Why the study?
Early detection and continuous monitoring of heart health are needed to improve patient outcomes and reduce mortality rates associated with heart diseases.
A proposed real-time ECG monitoring system integrating IoT and a CNN model trained on 22,167 images can classify ECG abnormalities into six categories, potentially aiding early detection of heart conditions.
Heart diseases have become one of the leading causes of mortality worldwide. Early detection and continuous monitoring of heart health can significantly improve patient outcomes and reduce mortality rates. In this paper, we propose a real-time ECG and pulse monitoring system that uses proposed system. The system employs an ECG sensor and a pulse sensor that is attached to the patient's chest to collect ECG signals and pulse data, which are read and visualized. The ECG data is also stored in a CSV file which can be used to get the past ECG data. The proposed CNN model is a deep learning algorithm that can detect abnormalities in ECG signals and classify them into six different categories. The model is trained on 22167 images of preclassified ECG signals on the IBM Watson cloud platform and deployed. A GUI has been created using flask to enable users to conveniently upload ECG signals, obtain a classification of their heart condition, and monitor their heart health in a convenient and accessible manner. This can contribute to early detection and treatment of heart conditions, potentially improving patient outcomes.
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Jakka et al. (2024) studied this question.
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