International Journal of Science, Engineering and TechnologyAuthors: Research Scholar Anitha Udayakumar, Professor Senthil Kumar Thillaigovindhan
Abstract: Despite significant advances in health care systems and technology, cardiovascular diseases (CVDs) continue to be the most common causes of death globally, with over 17.9 million fatalities per year. Continuous monitoring of cardiovascular health is vital for early identification of abnormalities but is limited by bulky, non-continuous nature of existing solutions such as the Holter monitor. In this work, we present a new intelligent wearable that provides continuous, real-time monitoring of the heart activity using an edge-AI approach based on an ECG, photoplethysmography (PPG), and accelerometer sensors. Specifically, our solution incorporates an energy-efficient multi-sensor wearable that transmits data to an edge-AI processor, running a lightweight 1D-CNN-LSTM model, for real-time classification of cardiac arrhythmia. The presented federated learning technique allows personalizing models across the population to guarantee individual-level privacy while maintaining high performance. In extensive experiments conducted using MIT-BIH Arrhythmia Database (n=49 patients) and a clinical trial (n=120 patients), our solution demonstrated up to 98.3% sensitivity and 97.6% specificity in recognizing 11 classes of cardiac arrhythmias, as well as end-to-end latency less than 100ms.
