Abstract
During the pulse signal acquisition, corruptions would be inevitably introduced such as high-frequency noise, baseline drift, saturation, and artifact. Some of the corrupted pulse signals can be recovered via preprocessing, but several types of corrupted pulse signals would be difficult to recover and should be removed from the pulse signal dataset. Therefore, low-quality pulse signal detection plays an important role in computational pulse diagnosis especially in the real-time pulse monitoring. In this work, we focus on the detection of two common pulse corruption types, i.e., saturation and artifact. For the detection of saturation, we use two criteria from its definition. For the artifact detection, we transform the pulse signal into a complex network and detect the artifact by measuring the connectivity of the network. The experimental results show that the saturation and artifact detection method can both achieve better detection accuracy and better time resolution.
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Zhang, D., Zuo, W., Wang, P. (2018). Detection of Saturation and Artifact. In: Computational Pulse Signal Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-10-4044-3_5
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DOI: https://doi.org/10.1007/978-981-10-4044-3_5
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