Subject-invariant feature learning for mTBI identification using LSTM-based variational autoencoder with adversarial regularization

Subject-invariant feature learning for mTBI identification using LSTM-based variational autoencoder with adversarial regularization

Developing models for identifying mild traumatic brain injury (mTBI) has often been challenging due to large variations in data from subjects, resulting in difficulties for the mTBI-identification models to generalize to data from unseen subjects. To tackle this problem, we present a long short-term...

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Bibliographic Details
Main Authors: Shiva Salsabilian, Laleh Najafizadeh
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-11-01
Series:Frontiers in Signal Processing
Subjects:
variational autoencoder (VAE)
adversarial regularization
cross-subject transfer learning
mild TBI (mTBI)
LSTM-long short-term memory
Online Access:https://www.frontiersin.org/articles/10.3389/frsip.2022.1019253/full

Internet

https://www.frontiersin.org/articles/10.3389/frsip.2022.1019253/full

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