SR-ScatNet Algorithm for On-device ECG Time Series Anomaly Detection

Abstract

Anomaly detection of real-time ECG time series is of particular interest for early detection of cardiovascular disease for aging population. To use convolutional neural networks (CNN) for any on-device training or inference, you need GPU-accelerated hardware which will not only increase the hardware cost but also consume higher battery power. We proposed a SR-ScatNet algorithm for on-device application such as smart cloth with ECG monitoring sensors. Two improvements were made. First on spectral residual, we use Fourier Transform of autocorrelation of ECG signals instead of original time series to increase the sensitivity. Second on feature extraction, we use shallow wavelet scattering network (ScatNet) instead of deep CNN network so the on-device training can be performed on a simple Arm Cortex-A53 processor without any GPU-accelerator. These improvements are made to create a compact machine learning model according to the nature of different waves constituting the ECG signals. To verify the proposed method, we use the MIT-BIH Arrhythmia Database. The spectral residual of autocorrelation ECG signals can detect the abnormal ECG signals with over 98% accuracy. The wavelet scattering network can further classify the type of abnormality with over 90% accuracy. We believe the design of ECG monitoring smart cloth can benefit from such SR-ScatNet algorithm.