An Energy-Efficient Configurable Coprocessor Based on 1-D CNN for ECG Anomaly Detection

Many healthcare devices have been widely used for electrocardiogram (ECG) monitoring. However, most of them have relatively low energy efficiency and lack flexibility. A novel ECG coprocessor is proposed in this paper, which can perform efficient ECG anomaly detection. In order to achieve high sensitivity and positive precision of R-peak detection, an algorithm based on Hilbert transform and adaptive threshold comparison is proposed. Also, a flexible one-dimensional convolutional neural network (1-D CNN) based classification engine is adopted, which can be configured with instructions to process various network models for different applications. Good energy efficiency is achieved by combining filter level parallelism and output channel parallelism within the processing element (PE) array with data reuse strategy. A 1-D CNN for arrhythmia detection is proposed to validate the hardware performance. The proposed ECG coprocessor is implemented using 55 nm CMOS technology, occupying an area of 1.39 mm
²
. At a clock frequency of 100MHz, the energy efficiency is 215.6 nJ/classification. The comparison results show that this design has advantages in energy overhead and detection performance.