Poster: Smart ECG Classification with Wearable Sensing and Cloud AI: A Mobile Health Approach Using Multi-Feature Time Series

We introduce a wearable‑based system for real‑time ECG anomaly detection and contextual interpretation within a mobile‑health framework. Twenty‑four‑hour Holter ECG data are synchronized over wireless/mobile networks with e.g. Apple Health streams (iPhone + iWatch), including activity states (walking, running, resting, sleeping) and heart rate history. A hybrid preprocessing pipeline extracts instantaneous frequency (Hilbert), spectral entropy, and RMS energy, concatenated into fixed‑length multichannel tensors for deep‑learning models deployed via edge or cloud SaaS. The model detects critical cardiac anomalies correlating each with user activity and exertion context. This multimodal approach distinguishes physiological deviations during motion from pathological events at rest or sleep and suppresses motion artifacts. Experiments with subjects wearing both Holter and Apple devices demonstrate improved sensitivity and specificity versus ECG‑only baselines. Our system exemplifies wearable computing, mobile health, ML‑enabled mobile systems, and edge/cloud mobile analytics. Fig. 1 shows a complete system for recording and classifying ECG signals, including a Holter ECG with electrodes, a smartphone and a smartwatch [1].