Abstract
Though many literatures on pulse feature extraction have been published, they just handle the pulse signals as simple 1-D time series and ignore the information within the class. This chapter presents a generalized method of pulse feature extraction, extending the feature dimension from 1-D time series to 2-D matrix. The conventional wrist pulse features correspond to a particular case of the generalized models. The proposed method is validated through pattern classification on actual pulse records. Both quantitative and qualitative results relative to the 1-D pulse features are given through diabetes diagnosis. The experimental results show that the generalized 2-D matrix feature is effective in extracting both the periodic and nonperiodic information. And it is practical for wrist pulse analysis.
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Zhang, D., Zuo, W., Wang, P. (2018). Generalized Feature Extraction for Wrist Pulse Analysis: From 1-D Time Series to 2-D Matrix. In: Computational Pulse Signal Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-10-4044-3_9
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DOI: https://doi.org/10.1007/978-981-10-4044-3_9
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