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Published in:
Respiratory Disease Diagnosis with Cough Sound Analysis Read chapter Read first chapter

2023 | OriginalPaper | Chapter

A Deep Learning-Based Model for Arrhythmia Detection Using Feature Selection and Machine Learning Methods

Authors : Santosh Kumar, Bharat Bhushan, Lekha Bhambhu, Debabrata Singh, Dilip Kumar Choubey

Published in: Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering

Publisher: Springer Nature Singapore

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Abstract

Arrhythmia is one of the diseases that affects many people around the world. Deep learning provides an efficient tool to detect arrhythmia disease. A convolutional neural network (CNN) is an emerging technique used often for feature extraction in the medical domain. In this paper, AlexNet, VGG-16, VGG-19 models are used as the feature extraction method, and the selected feature is supplied as input to four well-known classifiers such as decision tree, kNN, LDA, and SVM for arrhythmia detection. Furthermore, an experiment is conducted with the combination of proposed CNN model where mRMR is used as feature selection method. Finally, the result of experiment is compared with different machine learning algorithms where LDA shows the efficiency in term of classification accuracy. The classification accuracy of the proposed model is recorded as 99.46%. The performance of the proposed model is higher in terms of classification accuracy compared to previous work on arrhythmia detection.

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Literature
1.
Isin, A., & Ozdalili, S. (2017). Cardiac arrhythmia detection using deep learning. Procedia computer science, 120, 268–275.CrossRef
2.
Acharya, U. R., et al. (2017). A deep convolutional neural network model to classify heartbeats. Computers in Biology and Medicine, 89, 389–396.CrossRef
3.
Warrick, P. A., & Homsi, M. N. (2018). Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection. Physiological Measurement, 39(11), 114002.CrossRef
4.
Mousavi, S., & Afghah, F. (2019). Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: A sequence to sequence deep learning approach. In IEEE international conference on acoustics, speech and signal processing (ICASSP).
5.
Dindin, M., Umeda, Y., & Chazal, F. (2020). Topological data analysis for arrhythmia detection through modular neural networks. In Canadian conference on AI.
6.
Sannino, G., & De Pietro, G. (2018). A deep learning approach for ECG-based heartbeat classification for arrhythmia detection. Future Generation Computer Systems, 86, 446–455.CrossRef
7.
Sainath, T. N., et al. (2015). Convolutional, long short-term memory, fully connected deep neural networks. In IEEE international conference on acoustics, speech and signal processing (ICASSP).
8.
Li, C., et al. (2017). Skeleton-based action recognition using LSTM and CNN. In IEEE international conference on multimedia & expo workshops (ICMEW).
9.
Yildirim, O., et al. (2019). A new approach for arrhythmia classification using deep coded features and LSTM networks. Computer Methods and Programs in Biomedicine, 176, 121–133.CrossRef
10.
Mathews, S. M., Kambhamettu, C., & Barner, K. E. (2018). A novel application of deep learning for single-lead ECG classification. Computers in Biology and Medicine, 99, 53–62.CrossRef
11.
Yıldırım, Ö., et al. (2018). Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Computers in Biology and Medicine, 102, 411–420.CrossRef
12.
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.CrossRef
13.
Quinlan, J. R. (1993) C4. 5: Programming for machine learning. Morgan Kauffmann, 38, 48.
14.
Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21–27.CrossRefMATH
15.
Mustaqeem, A., Anwar, S. M., & Majid, M. (2018). Multiclass classification of cardiac arrhythmia using improved feature selection and SVM invariants. Computational and Mathematical Methods in Medicine.
16.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learnin. Cited on 33.
17.
Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238.CrossRef
Metadata
Title
A Deep Learning-Based Model for Arrhythmia Detection Using Feature Selection and Machine Learning Methods
Authors
Santosh Kumar
Bharat Bhushan
Lekha Bhambhu
Debabrata Singh
Dilip Kumar Choubey
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering

Print ISBN: 978-981-19-2224-4

Electronic ISBN: 978-981-19-2225-1

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-2225-1_2