Key result
Deep neural network-enabled portable ECG sensor enables real-time automated heart disease classification.
Why the study?
Early detection and continuous monitoring of heart disease are critically important for improving patient outcomes due to its status as a leading cause of death worldwide.
A low-cost, portable embedded system combining Arduino-based physiological sensors with deep neural networks can provide automated, real-time classification of ECG abnormalities.
Heart disease continues to be one of the leading causes of death worldwide, making early detection and continuous monitoring critically important for improving patient outcomes. This paper presents a novel approach for heart disease classification using electrocardiogram (ECG) signals combined with deep neaural network techniques.
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K. Lakshmanrao,Y Kotari Harsha Vardhan, Koka Kasaiah, Nelluri Jaswanth, Mitta Ganesh (2026) studied Heart Disease. Portable heart disease classification system using ECG, MAX30102 sensor, and Deep Neural Networks was evaluated on Classification accuracy of heart conditions (normal vs abnormal). A cost-effective, portable heart disease classification system integrating ECG signals, a MAX30102 sensor, and deep neural networks was successfully designed for real-time automated diagnosis.
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