Journal
Scientific and Technical Journal of Information Technologies, Mechanics and Optics
UDK004.8
Issue:2 (166)
Review of deep learning methods for imaging photoplethysmography data processing
Annotation
This paper presents a review of contemporary deep learning methods for processing remote photoplethysmography data. Architectures of convolutional neural networks, transformers, recurrent, and generative models are examined for video signal preprocessing and for extracting physiologically significant parameters under conditions involving artifacts caused by motion, illumination changes, or low video quality. An analysis of the prospects for implementing deep learning algorithms in real-world medical scenarios is conducted based on the proposed criteria, considering existing integration challenges, the demand for such solutions, and issues related to result validation. The study includes a review of existing deep learning approaches that utilize video signals to estimate imaging photoplethysmography. The methods are evaluated using newly proposed criteria, including the multidimensionality of the photoplethysmography output signal, the availability of open-source code, and the reporting of computational time costs, which is essential for their practical real-time application in medical institutions. It is shown that deep learning methods significantly outperform traditional approaches in physiological parameter estimation, cardiovascular disease diagnosis, and video signal preprocessing. However, most existing deep learning-based solutions are limited to one-dimensional output signals due to the complexity of obtaining multidimensional annotations required for supervised learning. Additional analysis revealed a lack of information regarding temporal and computational costs, which restricts the practical real-time implementation of these methods. The proposed systematization clarifies key terms related to photoplethysmography signal processing: contact photoplethysmography, imaging photoplethysmography, remote photoplethysmography, and photoplethysmographic imaging. Approaches to dataset collection are also described, considering the concepts of multidimensionality, multichannel, and multimodal signals. The results may be applied in the development of remote health monitoring systems, including medical and consumer devices. The review will be of interest to specialists in biomedical engineering, medical informatics, and developers of physiological signal analysis solutions.
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