Abstract
Background: Disorders of consciousness are challenging to diagnose, with inconsistent behavioral responses, motor and cognitive disabilities, leading to approximately 40% misdiagnoses. Heart rate variability (HRV) reflects the complexity of the heart-brain two-way dynamic interactions. HRV entropy analysis quantifies the unpredictability and complexity of the heart rate beats intervals. We here investigate the complexity index (CI), a score of HRV complexity by aggregating the non-linear multi-scale entropies over a range of time scales, and its discriminative power in chronic patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), and its relation to brain functional connectivity. Methods: We investigated the CI in short (CIs) and long (CIl) time scales in 14 UWS and 16 MCS sedated. CI for MCS and UWS groups were compared using a Mann-Whitney exact test. Spearman's correlation tests were conducted between the Coma Recovery Scale-revised (CRS-R) and both CI. Discriminative power of both CI was assessed with One-R machine learning model. Correlation between CI and brain connectivity (detected with functional magnetic resonance imagery using seed-based and hypothesis-free intrinsic connectivity) was investigated using a linear regression in a subgroup of 10 UWS and 11 MCS patients with sufficient image quality. Results: Higher CIs and CIl values were observed in MCS compared to UWS. Positive correlations were found between CRS-R and both CI. The One-R classifier selected CIl as the best discriminator between UWS and MCS with 90% accuracy, 7% false positive and 13% false negative rates after a 10-fold cross-validation test. Positive correlations were observed between both CI and the recovery of functional connectivity of brain areas belonging to the central autonomic networks (CAN). Conclusion: CI of MCS compared to UWS patients has high discriminative power and low false negative rate at one third of the estimated human assessors' misdiagnosis, providing an easy, inexpensive and non-invasive diagnostic tool. CI reflects functional connectivity changes in the CAN, suggesting that CI can provide an indirect way to screen and monitor connectivity changes in this neural system. Future studies should assess the extent of CI's predictive power in a larger cohort of patients and prognostic power in acute patients.
Highlights
Disorders of consciousness are a spectrum of pathologies affecting one’s ability to interact with the external world
In the S1 group, when comparing the complexity index (CI) values of minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) patients, higher values of CIs (z = −3.346, p < 0.001) and of complexity index in the long term (CIl) (z = −4.095, p < 0.0001) were observed for the MCS group compared to the UWS group (Figures 2, 3)
Superimposable results were obtained in the 10-fold cross-validation test (Table 2), with an accuracy of 90% and a correct MCS and UWS classification of 88 and 93% respectively
Summary
Disorders of consciousness are a spectrum of pathologies affecting one’s ability to interact with the external world. The clinical characterization of disorders of consciousness can be more reliably assessed using designed scales such as the Coma Recovery Scale-Revised (CRS-R) [14], practicing them requires a specific training of the physicians and, lower, might still induce diagnosis errors inherent to any behavior-based clinical assessment due to the patient’s possible inability to respond [13]. These assessments rely on observing the patient’s motor actions, and their absence. We here investigate the complexity index (CI), a score of HRV complexity by aggregating the non-linear multi-scale entropies over a range of time scales, and its discriminative power in chronic patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), and its relation to brain functional connectivity
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