Enabling on-device classification of ECG with compressed learning for health IoT

Abstract

In this paper, an on-device classification of electrocardiography (ECG) with Compressed Learning (CL) for health Internet of Things (IoT) is proposed. A CL algorithm combining with one-dimensional (1-D) Convolutional Neural Network (CNN) that directly learns on ECG signals in the compression domain without expanded normalization is proposed. Such an approach bypasses the reconstruction step and minimizes the raw input data dimension that significantly reduces the processing power. An automatic network optimization framework with Automatic Machine Learning (AutoML) tool Neural Network Intelligence (NNI) is suggested to adapt to the network structure search problem introduced by input dimension reduction with Compression Ratios (CRs). That ensures the minimized model size and operation number under a guaranteed classification accuracy. To implement the resized 1-D CL classifier in hardware, which has different kernel sizes, strides, and output channels under different CRs, a flexible architecture is proposed to further lower power consumption. Evaluated on the MIT-BIH database, the specific 1-D CNN network selected under CR =0.2 achieves a Macro-F1 of 0.9214 on 5-class ECG signal classification, with a 6.4× reduction in FLOPs and a 2.6× decrease in model size with an only 0.028 loss in Macro-F1 compared with the uncompressed situation. Synthesized in UMC 40 nm Low Power process, the hardware architecture with the 1-D CL classifier achieves an energy efficiency of 0.83 μJ/Classification under a 1.1-V power supply at a frequency of 5 MHz.