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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.