The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients
Abstract
In this paper, a novel computationally intelligent-based electrocardiogram (ECG) signal classification methodology using a deep learning (DL) machine is developed. The focus is on patient screening and identifying patients with paroxysmal atrial fibrillation (PAF), which represents a life threatening cardiac arrhythmia. The proposed approach operates with a large volume of raw ECG time-series data as inputs to a deep convolutional neural networks (CNN). It autonomously learns representative and key features of the PAF to be used by a classification module. The features are therefore learned directly from the large time domain ECG signals by using a CNN with one fully connected layer. The learned features can effectively replace the traditional ad hoc and time-consuming user's hand-crafted features. Our experimental results verify and validate the effectiveness and capabilities of the learned features for PAF patient screening. The main advantages of our proposed approach are to simplify the feature extraction process corresponding to different cardiac arrhythmias and to remove the need for using a human expert to define appropriate and critical features working with a large time-series data set. The extensive simulations and case studies conducted indicate that combining the learned features with other classifiers will significantly improve the performance of the patient screening system as compared to an end-to-end CNN classifier. The effectiveness and capabilities of our proposed ECG DL classification machine is demonstrated and quantitative comparisons with several conventional machine learning classifiers are also provided.
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features
deChazal, P., O'Dwyer, M., & Reilly, R. B. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827359 · Cited by 1,671
A review of unsupervised feature learning and deep learning for time-series modeling
Längkvist, M., Karlsson, L., & Loutfi, A. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.01.008 · Cited by 1,243
Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]
Arel, I., Rose, D. C., & Karnowski, T. P. (2010) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2010.938364 · Cited by 1,121
Deep convolutional neural networks for LVCSR
Sainath, T. N., Mohamed, A.-R., Kingsbury, B., & Ramabhadran, B. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639347 · Cited by 1,053
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces
Cecotti, H., & Graser, A. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.125 · Cited by 778
Cited by
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019) · Nature Medicine · doi:10.1038/s41591-018-0268-3 · Cited by 3,148
Deep learning for sensor-based activity recognition: A survey
Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.02.010 · Cited by 2,014
A review on deep learning methods for ECG arrhythmia classification
Ebrahimi, Z., Loni, M., Daneshtalab, M., & Gharehbaghi, A. (2020) · Expert Systems with Applications: X · doi:10.1016/j.eswax.2020.100033 · Cited by 447
Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study
Canizo, M., Triguero, I., Conde, A., & Onieva, E. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.07.034 · Cited by 420
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Wang, Y., Cang, S., & Yu, H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.057 · Cited by 409
Metadata
- Authors
- Pourbabaee, B., Roshtkhari, M. J., & Khorasani, K.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Published
- 2018-12
- Volume
- 48
- Issue
- 12
- Pages
- 2095-2104
- ISSN
- 2168-2216 (ISSN-L)
- 2168-2232
- OpenAlex
- W2621205740
- Cited by
- 411
- References
- 27
- FWCI
- 31.17
- Type
- journal article
- Language
- en
- Open access
- closed
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- 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. https://doi.org/10.1109/tsmc.2017.2705582
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