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Erschienen in:
2023 | OriginalPaper | Buchkapitel
A Deep Learning-Based Model for Arrhythmia Detection Using Feature Selection and Machine Learning Methods
verfasst von : Santosh Kumar, Bharat Bhushan, Lekha Bhambhu, Debabrata Singh, Dilip Kumar Choubey
Erschienen in: Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering
Verlag: 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.