Signal Quality Assessment of Photoplethysmogram Signals Using Hybrid Rule- and Learning-Based Models

Authors

  • Giovani Decico Lucafó Samsung R&D Institute
  • Pedro Freitas Samsung R&D Institute
  • Rafael Lima Samsung R&D Institute
  • Gustavo da Luz Samsung R&D Institute
  • Ruan Bispo Samsung R&D Institute
  • Paula Rodrigues Samsung R&D Institute
  • Frank Cabello Samsung R&D Institute
  • Otavio Penatti Samsung R&D Institute

DOI:

https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1080

Keywords:

Quality Assessment, Photoplethysmography, Deep Learning

Abstract

Photoplethysmography signals are crucial for a wide range of applications and, therefore, high-quality PPG signals are crucial to describe the cardiorespiratory status accurately. Motion artifacts can impair PPG-based applications, especially when these signals are recorded via wearable devices. Taking that in consideration, some researchers had proposed few methods for assessing the quality of these signals. Some rule- and learning-based approaches for PPG signal are available to determine the quality of the signal. In this paper, we propose a tradeoff between these two approaches by introducing a hybrid model that employs both learning and decision rules to determine the quality of the signal.

Author Biographies

Pedro Freitas, Samsung R&D Institute

Samsung R&D Institute

Rafael Lima, Samsung R&D Institute

Samsung R&D Institute 

Gustavo da Luz, Samsung R&D Institute

Samsung R&D Institute

Ruan Bispo, Samsung R&D Institute

Samsung R&D Institute

Paula Rodrigues, Samsung R&D Institute

Samsung R&D Institute

Frank Cabello, Samsung R&D Institute

Samsung R&D Institute

Otavio Penatti, Samsung R&D Institute

Samsung R&D Institute

References

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A novel method for accurate estimation of HRV from smartwatch PPG signals. Bhowmik, Tanmoy, Jishnu Dey, and Vijay Narayan Tiwari. 2017, 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp. 109-112.

Published

2023-07-20

How to Cite

Lucafó, G. D., Freitas, P., Lima, R., Luz, G. da, Bispo, R., Rodrigues, P., … Penatti, O. (2023). Signal Quality Assessment of Photoplethysmogram Signals Using Hybrid Rule- and Learning-Based Models. Journal of Health Informatics, 15(Especial). https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1080

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