Abstract
Since wrist pulse signals collected by the sensors are often corrupted by artifacts in real situations, many approaches on the wrist pulse preprocessing including pulse denoising and baseline drift removal are introduced for more accurate wrist pulse analysis. However, these scattered methods are incomplete with some limitations when used to preprocess our special pulse data for the clinical applications. This chapter presents a robust signal preprocessing framework for wrist pulse analysis. The cascade filter based on frequency-dependent analysis (FDA) is first introduced to remove the high-frequency noises and to select the significant intervals. Then the curve fitting method is developed to adjust the direction and the baseline drift with minimum signal distortion. Last, the period segmentation and normalization is applied for the feature extraction. The effectiveness of the proposed framework is validated through experiments on actual pulse records with biochemical markers. Both quantitative and qualitative results are given. The results show that the proposed pulse preprocessing framework is effective in extracting more accurate pulse features and practical for wrist pulse analysis.
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Zhang, D., Zuo, W., Wang, P. (2018). Optimized Preprocessing Framework for Wrist Pulse Analysis. In: Computational Pulse Signal Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-10-4044-3_6
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DOI: https://doi.org/10.1007/978-981-10-4044-3_6
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