for clinicians to make decisions. In this article, various artificial intelligence methods are tested for AF detection on long recorded ECG data. Since the ECG data is a time series, a hybrid model has been tried to be created with the Long Short Term Memory (LSTM) algorithm, which gives high results in time series classification and regression, and a hybrid method has been developed with the Extreme Gradient Boosting algorithm, which is derived from the Gradient Boosting algorithm. To improve the accuracy of the LSTM architecture, the architecture has been strengthened with an Attention-based block. To control the performance of the developed hybrid Attention-based LSTM-XGBoost algorithm, a public data set was used. Some preprocessing (filter, feature extraction) has been applied to this data set used. With the removal of these features, the accuracy rate has increased considerably. It has been proven to be a consistent study that can be used as a support system in decision-making by clinicians with an accuracy rate of 98.94%. It also provides a solution to the problem of long ECG record review by facilitating data tracking.

Keywords

LSTM, Atrial Fibrillation, Classification, Deep Learning

References

• Acharya, U. R., Fujita, H., Lih, O. S., Adam, M., Tan, J. H., & Chua, C. K. (2017). Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowledge-Based Systems, 132, 62-71. doi:10.1016/j.knosys.2017.06.003
• Andersen, R. S., Peimankar, A., & Puthusserypady, S. (2019). A deep learning approach for real-time detection of atrial fibrillation. Expert Systems with Applications, 115, 465-473. doi:10.1016/j.eswa.2018.08.011
• Balci, F., & Oralhan, Z. (2020). LSTM ile EEG Tabanlı Kimliklendirme Sistemi Tasarımı. Avrupa Bilim ve Teknoloji Dergisi, Özel Sayı (HORA), 135-141. doi:10.31590/ejosat.779526
• Buscema, P. M., Grossi, E., Massini, G., Breda, M., & Della Torre, F. (2020). Computer aided diagnosis for atrial fibrillation based on new artificial adaptive systems. Computer Methods and Programs in Biomedicine, 191, 105401. doi:10.1016/j.cmpb.2020.105401
• Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). doi:10.1145/2939672.2939785
• Chen, C., Hua, Z., Zhang, R., Liu, G., & Wen, W. (2020). Automated arrhythmia classification based on a combination network of CNN and LSTM. Biomedical Signal Processing and Control, 57, 101819. doi:10.1016/j.bspc.2019.101819
• Chen, X., Cheng, Z., Wang, S., Lu, G., Xv, G., Liu, Q., & Zhu, X. (2021). Atrial fibrillation detection based on multi-feature extraction and convolutional neural network for processing ECG signals. Computer Methods and Programs in Biomedicine, 202, 106009. Doi:10.1016/j.cmpb.2021.106009
• Ciregan, D., Meier, U., & Schmidhuber, J. (2012, June). Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3642-3649). doi:10.1109/CVPR.2012.6248110
• Deng, L., & Yu, D. (2014). Deep Learning: Methods and Applications. Foundations and Trends in Signal Processing, 7(3–4), 197-387. doi:10.1561/2000000039
• Faust, O., Shenfield, A., Kareem, M., San, T. R., Fujita, H., & Acharya, U. R. (2018). Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Computers in Biology and Medicine, 102, 327-335. doi:10.1016/j.compbiomed.2018.07.001
• Fukushima, K., & Miyake, S. (1982). Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. In: S-i. Amari, & M. A. Arbib (Eds.), Competition and Cooperation in Neural Nets (pp. 267-285). Springer, Berlin, Heidelberg. doi:10.1007/978-3-642-46466-9_18
• Guo, L., Sim, G., & Matuszewski, B. (2019). Inter-patient ECG classification with convolutional and recurrent neural networks. Biocybernetics and Biomedical Engineering, 39(3), 868-879. doi:10.1016/j.bbe.2019.06.001
• Hagiwara, Y., Fujita, H., Oh, S. L., Tan, J. H., San Tan, R., Ciaccio, E. J., & Acharya, U. R. (2018). Computer-aided diagnosis of atrial fibrillation based on ECG signals: A review. Information Sciences, 467, 99-114. doi:10.1016/j.ins.2018.07.063
• Jin, Y., Qin, C., Huang, Y., Zhao, W., & Liu, C. (2020). Multi-domain modeling of atrial fibrillation detection with twin attentional convolutional long short-term memory neural networks. Knowledge-Based Systems, 193, 105460. doi:10.1016/j.knosys.2019.105460
• Kalidas, V., & Tamil, L. S. (2019). Detection of atrial fibrillation using discrete-state Markov models and Random Forests. Computers in Biology and Medicine, 113, 103386. doi:10.1016/j.compbiomed.2019.103386
• Kim, T.-Y., & Cho, S.-B. (2019). Predicting residential energy consumption using CNN-LSTM neural networks. Energy, 182, 72-81. doi:10.1016/j.energy.2019.05.230
