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Motion artifact reduction in photoplethysmography utilizing empirical mode decomposition method

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Motion artifact reduction in photoplethysmography utilizing empirical mode decomposition method

  1. 1. Motion Artifact Reduction In Photoplethysmography Utilizing Empirical Mode Decomposition Method Wang Qian Institute of biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Key Laboratory of Biomedical information and Health Engineering. D.Y. Che, Institute of biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Key Laboratory of Biomedical information and Health Engineering. Y. T Zhang. is with the Joint Research Center for Biomedical Engineering, Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong. CAS/CUHK Research centre for Biosensors and Medical Instruments
  2. 2. <ul><li>The artifact reduction procedure consists of three major steps,The experience data </li></ul><ul><li>generated from the finger random motion in the BIOPAC MP150 system. </li></ul><ul><li>The first step is that the EMD method yielded seven IMF components as shown in Fig.2 (a). </li></ul><ul><li>The second step is that the instantaneous frequencies of these IMF components obtain using the Hilbert transform are given in Fig.2 (b). The relationship efficient respectively is ①0.3813,②0.7843,③0.1046,④0.0346,⑤0.0071,⑥0.0058 from the first IMF component to the seventh IMF component, in which the second component is the most value, thus indicating that the second component is the clean PPG signal and the others components are the noises. </li></ul><ul><li>The three componentscorrespond to the heartbeat, PPG signal and harmonic signal,respectively. Component 4 is assumed to be the ongoing motion artifact, which clearly reflects the non-stationary nature of finger moving. This can be further confirmed by scrutinizing the frequency content of each component, The peak frequencies of components 1, 2, 3 and 4 are successively 1.02 Hz, 0.2 Hz, 0.1 Hz and 0.05 Hz, which correspond to the heartbeat, PPG signal, harmonic signal and motion artifact, respectively. For comparison, the extracted PPG signal is components 2. So the final step is to extract the PPG signal (component 2). </li></ul>CAS/CUHK Research centre for Biosensors and Medical Instruments Photoplethysmography Extraction using Empirical Mode Decomposition
  3. 3. CAS/CUHK Research centre for Biosensors and Medical Instruments (a) seven IMF components of simulated PPG by EMD method;
  4. 4. CAS/CUHK Research centre for Biosensors and Medical Instruments (b) instantaneous frequencies of IMF components in (a).
  5. 5. CAS/CUHK Research centre for Biosensors and Medical Instruments (c) extracted clean PPG using EMD method and band-pass filter
  6. 6. Lauterbur Biomedical Imaging Center For the PPG signal is not generated from a stationary, randomly rescaled linear Gaussian noise [3], the EMD method is will suited to processing of the PPG especially with the motion artifact. The signal-to-noise is 6.2 dB after processing compared to 1.7 dB before processing, and the peak detection rate rises from 38.3% to 86.9%. Qualitative analysis of a larger number of signals shows that the algorithm appears to exhibit sound effects. However, the problem still exist, there is much room for improvement in the motion artifact reduction in PPG using the EMD method. Thank you Results

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