Liuzzi Piergiuseppe, Grippo Antonello, Draghi Francesca, Hakiki Bahia, Macchi Claudio, Cecchi Francesca, Mannini Andrea
IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Firenze, Italy.
Istituto di BioRobotica, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.
Diagnostics (Basel). 2023 Jan 30;13(3):507. doi: 10.3390/diagnostics13030507.
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.
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.
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.
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.
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.
自主神经系统（ANS）活动，如心脏、呼吸和皮肤电活动，已被证明能提供有关不同意识状态的特定信息。呼吸频率（RRs）被视为ANS活动的指标，呼吸模式目前已被纳入重症监护患者的评估中。
这项工作的目的是通过RR变异性得出自主功能的替代指标，并将其诊断能力与已知的意识神经生理生物标志物进行比较。
在一组急性后期康复期间患有脑损伤的亚急性患者中，收集了多导记录（心电图、脑电图）。根据美国临床神经生理学会的术语，通过描述符对脑电图进行标记，并通过计算心电图衍生呼吸信号的近似熵（ApEN）来提取呼吸变异性。应用竞争逻辑回归来评估RR ApEN对模型性能的改善。
与仅使用脑电图描述符构建的模型相比，更高的RR复杂性与更高的意识水平显著相关，并改善了诊断模型的性能。
在多模式意识评估方案中加入基于仪器的RR变异性定量复杂性测量，可能会提高仅基于脑电图描述符的诊断准确性。总体而言，本研究促进了中枢和自主神经系统衍生的生物标志物的整合，以便在康复环境中对意识进行最全面的诊断。