• Kiranyaz, S., Ince, T., & Gabbouj, M. (2015). Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Transactions on Biomedical Engineering, 63(3), 664-675. doi:10.1109/TBME.2015.2468589
• Król-Józaga, B. (2022). Atrial fibrillation detection using convolutional neural networks on 2-dimensional representation of ECG signal. Biomedical Signal Processing and Control, 74, 103470. doi:10.1016/j.bspc.2021.103470
• Kumar, M., Pachori, R. B., & Acharya, U. R. (2018). Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform. Biocybernetics and Biomedical Engineering, 38(3), 564-573. doi:10.1016/j.bbe.2018.04.004
• Li, H., Pan, D., & Chen, C. P. (2014). Intelligent prognostics for battery health monitoring using the mean entropy and relevance vector machine. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(7), 851-862. doi:10.1109/TSMC.2013.2296276
• Mitchell, R., & Frank, E. (2017). Accelerating the XGBoost algorithm using GPU computing. PeerJ Computer Science, 3, e127. doi:10.7717/peerj-cs.127
• Moody, G. B., & Mark, R. G. (1983). A new method for detecting atrial fibrillation using R-R intervals. Computers in Cardiology, 227-230.
• Pascanu, R., Mikolov, T., & Bengio, Y. (2012). Understanding the exploding gradient problem. Computing Research Repository (CoRR). arxiv.org/abs/1211.5063v1
• Petmezas, G., Haris, K., Stefanopoulos, L., Kilintzis, V., Tzavelis, A., Rogers, J. A., Katsaggelos, A. K., & Maglaveras, N. (2021). Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets. Biomedical Signal Processing and Control, 63, 102194. doi:10.1016/j.bspc.2020.102194
• Pourbabaee, B., Roshtkhari, M. J., & Khorasani, K. (2018). Deep convolutional neural networks and learning ECG features for screening paroxysmal atrial fibrillation patients. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(12), 2095-2104. doi:10.1109/TSMC.2017.2705582
• Sadeghi, D., Shoeibi, A., Ghassemi, N., Moridian, P., Khadem, A., Alizadehsani, R., Teshnehlab, M., Gorriz, J. M., Khozeimeh, F., Zhang, Y.-D., Nahavandi, S., & Acharya, U. R. (2022). An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Computers in Biology and Medicine, 146, 105554. doi:10.1016/j.compbiomed.2022.105554
• Shoeibi, A., Khodatars, M., Jafari, M., Moridian, P., Rezaei, M., Alizadehsani, R., Khozeimeh, F., Gorriz, J. M., Heras, J., Panahiazar, M., Nahavandi, S., & Acharya, U. R. (2021). Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review. Computers in Biology and Medicine, 136, 104697. doi:10.1016/j.compbiomed.2021.104697
• Song, S., Huang, H., & Ruan, T. (2019). Abstractive text summarization using LSTM-CNN based deep learning. Multimedia Tools and Applications, 78(1), 857-875. doi:10.1007/s11042-018-5749-3
• Stollenga, M. F., Byeon, W., Liwicki, M., & Schmidhuber, J. (2015). Parallel multi-dimensional LSTM, with application to fast biomedical volumetric image segmentation. In: Proceedings of the Advances in Neural Information Processing Systems (pp. 2998-3006).
• Wang, J., Wang, P., & Wang, S. (2020). 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, 101662. doi:10.1016/j.bspc.2019.101662
• Wei, X., Li, J., Zhang, C., Liu, M., Xiong, P., Yuan, X., Li, Y., Lin, F., & Liu, X. (2019). Atrial fibrillation detection by the combination of recurrence complex network and convolution neural network. Journal of Probability and Statistics, 2019. doi:10.1155/2019/8057820
• Xiong, Z., Stiles, M. K., & Zhao, J. (2017, September). Robust ECG signal classification for detection of atrial fibrillation using a novel neural network. In: 2017 Computing in Cardiology (CinC), vol.44, (pp. 1-4). doi:10.22489/CinC.2017.066-138
• Yao, Q., Wang, R., Fan, X., Liu, J., & Li, Y. (2020). Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network. Information Fusion, 53, 174-182. doi:10.1016/j.inffus.2019.06.024
• Yin, Y., Zheng, X., Hu, B., Zhang, Y., & Cui, X. (2021). EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM. Applied Soft Computing, 100, 106954. doi:10.1016/j.asoc.2020.106954
• Zarei, R., He, J., Huang, G., & Zhang, Y. (2016). Effective and efficient detection of premature ventricular contractions based on variation of principal directions. Digital Signal Processing, 50, 93-102. doi:10.1016/j.dsp.2015.12.002
• Zhang, S., Wu, Y., Che, T., Lin, Z., Memisevic, R., Salakhutdinov, R. R., & Bengio, Y. (2016). Architectural complexity measures of recurrent neural networks. In: D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, & R. Garnett (Eds.) Advances in Neural Information Processing Systems 29 (NIPS 2016).