ECG Reconstruction Based on Improved Deep Convolutional Generative Adversarial Networks

ZHAO Yaqin; SUN Ruirui; WU Longwen; NIE Yuting; HE Shengyang

doi:10.11999/JEIT210922

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 59-69

ZHAO Yaqin, SUN Ruirui, WU Longwen, NIE Yuting, HE Shengyang. ECG Reconstruction Based on Improved Deep Convolutional Generative Adversarial Networks[J]. Journal of Electronics & Information Technology, 2022, 44(1): 59-69. doi: 10.11999/JEIT210922

Citation:

ZHAO Yaqin, SUN Ruirui, WU Longwen, NIE Yuting, HE Shengyang. ECG Reconstruction Based on Improved Deep Convolutional Generative Adversarial Networks[J]. Journal of Electronics & Information Technology, 2022, 44(1): 59-69. doi: 10.11999/JEIT210922

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ECG Reconstruction Based on Improved Deep Convolutional Generative Adversarial Networks

doi: 10.11999/JEIT210922

1.
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
2.
Beijing Research Institute, Huawei Technology Limited Company, Beijing 100095, China

Funds: The National Natural Science Foundation of China (61671185, 62071153)

Received Date: 2021-09-01
Accepted Date: 2021-12-22
Rev Recd Date: 2021-12-21

Available Online: 2021-12-29

Publish Date: 2022-01-10

Abstract

Abstract

BallistoCardioGraphy (BCG) signal contains physiological parameters during sleep for example heartbeat. It is measured by non-contact method, therefore its application is limited due to interference. ElectroCardioGram (ECG) signals are widely used, but it is difficult to operate using contact measure. In order to realize the non-contact measurement and monitoring of ECG signals, this paper introduces the Parameterless Scale space Approach (PSA) and improves the Empirical Wavelet Transform (EWT) algorithm to decompose the heartbeat component from BCG signal. The results show that the proposed method can effectively decompose the heartbeat signal from BCG signal to the maximum extent. On this basis, ECG signals are reconstructed by improved Deep Convolutional Generative Adversarial Networks (DCGAN). The results show that the ECG signals can be reconstructed from heartbeat components by the proposed algorithm, and the root mean square error is –16.8422 dB.
- BallistoCardioGraphy (BCG),
- ElectroCardioGram (ECG),
- Generative Adversarial Nets (GAN),
- Empirical wavelet transform,
- Sleep analysis

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References(25)

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