Deep learning-based near-infrared spectroscopy time-series analysis with long short-term memory

Jingyi Wu; Shaojie Bai; Jana M. Kainerstorfer

doi:10.1117/12.2650749

7 March 2023 Deep learning-based near-infrared spectroscopy time-series analysis with long short-term memory

Jingyi Wu, Shaojie Bai, Jana M. Kainerstorfer

Proceedings Volume PC12376, Optical Tomography and Spectroscopy of Tissue XV; PC123760B (2023) https://doi.org/10.1117/12.2650749
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

NIRS measurement is known to be susceptible to motion artifact, instrumental noise, etc. When analyzing NIRS time series, we almost always lack the ground truth for evaluation, and from many methods, it could be hard to pick the most appropriate method for one’s application. In this work, we proposed and examined the following pipeline: First, generate ground truth synthetic NIRS signal. Second, add application specific noises/activations. Finally, train a Long Short-Term Memory deep learning model for time series prediction. Our results revealed that this approach can recover the uncorrupted NIRS/PPG signal accurately and efficiently from noisy signals.

Conference Presentation

Citation Download Citation

Jingyi Wu, Shaojie Bai, and Jana M. Kainerstorfer "Deep learning-based near-infrared spectroscopy time-series analysis with long short-term memory", Proc. SPIE PC12376, Optical Tomography and Spectroscopy of Tissue XV, PC123760B (7 March 2023); https://doi.org/10.1117/12.2650749

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