A Low-Power Co-Processor to Predict Ventricular Arrhythmia for Wearable Healthcare Devices

Abstract

Ventricular arrhythmia (VA) is the most critical cardiac anomaly among all arrhythmia beats. Thus, it becomes imperative to predict the occurrence of VA to avoid sudden casualties caused by these arrhythmia beats. In the past, only a few hardware designs have been proposed to predict VA using various features derived from electrocardiogram (ECG) signals and processed using machine learning classifiers. However, these designs are either complex or need more prediction accuracy. Therefore, a deep neural network (DNN)-based co-processor for arrhythmia prediction is proposed in this article. It can predict VA at least $15 \ \min $ before its occurrence with 91.6% accuracy. Co-processor architecture for arrhythmia prediction (CoAP) uses an optimal feature vector extracted from the ECG signal and an optimized DNN, using a novel approximate multiplier (AM). CoAP operates at 12.5 kHz and consumes $4.69~\mu \text { W}$ when implemented using SCL $180\text {-nm}$ bulk CMOS technology. The low power realization of the proposed design and its higher accuracy, compared with well-known state-of-the-art methods, make it suitable for wearable devices.