Effective classification of ECG signals using enhanced convolutional neural network in IOT

TL;DR

This paper presents a novel IoT-based ECG monitoring system utilizing deep convolutional neural networks for efficient and accurate cardiac signal classification, validated on MIT/BIH data with superior performance.

Contribution

It introduces a deep CNN approach for ECG classification within IoT healthcare, outperforming existing methods in accuracy and efficiency.

Findings

Deep CNN achieves higher classification accuracy.

Proposed system effectively identifies cardiac abnormalities.

Outperforms traditional machine learning approaches.

Abstract

In this paper, a novel ECG monitoring approach based on IoT technology is suggested. This paper proposes a routing system for IoT healthcare platforms based on Dynamic Source Routing (DSR) and Routing by Energy and Link Quality (REL). In addition, the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Convolution Neural Networks (CNNs)-based approaches for ECG signal categorization were tested in this study. Deep-ECG will employ a deep CNN to extract important characteristics, which will then be compared using simple and fast distance functions in order to classify cardiac problems efficiently. This work has suggested algorithms for the categorization of ECG data acquired from mobile watch users in order to identify aberrant data. The Massachusetts Institute of Technology (MIT) and Beth Israel Hospital (MIT/BIH) Arrhythmia Database have been used for experimental verification of the suggested approaches. The results show that the proposed strategy outperforms others in terms of classification accuracy.