Predicting and Understanding Disorders of Consciousness of Sleep Patients Through Multimodal Data Fusion and Temporal Attention Mechanisms

Predicting and Understanding Disorders of Consciousness of Sleep Patients Through Multimodal Data Fusion and Temporal Attention Mechanisms

This study introduces MTAMA-DoC (Multimodal Temporal Attention Multitask Analyzer for Disorders of Consciousness), a novel deep learning approach that leverages sleep-related physiological data to predict consciousness states in patients with Disorders of Consciousness (DoC). Employing a transformer...

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Bibliographic Details
Main Authors: Haixia Pan, Haotian Geng, Pingshu Zhang, Xiaodong Yuan
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Disorders of consciousness
multimodal deep learning
sleep physiology
time series
consciousness assessment
Online Access:https://ieeexplore.ieee.org/document/10943168/
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https://ieeexplore.ieee.org/document/10943168/

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