An Energy-Efficient Configurable 1-D CNN-Based Multi-Lead ECG Classification Coprocessor for Wearable Cardiac Monitoring Devices

Abstract

Many electrocardiogram (ECG) processors have been widely used for cardiac monitoring. However, most of them have relatively low energy efficiency, and lack configurability in classification leads number and inference algorithm models. A multi-lead 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, a method based on zero-crossing slope adaptive threshold comparison is proposed. Also, a one-dimensional convolutional neural network (1-D CNN) based classification engine with reconfigurable processing elements (PEs) is designed, good energy efficiency is achieved by combining filter level parallelism and output channel parallelism within the PE chains with register level data reuse strategy. To improve configurability, a single instruction multiple data (SIMD) based central controller is adopted, which facilitates ECG classification with configurable number of leads and updatable inference models. The proposed ECG coprocessor is fabricated using 55 nm CMOS technology, supporting classification with an accuracy of over 98%. The test results indicate that the chip consumes 62.2 nJ at 100 MHz, which is lower than most recent works. The energy efficiency reaches 397.1 GOPS/W, achieving an improvement of over 40% compared to the reported ECG processors using CNN models. The comparison results show that this design has advantages in energy overhead and configurability.