The Generalized Sleep Spindles Detector: A Generative Model Approach on Single-Channel EEGs

Carlos A. Loza, Jose C. Principe

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

1 Scopus citations

Abstract

We propose a data-driven, unsupervised learning framework for one of the hallmarks of stage 2 sleep in the electroencephalogram (EEG)—sleep spindles. Neurophysiological principles and clustering of time series subsequences constitute the underpinnings of methods fully based on a generative latent variable model for single-channel EEG. Learning on the model results in representations that characterize families of sleep spindles. The discriminative embedding transform separates potential micro-events from ongoing background activity. Then, a hierarchical clustering framework exploits Minimum Description Length (MDL) encoding principles to effectively partition the time series into patterns belonging to clusters of different dimensions. The proposed algorithm has only one main hyperparameter due to online model selection and the flexibility provided by cross-correlation operators. Methods are validated on the DREAMS Sleep Spindles database with results that echo previous approaches and clinical findings. Moreover, the learned representations provide a rich parameter space for further applications such as sparse encoding, inference, detection, diagnosis, and modeling.

Original languageEnglish
Title of host publicationAdvances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings
EditorsGonzalo Joya, Ignacio Rojas, Andreu Catala
PublisherSpringer Verlag
Pages127-138
Number of pages12
ISBN (Print)9783030205201
DOIs
StatePublished - 2019
Event15th International Work-Conference on Artificial Neural Networks, IWANN 2019 - Gran Canaria, Spain
Duration: 12 Jun 201914 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11506 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Work-Conference on Artificial Neural Networks, IWANN 2019
Country/TerritorySpain
CityGran Canaria
Period12/06/1914/06/19

Keywords

  • EEG
  • Generative model
  • Representation learning
  • Sleep spindles

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