Wireless Healthcare Monitoring System for Heart Diseases Classification using Efficient ECG-Based Wave Modeling and Machine Learning Techniques

Abstract

This paper presents a new methodology for developing a low-cost wireless ECG transmission and monitoring system based on IoT technology, designed for real-time detection and classification of heart diseases. The study focuses on using ECG data for heart disease classification, which is an area of growing interest in recent years. The study collected data from 1000 subjects using our designed system to collect the normal data (300 patients) and a Biopac MP160 data acquisition system for the collection of 10 diseases abnormal data, where all the data are acquired from lead 1. The aim of this study is to develop an accurate and reliable classification model for heart diseases using ECG data. Pre-processing steps were taken to prepare the data for feature extraction, including the use of Empirical Mode Decomposition (EMD) and digital filters such as low pass, high pass, and derivative pass filters. A new feature extraction steps based on a new ECG peak detection, segmentation, and wave modeling for each segment is also presented. Two classification methods were used: Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF). The results showed that MLP had a much higher accuracy of 99.1% compared to RBF, which had an accuracy of 97.4%. The study emphasizes the potential of using ECG data for accurate classification of heart diseases. The results demonstrate that proper pre-processing and feature extraction techniques are crucial for improving accuracy. This study is significant for remote patient monitoring and telemedicine applications, as it provides a low-cost, non-invasive method for detecting and classifying heart diseases using ECG data.