Key result
Higher respiration rate complexity was significantly associated with higher consciousness levels and improved diagnostic model performance compared to electroencephalographic descriptors alone.
Why the study?
Autonomic nervous system activity provides information on consciousness states, and this study aimed to derive an autonomic proxy via respiration rate variability and compare its diagnostic capability with known neurophysiological biomarkers of consciousness.
Does adding Approximate Entropy of ECG-derived respiration signal to EEG descriptors improve diagnostic accuracy for consciousness levels in sub-acute patients with brain injury?
Cohort
Does adding Approximate Entropy of ECG-derived respiration signal to EEG descriptors improve diagnostic accuracy for consciousness levels in sub-acute patients with brain injury?
Adding ECG-derived respiration rate variability complexity to EEG descriptors improves diagnostic accuracy for consciousness levels in brain injury rehabilitation.
May support RR variability as autonomic proxy in critical care; leaves open diagnostic utility for consciousness states without validation.
BACKGROUND: Autonomic Nervous System (ANS) activity, as cardiac, respiratory and electrodermal activity, has been shown to provide specific information on different consciousness states. Respiration rates (RRs) are considered indicators of ANS activity and breathing patterns are currently already included in the evaluation of patients in critical care. OBJECTIVE: The aim of this work was to derive a proxy of autonomic functions via the RR variability and compare its diagnostic capability with known neurophysiological biomarkers of consciousness. METHODS: In a cohort of sub-acute patients with brain injury during post-acute rehabilitation, polygraphy (ECG, EEG) recordings were collected. The EEG was labeled via descriptors based on American Clinical Neurophysiology Society terminology and the respiration variability was extracted by computing the Approximate Entropy (ApEN) of the ECG-derived respiration signal. Competing logistic regressions were applied to evaluate the improvement in model performances introduced by the RR ApEN. RESULTS: Higher RR complexity was significantly associated with higher consciousness levels and improved diagnostic models' performances in contrast to the ones built with only electroencephalographic descriptors. CONCLUSIONS: Adding a quantitative, instrumentally based complexity measure of RR variability to multimodal consciousness assessment protocols may improve diagnostic accuracy based only on electroencephalographic descriptors. Overall, this study promotes the integration of biomarkers derived from the central and the autonomous nervous system for the most comprehensive diagnosis of consciousness in a rehabilitation setting.
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Liuzzi et al. (2023) conducted a cohort in Disorders of consciousness after brain injury. Respiration rate variability complexity (Approximate Entropy) vs. Electroencephalographic descriptors alone was evaluated on Consciousness levels and diagnostic model performance. Higher respiration rate complexity was significantly associated with higher consciousness levels and improved diagnostic model performance compared to electroencephalographic descriptors alone.
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