A Robust Beat-to-Beat Artifact Detection Algorithm for Pulse Wave

Qihan Hu, Xintao Deng, Xin Liu, Aiguo Wang and Cuiwei Yang

Mathematical Problems in Engineering, 2020, vol. 2020, 1-8

Abstract:

With the rise of the concept of smart cities and healthcare, artificial intelligence helps people pay increasing attention to the health of themselves. People can wear a variety of wearable devices to monitor their physiological conditions. The pulse wave is a kind of physiological signal which is widely applied in the physiological monitoring system. However, the pulse wave is susceptible to artifacts, which prevents its popularization. In this work, we propose a novel beat-to-beat artifact detection algorithm, which performs pulse wave segmentation based on wavelet transform and then detects artifacts beat by beat based on the decision list. We verified our method on data acquired from different databases and compared with experts’ annotations. The segmentation algorithm achieved an accuracy of 96.13%. When it is applied to detect main peaks, the performance achieved an accuracy of 99.11%. After the previous segmentation algorithm, the artifact detection algorithm can detect beat-to-beat pulse waves and artifacts with an accuracy of 98.11%. The result indicated that the proposed method is robust for pulse waves of different patterns and could effectively detect the artifact without the complex algorithm. In summary, our proposed algorithm is capable of annotating pulse waves of various patterns and determining pulse wave quality. Since our method is developed and evaluated on the transmission-mode PPG data, it is more suitable for the devices and applications inside the hospitals instead of reflectance-mode PPG.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:5691805

DOI: 10.1155/2020/5691805

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