Portable ECG Machine with Future Prediction

Abstract

Approximately thirty percent of people live in poverty in rural areas. The difficulty of limited access to nursing and diagnostic services stems from the outdated healthcare infrastructure. As a result, when heart failure strikes, people frequently neglect to get help and make use of the resources that are accessible. A study suggests a smart electrocardiogram (ECG) monitoring system for heart patients based on the Internet of Things to address these problems. The ECG sensing network (data gathering), IoT cloud (data transmission), result analysis (data prediction), and monetization are the various components that make up the system. The P, Q, R, S, and T ECG signal characteristics are gathered by the ECG sensor network. For the purpose of managing future health, these signals are then pre-processed, examined, and projected down to the age level. Hypertext Transfer Protocol (HTTP) servers and message queuing telemetry transport (MQTT) systems can both access the cloudstored data. To ascertain the influence of error rate and ECG signal properties, the study used the linear regression method. The prediction evaluates the PQRST regularity variation and its applicability to an ECG monitoring device. The suggested system seeks to attain acceptable results by identifying the quality parameter values, which would ultimately lower future medical expenses and challenges for heart patients.

Introduction

I. INTRODUCTION

The study, which recorded 854,253 deaths overall, found that heart disease was responsible for 21.1% of deaths, with heart attacks accounting for 180,408 of those deaths. According to the general pattern, people with cardiac illnesses usually wait until they feel ill before seeking medical assistance, frequently when the condition is severe and irreversible damage has already happened. There is a push for a paradigm change that would standardize passive healthcare in order to combat this.

It is suggested that physicians keep a close eye on their patients' physical health in order to provide proactive treatment based on current conditions. Significant progress has been made in the field of medicine and healthcare systems in the last few years. Reduced cellular connectivity costs have made it easier to integrate health surveillance systems into commonplace devices like cellphones. The goal of this strategic integration is to solve problems like the lack of medical equipment and services. Particularly, there is potential for using Internet of Things (IoT) technology to monitor electrocardiograms (ECGs) and identify cardiac issues early. The use of IoT in ECG monitoring has been the subject of earlier study, which points to a promising future for technological integration in healthcare.

The incorporation of machine learning algorithms into electrocardiogram (ECG) devices represents a noteworthy progression in healthcare technology, namely in terms of augmenting diagnostic skills and facilitating prospective forecasts. By adding these algorithms, conventional ECG machines are intended to become smart devices that may anticipate future problems in addition to identifying existing heart disorders. By examining patterns and trends in the gathered data, machine learning algorithms give ECG devices a predictive aspect.

This takes a proactive approach to treating cardiovascular health, going above and beyond traditional diagnostic techniques. These algorithms use patient records, previous ECG data, and a wide range of pertinent characteristics to find minor patterns that may indicate impending cardiac events. The capacity to quickly identify abnormalities and departures from known patterns is a crucial component.

Through the use of a variety of datasets, machine learning models can be taught to identify minute differences in ECG signals that could be early warning indications of heart problems. By taking proactive identification, serious health consequences may be avoided by enabling prompt intervention and preventive actions.

A. Block Diagram With Description

Healthcare has undergone a transformation thanks to the convergence of wearable monitoring technologies and the Internet of Things (IoT), which has made real-time monitoring and management solutions possible. When it comes to medical devices in particular, this integration provides reliable and consistent services that help those who need care for the elderly, manage chronic illnesses, or require constant monitoring. Wide-ranging health data generated by IoT devices is essential for in-the-moment modifications and timely alerts. This work presents a novel Internet of Things (IoT) solution for the management of cardiac sickness that uses Arduino Mega 2560 sensors that are inserted into the patient's chest to record ECG data. Through the use of an ESP8266 Wi-Fi module, the data is smoothly transported to a cloud server, making it simple for medical experts to access via MQTT and HTTP servers. The data is handled well by a non-relational database, enabling the creation of an online application for the diagnosis of cardiac problems. The For improved patient care, IoT-based cloud solutions guarantee accurate, dependable ,and efficient data collecting and processing.

To find out if the pulse tracker is functioning, the heartbeat result is compared to the heartbeat output of an automated existing pressure measurement system. Data was collected from five different people with different ages.

This chart shows the data on a specific day and time:

II. ACKNOWLEDGMENT

Finally, a potential direction for preventive cardiovascular healthcare is the application of machine learning algorithms to improve electrocardiogram (ECG) equipment with future prediction capabilities. Significant progress has been made in reliably forecasting the status of angiographic diseases by the examination of several machine learning techniques, including as logistic regression, K-Nearest Neighbours, and Decision Trees. High accuracy and an F1 score were displayed by the logistic regression model, demonstrating dependable predictive performance. To assure clinical efficacy, additional optimisation and validation are necessary. In cardiovascular care, the effective use of ML-driven ECG devices holds great promise for early identification, individualised treatment plans, and better patient outcomes.

To fully utilise ML algorithms in transforming cardiac care and lowering the prevalence of cardiovascular illness, more research and development in this area are required.

Conclusion

Finally, a potential direction for preventive cardiovascular healthcare is the application of machine learning algorithms to improve electrocardiogram (ECG) equipment with future prediction capabilities. Significant progress has been made in reliably forecasting the status of angiographic diseases by the examination of several machine learning techniques, including as logistic regression, K-Nearest Neighbours, and Decision Trees. High accuracy and an F1 score were displayed by the logistic regression model, demonstrating dependable predictive performance. To assure clinical efficacy, additional optimisation and validation are necessary. In cardiovascular care, the effective use of ML-driven ECG devices holds great promise for early identification, individualised treatment plans, and better patient outcomes. To fully utilise ML algorithms in transforming cardiac care and lowering the prevalence of cardiovascular illness, more research and development in this area are required.

References

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Copyright

Copyright © 2024 Arundhati Nagdive, Eshan Joshi, Aditi Galgalikar, Nitesh Chimurkar, Gaurav Vamsi Peri, Dr. Shubhangi Rathkanthiwar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.