Model-Based Assessment of Photoplethysmogram Signal Quality in Real-Life Environments
Su, Y.-W.; Hao, C.-C.; Liu, G.-R.; Sheu, Y.-C.; Wu, H.-T.
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AO_SCPLOWBSTRACTC_SCPLOWAssessing signal quality is crucial for photoplethysmogram analysis, yet a precise mathematical model for defining signal quality is often lacking, posing challenges in the quantitative analysis. To tackle this problem, we propose a Signal Quality Index (SQI) based on the adaptive non-harmonic model (ANHM) and a Signal Quality Assessment (SQA) model, which is trained using the boosting learning algorithm. The effectiveness of the proposed SQA model is tested on publicly available databases with experts annotations. Result: The DaLiA database [20] is used to train the SQA model, which achieves favorable accuracy and macro-F1 scores in other public databases (accuracy 0.83, 0.76 and 0.87 and macro-F1 0.81, 0.75 and 0.87 for DaLiA-testing dataset, TROIKA dataset [31], and WESAD dataset [23], respectively). This preliminary result shows that the ANHM model and the model-based SQI have potential for establishing an interpretable SQA system.
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