Abstract
In this work, discrete wavelet transform was used to remove the effect of motion artifact on the Photoplethysmogram (PPG) signal obtained at fingertip. Clean PPG signal and motion data (one direction) were collected from 40 healthy volunteers at 14-bit resolution using NI 6009 DAQ card, and synthetic noisy signal was generated by addition. The noisy signal was first decomposed into a specific number of levels to obtain different frequency bands. Then, soft thresholding method was used to remove the noisy components. Different wavelet functions (Daubechies, Symlet, Coiflet) and soft thresholding methods (‘rigrsure,’ ‘heursure,’ ‘sqtwolog,’ etc.) were used to denoise the corrupted PPG signal. A comparative study was made between all of these methods by calculating performance measures such as signal-to-noise ratio improvement, mean square error, and percentage noise retention. The mother wavelet ‘Db6’ and ‘rigrsure’ soft thresholding method showed the best result.
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Biswas, A., Singha Roy, M., Gupta, R. (2019). Motion Artifact Reduction from Finger Photoplethysmogram Using Discrete Wavelet Transform. In: Bhattacharyya, S., Mukherjee, A., Bhaumik, H., Das, S., Yoshida, K. (eds) Recent Trends in Signal and Image Processing. Advances in Intelligent Systems and Computing, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-8863-6_10
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DOI: https://doi.org/10.1007/978-981-10-8863-6_10
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