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

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.

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

“…With regard to DL approaches, convolutional neural network (CNN) architecture was applied to arrhythmia [ 52 , 53 , 54 ] and AF classifications [ 24 , 55 ]. Other architectures of interest for AF classification include a deep densely connected neural network based on 12-lead ECG [ 15 ], a feedforward neural network based on features encompassing R-R intervals [ 56 ] and another based on the Lightweight Fusing Transformer [ 17 ].…”

Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment

“…The latter task was performed using the developed Coy-Grey Wolf optimizerbased deep CNN (Coy-GWO-based Deep CNN) classifier for the detection of anomalies in ECG signal. Jagadeesh et al [13] introduced an automatic arrhythmia classification technique by applying a Harris Hawks optimizer-based DL (AC-HHODL) approach in the IoT. In this study, the MobileNetv2 algorithm was implemented to generate a group of feature vectors.…”

A Novel Personalized Incremental Arrhythmias Classification Method for ECG Monitoring

“…Mary [6] designed an IoT system based on ECG classification, deploying adaptive deep neural networks on the cloud for real-time ECG monitoring. S. Karthiga [7] designed an ECG classification framework based on an IoT system and researched the diagnostic performance of SVM, ANN, and CNN in such systems. In this IoT-based system, the models are typically deployed on cloud servers, lacking the adaptive ability to accommodate the varied distribution of ECG signals accumulated from individual users.…”