Integrating Delta Modulation and Stochastic Computing for Real-time Machine Learning based Heartbeats Monitoring in Wearable Systems

Real-time electrocardiogram (ECG) monitoring using wearable devices is crucial for early cardiovascular disease diagnosis and by using machine learning (ML) algorithms, it can be automated. Unfortunately, wearable devices face stringent hardware resource constraints, and thus low-complexity designs that can implement ML-based detection of heartbeat anomalies are required. This paper proposes the integration of a delta modulator (DM) used to digitize the ECG signal with a Stochastic Computing (SC) implementation of the ML algorithms. The DM enables a low-cost conversion of the ECG to binary sequences that are then directly processed in the SC implementation of an ML algorithm. This eliminates the need of converting the DM outputs to integers and then to stochastic sequences and thus the proposed integrated design considerably reduces the complexity of the system. The proposed scheme has been evaluated on a premature ventricular contraction (PVC) heartbeat recognition system based on a support vector machine classifier. The estimated chip area and power dissipation of the proposed system using a commercial 180nm CMOS technology are 0.36 mm2 and 0.6 µW, respectively, so achieving more than 38% and 54% reduction in these metrics compared to state-of-the-art solutions while providing similar performance in terms of heartbeat anomaly detection.