Signal Quality Assessment of Photoplethysmogram Signals Using Hybrid Rule- and Learning-Based Models
DOI:
https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1080Keywords:
Quality Assessment, Photoplethysmography, Deep LearningAbstract
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
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