Abstract: P. G. Freitas, R. G. De, G. D. Lucafo, O. A. Penatti (2023)

Proceedings of the 15th International Conference on Quality of Multimedia Experience (QoMEX 2023), 165–170p. (2023) DOI:10.1109/QoMEX58391.2023.10178569

Photoplethysmogram Signal Quality Assessment via 1D-to-2D Projections and Vision Transformers

Real-time health monitoring is revolutionizing healthcare delivery nowadays. Using everyday settings, especially due to the recent wearable health devices, it is possible to monitor individuals at any place and moment, allowing the detection and prevention of many diseases. Among the various technologies present in wearable devices that allow continuous health monitoring, Photoplethysmography (PPG) is one of the most important techniques. PPG is non-invasive, low-cost, easy-to-implement, and, therefore, convenient to track physiological signals, such as oxygen saturation in the bloodstream, heart rate variability, respiration rate, etc. Due to these advantages, PPG is widely used in diverse health applications, notably in commercial wearable apparatuses. However, despite its advantages, PPG presents a main drawback of being highly susceptible to motion artifacts and environmental noises, which impair PPG-based applications, especially when PPG signals are recorded via wearable devices. Therefore, to enable reliable measurements, signal quality must be assessed, and unreliable signals should be rejected. With such signal reliability needs, the most important thing is Signal Quality Assessment (SQA). In this paper, we introduce a novel SQA method that projects the 1D PPG signals into 2D images and then uses a Vision Transformer (ViT) to classify their quality. Results show that the proposed method presents a competitive quality prediction accuracy when compared with the state-of-the-art.

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