Personalized Sleep State Classification via Learned Factor Graphs

Bar Rubinstein, Yoav Filin, Nir Shlezinger, Nariman Farsad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Recent years have witnessed a growing interest in using deep learning for sleep state tracking. A key challenge in doing so stems from the variability between different patients, and a model trained using the data of several patients may perform poorly on another subject. In this work, we study mechanisms for achieving personalized sleep state tracking, which can be utilized with various deep neural network (DNN) architectures. Our design uses learned factor graphs to exploit temporal correlation in a principled manner. Inspired by recent advances in federated learning, we incorporate schemes based on data and model interpolation for achieving personalized models. Our experimental study demonstrates that this approach achieves accurate classification with compact DNNs.
Original languageEnglish
Title of host publication30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
ISBN (Electronic)9789082797091
StatePublished - 18 Oct 2022
Event30th European Signal Processing Conference, EUSIPCO 2022 - Belgrade, Serbia
Duration: 29 Aug 20222 Sep 2022

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference30th European Signal Processing Conference, EUSIPCO 2022


  • Sleep state monitoring
  • deep learning
  • signal processing

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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