Integration Design of Portable ECG Signal Acquisition With Deep-Learning Based Electrode Motion Artifact Removal on an Embedded System

Abstract

For long-term electrocardiogram (ECG) signal monitoring, a portable and small size acquisition device with Bluetooth low energy (BLE) communication is designed and integrated with a Nvidia Jetson Xavier NX for realizing the electrode motion artifact removal technique. The digitalized ECG codes are converted from a front-end circuit, which contains several amplifiers and filters in the acquisition system. Thereafter, a zero padding scheme is applied for each 10-bits data to separate them into two-bytes data for BLE transmission. Xavier Edge AI platform receives these transmitted data and removes the electrode motion (EM) noise using the proposed low memory shortcut connection-based denoised autoencoder (LMSC-DAE). The simulation results demonstrate that the proposed algorithm significantly improves the signal-to-noise ratio (SNR) by 5.41 dB under the condition of SNRin = 12 dB, compared with convolutional denoising autoencoder with long short-term memory (CNN-LSTM-DAE) method. For practical test, an Arduino DUE platform is employed to generate noise interference by controlling a commercial digital-to-analog convertor. By combining the proposed ECG acquisition device with a non-inverting weighted summer, it can be applied to verify the reproducibility of measurement for the proposed method. The measurement results clearly indicate that the proposed LMSC-DAE has a higher improvement of SNR and lower percentage root-mean-square difference than the state-of-the-art Fully Convolutional Denoising Autoencoder (FCN-DAE).

Keywords

• Electrocardiogram (ECG)
• ECG signal enhancement
• embedded system
• deep learning
• denoising autoencoder (DAE)