The Design and Implementation of Smart ECG Machine using Machine Learning
DOI:
https://doi.org/10.64882/ijrt.v14.iS1.1149Keywords:
ECG (electrocardiogram), Machine Learning, Confusion Matrix, Cardiovascular Diseases, MATLABAbstract
This research paper develops a portable, AI-based system for real-time heart disease monitoring and prediction, less expensive, heavy diagnostic tool by integrating ECG analysis with vital signs like SpO2 and pulse rate. The system consists of an Arduino UNO R4 WiFi microcontroller interfaced with MAX30102 pulse oximeter and AD8232 ECG module to collect data, which is transmitted to MATLAB for signal processing, feature extraction (e.g., R-peaks, RR intervals), and machine learning classification of arrhythmias such as bradycardia, tachycardia, and ventricular tachycardia. To further assess the model’s effectiveness, we performed confusion matrix and receiver operating characteristic (ROC) analysis. Predictions and vital readings are displayed as a result on an OLED screen, enabling accessible, non-invasive early detection with high accuracy.
References
A.G. Skrivanos et al.: Machine Learning-Based Heart Disease Detection Scheme VOLUME 13, 2025 IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 29, NO. 8, AUGUST 2025
G. B. Krishnapriya et al.: A Resource-Efficient Time- Domain-Based Algorithm to Estimate Respiration Rate from Single-Lead ECG Signal, Vol 4, 06 March 2025
H. Liu et al.: Detection of Fetal ECG R Wave from Single-Lead an ECG
K.-K. Tseng et al.: Robust Multistage ECG Identification for Astronaut Spacesuits with IoT Applications
M. Wasimuddin et al.: Stages-Based ECG Signal Analysis: A Survey
T. Subba, T. Chingtham: Comparative Analysis of ML Algorithms with Advanced Feature Extraction. International Journal of Computer and Electrical Engineering, Vol. 4, No. 3, June 2012
U. Sumalatha et al.: Deep Learning Applications in ECG Analysis and Disease Detection VOLUME 12, 2024
Xinwen Liu et al.: Deep learning in ECG diagnosis: A review, Vol 25, 5 September 2021, 107187
Y. Qiu et al.: Pre-Orthogonal AFD for ECG Denoising and CHD Monitoring.
Downloads
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
