Deep Neural Dynamic Bayesian Networks Applied to EEG Sleep Spindles Modeling

Carlos A. Loza, Laura L. Colgin

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

Resumen

We propose a generative model for single–channel EEG that incorporates the constraints experts actively enforce during visual scoring. The framework takes the form of a dynamic Bayesian network with depth in both the latent variables and the observation likelihoods—while the hidden variables control the durations, state transitions, and robustness, the observation architectures parameterize Normal–Gamma distributions. The resulting model allows for time series segmentation into local, reoccurring dynamical regimes by exploiting probabilistic models and deep learning. Unlike typical detectors, our model takes the raw data (up to resampling) without pre–processing (e.g., filtering, windowing, thresholding) or post–processing (e.g., event merging). This not only makes the model appealing to real–time applications, but it also yields interpretable hyperparameters that are analogous to known clinical criteria. We derive algorithms for exact, tractable inference as a special case of Generalized Expectation Maximization via dynamic programming and backpropagation. We validate the model on three public datasets and provide support that more complex models are able to surpass state–of–the–art detectors while being transparent, auditable, and generalizable.

Idioma originalInglés
Título de la publicación alojadaMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
EditoresMarleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas550-560
Número de páginas11
ISBN (versión impresa)9783030872397
DOI
EstadoPublicada - 2021
Publicado de forma externa
Evento24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duración: 27 sep. 20211 oct. 2021

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12905 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CiudadVirtual, Online
Período27/09/211/10/21

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