Evaluation of Different Machine Learning Models for Photoplethysmogram Signal Artifact Detection | IEEE Conference Publication | IEEE Xplore

Evaluation of Different Machine Learning Models for Photoplethysmogram Signal Artifact Detection


Abstract:

Photoplethysmography (PPG) is a convenient as well as a simple method to detect the change in blood volume level. It is recently in wide use for noninvasive measurement u...Show More

Abstract:

Photoplethysmography (PPG) is a convenient as well as a simple method to detect the change in blood volume level. It is recently in wide use for noninvasive measurement using optical technique. But PPG signals are very sensitive to various artifacts. These artifacts impact measurement accuracy in negative way which can provide a significant number of inaccurate diagnoses. Thus in this paper, we propose to build a system to detect PPG signal artifacts of the MIMIC database and divide them into two classes, one is acceptable and another is anomalous. Different machine learning algorithms were applied to see the classification accuracy. Among them, Random Forest (RF) performed the best with the accuracy ± standard deviation of 84.00 ± 2.89%.
Date of Conference: 21-23 October 2020
Date Added to IEEE Xplore: 21 December 2020
ISBN Information:
Print on Demand(PoD) ISSN: 2162-1233
Conference Location: Jeju, Korea (South)

References

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