Signal Quality Assessment of Photoplethysmogram Signals Using Hybrid Rule- and Learning-Based Models
DOI:
https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1080Palavras-chave:
Quality Assessment, Photoplethysmography, Deep LearningResumo
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.
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Copyright (c) 2023 Giovani Decico Lucafó, Pedro Freitas, Rafael Lima, Gustavo da Luz, Ruan Bispo, Paula Rodrigues, Frank Cabello, Otavio Penatti
Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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