Internet of Things (IoT) based ECG System for Rural Health Care

TL;DR

This paper presents an IoT-based ECG monitoring system tailored for rural healthcare in Bangladesh, utilizing cloud storage and machine learning to improve cardiovascular diagnosis and reduce healthcare costs.

Contribution

It introduces an innovative IoT-enabled ECG system with machine learning analysis specifically designed for rural health monitoring, addressing technology gaps in underserved areas.

Findings

Logistic regression effectively predicts ECG parameters.

The system accurately monitors cardiovascular health.

Cost reduction potential for rural healthcare.

Abstract

Nearly 30% of the people in the rural areas of Bangladesh are below the poverty level. Moreover, due to the unavailability of modernized healthcare-related technology, nursing and diagnosis facilities are limited for rural people. Therefore, rural people are deprived of proper healthcare. In this perspective, modern technology can be facilitated to mitigate their health problems. ECG sensing tools are interfaced with the human chest, and requisite cardiovascular data is collected through an IoT device. These data are stored in the cloud incorporates with the MQTT and HTTP servers. An innovative IoT-based method for ECG monitoring systems on cardiovascular or heart patients has been suggested in this study. The ECG signal parameters P, Q, R, S, T are collected, pre-processed, and predicted to monitor the cardiovascular conditions for further health management. The machine learning algorithm is used to determine the significance of ECG signal parameters and error rate. The logistic regression model fitted the better agreements between the train and test data. The prediction has been performed to determine the variation of PQRST quality and its suitability in the ECG Monitoring System. Considering the values of quality parameters, satisfactory results are obtained. The proposed IoT-based ECG system reduces the health care cost and complexity of cardiovascular diseases in the future.