Removing ECG Noise from Surface EMG Based On Information Theory
| Autor: | Jaber Parchami, Ali Darroudi, Sreeraman Rajan, Ghazaleh Sarbishaei |
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| Rok vydání: | 2018 |
| Předmět: |
Adaptive algorithm
Mean squared error Computer science Gaussian Noise reduction Order statistic Data_CODINGANDINFORMATIONTHEORY 030229 sport sciences 02 engineering and technology Information theory Adaptive filter 03 medical and health sciences symbols.namesake 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering symbols Entropy (information theory) 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | Electrical Engineering (ICEE), Iranian Conference on. |
| Popis: | An adaptive filtering method is presented which eliminates ECG artifact from EMG signals based on error entropy criterion. In this method, the error distribution is estimated and minimized in an adaptive manner. Mean squared error (MSE) criterion only minimizes 2nd order statistics of the error, so it is sufficient in cases where inherent noise is Gaussian. The error entropy (EE) criterion, used in the proposed algorithm, minimizes all moments of error distribution. So in EMG denoising, where ECG artifact is typically non-Gaussian, Minimum Error Entropy (MEE)-based adaptive algorithm will improve noise elimination performance. Simulation results show that proposed algorithm has better spectral coherence in low frequencies and improves the SNR of the denoised EMG signal (about 5dB), especially in low SNR inputs, compared to MSE based algorithms. |
| Databáze: | OpenAIRE |
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