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Updated: May 13, 2026
07:37
Assessment and Communication for People with Disorders of Consciousness
Published on: August 1, 2017
Respiratory Variability in Disorders of Consciousness: Relationship and Clinical Applications
Yongli Wu1, Huaping Pan2, Hui Feng3
1Department of Rehabilitation Medicine, The First People's Hospital of Foshan (Foshan Hospital Affiliated to Southern University of Science and Technology), School of Medicine, Southern University of Science and Technology, Foshan, China.
CNS Neuroscience & Therapeutics
|May 12, 2026
Summary
Respiratory variability (RV) shows promise as a biomarker for consciousness. Machine learning, particularly random forest, accurately predicts outcomes in patients with disorders of consciousness (DOC).
Area of Science:
- Neuroscience
- Physiology
- Medical Technology
Background:
- Respiratory variability (RV) is modulated by the central nervous system and may indicate consciousness levels.
- The predictive value of RV in disorders of consciousness (DOC) requires further investigation.
Purpose of the Study:
- To explore the link between RV and consciousness levels in DOC patients.
- To assess RV's potential for predicting clinical outcomes in DOC.
Main Methods:
- Inertial measurement unit (IMU) sensors recorded breathing patterns in DOC patients and controls.
- Respiratory waveforms were analyzed for RV indicators.
- Machine learning models, including random forest, predicted Coma Recovery Scale-Revised (CRS-R) scores and outcomes.
Main Results:
- Significant RV differences were found between DOC patients and controls (p < 0.05).
- The random forest model demonstrated superior prediction for CRS-R scores (R²=0.76) and clinical outcomes (AUC=0.74).
Conclusions:
- RV analysis, especially with random forest, offers potential for prognostic assessment in DOC.
- RV may serve as a non-invasive biomarker for neural control of respiration and consciousness.
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