ECG Reconstruction Based on Improved Deep Convolutional Generative Adversarial Networks
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摘要: 心冲击图(BCG)信号中含有睡眠时期的心跳等生理参数，采用非接触式测量，但易受干扰影响应用受限；心电图(ECG)信号应用很广，但采用接触式测量，操作不便。为了实现非接触式测量并监测心电信号，该文将无参数尺度空间法(PSA)引入并与经验小波变换(EWT)算法结合，从BCG信号中分解得到心跳分量，结果表明所提分解方法能有效地从BCG信号中最大限度地分解出心跳信号；并在此基础上通过改进的深度卷积生成对抗网络(DCGAN)重构出ECG信号。实验结果表明，该文所提信号重构算法能从心跳分量重构恢复出ECG信号，均方根误差为–16.8422 dB。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.
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表 1 不同分解方法下的心跳分量评价指标
方法 EMD VMD 小波 PSA-EWT 相关系数 0.1594 0.1187 0.3947 0.6590 峭度 3.5634 3.3030 3.4048 15.3154 查全率 0.0476 0.0000 0.5526 0.9512 查准率 0.0476 0.0000 0.8750 1.0000 
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表 2 不同方法下ECG信号重构结果对比
GAN DCGAN 改进DCGAN 相关系数 0.9788 0.9135 0.9885 均方根误差(dB) –15.5248 –12.1443 –16.8422
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