Key result
Proposed mobile app combines wireless ECG and CNNs to automate real-time remote cardiac anomaly detection.
Why the study?
Cardiovascular diseases represent a serious global health issue requiring novel solutions for remote cardiac monitoring and proactive health management.
This paper proposes a novel technological framework combining wireless ECG sensors, a mobile application, and deep learning for real-time remote cardiac monitoring and automated anomaly detection.
May support remote monitoring if validated; leaves open the clinical role of CNN-based ECG anomaly detection.
This research presents a novel solution to the serious global health issue of cardiovascular diseases. The suggested technology revolutionizes remote cardiac monitoring by combining wireless Electrocardiogram sensors and powerful deep learning algorithms. A user-friendly mobile application acts as a center for data collection, storage, and analysis in real time. More importantly, a convolutional neural network can be used for the automatic detection of cardiac problems by pattern extraction from Electrocardiogram readings. The system enables immediate action by sending real-time notifications for detected anomalies, while also keeping a detailed historical record for long-term monitoring. Prioritizing data security, messages are encrypted, and the system contains user authentication mechanisms that are in line with healthcare standards. Engaging individuals in proactive health management, this proposal aims to foster cooperation between users and healthcare providers for better patient outcomes. The suggested system shall offer accessibility, accuracy, and user centricity, coupled with a new approach toward solving the global cardiovascular disease crisis using wireless Electrocardiogram monitoring and deep learning technology.
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Sunil et al. (2024) studied Cardiovascular diseases. Wireless ECG monitoring and deep learning mobile app was evaluated. A proposed mobile application combining wireless ECG sensors and convolutional neural networks aims to provide real-time automated detection of cardiac anomalies for remote patient monitoring.
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