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

Carlos A. Loza, Jose C. Principe

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaAdvances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings
EditoresGonzalo Joya, Ignacio Rojas, Andreu Catala
EditorialSpringer Verlag
Páginas127-138
Número de páginas12
ISBN (versión impresa)9783030205201
DOI
EstadoPublicada - 2019
Evento15th International Work-Conference on Artificial Neural Networks, IWANN 2019 - Gran Canaria, Espana
Duración: 12 jun. 201914 jun. 2019

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11506 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia15th International Work-Conference on Artificial Neural Networks, IWANN 2019
País/TerritorioEspana
CiudadGran Canaria
Período12/06/1914/06/19

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