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

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 c...

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Vydané v:	IEEE access Ročník 10; s. 57555 - 57564
Hlavní autori:	Jhang, Yu-Syuan, Wang, Szu-Ting, Sheu, Ming-Hwa, Wang, Szu-Hong, Lai, Shin-Chi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptive filters Algorithms Artificial intelligence Bluetooth Circuits Convolution Deep learning denoising autoencoder (DAE) ECG signal enhancement Electrocardiogram (ECG) Electrocardiography Electrodes embedded system Embedded systems Machine learning Noise Noise generation Noise measurement Noise reduction Portable equipment Signal monitoring Signal to noise ratio
ISSN:	2169-3536, 2169-3536
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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 SNR in = 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).
AbstractList	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). 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 SNR in = 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).
Author	Sheu, Ming-Hwa Wang, Szu-Ting Jhang, Yu-Syuan Lai, Shin-Chi Wang, Szu-Hong
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SubjectTerms	Adaptive filters Algorithms Artificial intelligence Bluetooth Circuits Convolution Deep learning denoising autoencoder (DAE) ECG signal enhancement Electrocardiogram (ECG) Electrocardiography Electrodes embedded system Embedded systems Machine learning Noise Noise generation Noise measurement Noise reduction Portable equipment Signal monitoring Signal to noise ratio
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Title	Integration Design of Portable ECG Signal Acquisition With Deep-Learning Based Electrode Motion Artifact Removal on an Embedded System
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