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

Autores

  • 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

Palavras-chave:

Quality Assessment, Photoplethysmography, Deep Learning

Resumo

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.

Biografia do Autor

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

Referências

A Quality Assessment System for PPG Waveform. Hao, Jiang, and Gao Bo. 2021, 2021 IEEE 3rd International Conference on Circuits and Systems (ICCS).

Optimal signal quality index for photoplethysmogram signals. Elgendi, Mohamed. 2016, Bioengineering 3.4, p. 21.

Extending the battery lifetime of wearable sensors with embedded machine learning. Fafoutis, X., Marchegiani, L., Elsts, A., Pope, J., Piechocki, R., & Craddock. 2018, IEEE 4th World Forum on Internet of Things (WF-IoT).

A survey of convolutional neural networks: analysis, applications, and prospects. Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. 2021, IEEE transactions on neural networks and learning systems.

1-D convolutional neural networks for signal processing applications. Kiranyaz, S., Ince, T., Abdeljaber, O., Avci, O., & Gabbouj, M. 2019, ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Keras-Spiking. www.nengo.ai/keras-spiking. [Online]

Cross-validation. Refaeilzadeh, Payam, Lei Tang, and Huan Liu. 2009, Encyclopedia of database systems 5, pp. 532-538.

Accelerated visual context classification on a low-power smartwatch. Conti, F., Palossi, D., Andri, R., Magno, M., & Benini, L. 2016, IEEE Transactions on Human-Machine Systems 47.1, pp. 19-30.

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.

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Publicado

20-07-2023

Como Citar

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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