Smart implantable devices for cardiac health: A novel self-powered wireless ECG monitoring system using energy harvesting and machine learning-driven anomaly detection

Introduction of smart implantable devices is changing the face of cardiac health monitoring through continuous and real time ECG monitoring useful in early diagnosis of cardiac pathologies. This paper describes CardioHarvest-Net , a newly developed self-powered wireless ECG monitoring system that employs, physiological movements for its power in order to reduce the probability of frequent power replenishment. This self-powered capability eliminates dependency on conventional batteries, thereby offering a viable solution for continuous, long-term cardiac monitoring in real-world conditions. CardioHarvest-Net enables an enhanced machine learning (ML)-based anomaly detection model that learns and adapt to each patient’s cardiac behavior to provide high sensitivity in abnormal ECG signs related to diseases like arrhythmia, myocardial infarction, and other diseases of the heart. The CardioHarvest-Net model applies CNN for feature extraction of vital signs such as ECG and uses LSTM for temporal feature extraction for accurate anomaly detection in real-world settings. Evaluation results reveal that gain scores of cardio health phenomena via CardioHarvest-Net is a detection accuracy of 97.2% and the anomaly recall rate of 95.3% that qualifies the proposed system as an effective and timely monitoring tool of putting up a signal and cautionary measure on possible event of cardiac occurrences. The average response time for an entire system to detect an anomaly is 10 ms, which makes the system’s intervention capacity rather fast. Moreover, they use a power build-up efficiency of 78% in otherwise low power, real-life in-vivo conditions ranging from acute circumstances to chronic conditions requiring prolonged operation. This ML model is running on an energy-efficient microcontroller suitable for wearable and implantable medical devices along with a feedback adaptation that enhances the accuracy of the predictions based on data that changes over time concerning an individual patient. The outcomes of this study further state the viability of CardioHarvest-Net to transform sustainable cardiac niche by addressing limitations into power independence and facilitating real-time tracking. This development is a breakthrough in moving towards the preventive, long-term approach to cardiac reliability enhancing our method in a manner that offers a solid framework for constant, individualized cardiac monitoring, and timely action in cases of essential occurrences in the heart.