A power-aware ECG processing node for real-time feature extraction in WBAN

Abstract

In modern WBAN (Wireless Body Area Network) applications, wearable sensor nodes are advancing towards in-node detection of cardiovascular diseases with continuous observation of ECG signal. However, a robust power-aware architecture with in-node processing for extraction of cardiac events triggers challenges due to constraints in hardware resources in addition to intensive computational complexity. In this work, a dual mode based transmission system depending on battery-level is discussed that reduces bitstream load of raw ECG signal for saving transmission power. For encoding the bitstream, a power-aware ECG processing architecture is proposed that performs a real-time feature extraction for continuous health monitoring. The proposed architecture explores the advantage of low computationally complex operations focusing on a resource constraint sensor node. The architecture deals with two types of cardiac anomalies i.e., irregular heart rate variation (HRV) and 1st degree AV block; detecting R-peak and P-peak through adaptive thresholding technique. The overall sensitivity and positive predictivity for the proposed algorithm reach upto 99% while validated with the MIT-BIH arrhythmia database. The proposed architecture is synthesized in TSMC 90 nm technology node with an estimated power consumption of 2.11 µW, consuming an area overhead of 0.087 mm2. The architecture operates at 1.2 V with an operating frequency of 10 KHz.