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Deep Learning Convolutional Neural Network for ECG Signal Classification Aggregated Using IoT

S. Karthiga*, A. M. Abirami

Department of Information Technology, Thiagarajar College of Engineering, 625015, Tamil Nadu, India

* Corresponding Author: S. Karthiga. Email: email

Computer Systems Science and Engineering 2022, 42(3), 851-866. https://doi.org/10.32604/csse.2022.021935

Abstract

Much attention has been given to the Internet of Things (IoT) by citizens, industries, governments, and universities for applications like smart buildings, environmental monitoring, health care and so on. With IoT, network connectivity is facilitated between smart devices from anyplace and anytime. IoT-based health monitoring systems are gaining popularity and acceptance for continuous monitoring and detect health abnormalities from the data collected. Electrocardiographic (ECG) signals are widely used for heart diseases detection. A novel method has been proposed in this work for ECG monitoring using IoT techniques. In this work, a two-stage approach is employed. In the first stage, a routing protocol based on Dynamic Source Routing (DSR) and Routing by Energy and Link quality (REL) for IoT healthcare platform is proposed for efficient data collection, and in the second stage, classification of ECG for Arrhythmia. Furthermore, this work has evaluated Support Vector Machine (SVM), Artificial Neural Network (ANN), and Convolution Neural Networks (CNNs)-based approach for ECG signals classification. Deep-ECG will use a deep CNN to extract critical features and then compare through evaluation of simple and fast distance functions in order to obtain an efficient classification of heart abnormalities. For the identification of abnormal data, this work has proposed techniques for the classification of ECG data, which has been obtained from mobile watch users. For experimental verification of the proposed methods, the Beth Israel Hospital (MIT/BIH) Arrhythmia and Massachusetts Institute of Technology (MIT) Database was used for evaluation. Results confirm the presented method’s superior performance with regards to the accuracy of classification. The CNN achieved an accuracy of 91.92% and has a higher accuracy of 4.98% for the SVM and 2.68% for the ANN.

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APA Style
Karthiga, S., Abirami, A.M. (2022). Deep learning convolutional neural network for ECG signal classification aggregated using iot. Computer Systems Science and Engineering, 42(3), 851-866. https://doi.org/10.32604/csse.2022.021935
Vancouver Style
Karthiga S, Abirami AM. Deep learning convolutional neural network for ECG signal classification aggregated using iot. Comput Syst Sci Eng. 2022;42(3):851-866 https://doi.org/10.32604/csse.2022.021935
IEEE Style
S. Karthiga and A.M. Abirami, "Deep Learning Convolutional Neural Network for ECG Signal Classification Aggregated Using IoT," Comput. Syst. Sci. Eng., vol. 42, no. 3, pp. 851-866. 2022. https://doi.org/10.32604/csse.2022.021935



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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