Differentiation of autonomic nervous activity in different stages of coma displayed by power spectrum analysis of heart rate variability

Pain Perception in Disorder of Consciousness: A Scoping Review on Current Knowledge, Clinical Applications, and Future Perspective

Pain perception in individuals with prolonged disorders of consciousness (PDOC) is still a matter of debate. Advanced neuroimaging studies suggest some cortical activations even in patients with unresponsive wakefulness syndrome (UWS) compared to those with a minimally conscious state (MCS). Therefore, pain perception has to be considered even in individuals with UWS. However, advanced neuroimaging assessment can be challenging to conduct, and its findings are sometimes difficult to be interpreted. Conversely, multichannel electroencephalography (EEG) and laser-evoked potentials (LEPs) can be carried out quickly and are more adaptable to the clinical needs. In this scoping review, we dealt with the neurophysiological basis underpinning pain in PDOC, pointing out how pain perception assessment in these individuals might help in reducing the misdiagnosis rate. The available literature data suggest that patients with UWS show a more severe functional connectivity breakdown among the pain-related brain areas compared to individuals in MCS, pointing out that pain perception increases with the level of consciousness. However, there are noteworthy exceptions, because some UWS patients show pain-related cortical activations that partially overlap those observed in MCS individuals. This suggests that some patients with UWS may have residual brain functional connectivity supporting the somatosensory, affective, and cognitive aspects of pain processing (i.e., a conscious experience of the unpleasantness of pain), rather than only being able to show autonomic responses to potentially harmful stimuli. Therefore, the significance of the neurophysiological approach to pain perception in PDOC seems to be clear, and despite some methodological caveats (including intensity of stimulation, multimodal paradigms, and active vs. passive stimulation protocols), remain to be solved. To summarize, an accurate clinical and neurophysiological assessment should always be performed for a better understanding of pain perception neurophysiological underpinnings, a more precise differential diagnosis at the level of individual cases as well as group comparisons, and patient-tailored management.

Utilizing heart rate variability to predict ICU patient outcome in traumatic brain injury

Background

Prediction of patient outcome in medical intensive care units (ICU) may help for development and investigation of early interventional strategies. Several ICU scoring systems have been developed and are used to predict clinical outcome of ICU patients. These scores are calculated from clinical physiological and biochemical characteristics of patients. Heart rate variability (HRV) is a correlate of cardiac autonomic regulation and has been evident as a marker of poor clinical prognosis. HRV can be measured from the electrocardiogram non-invasively and monitored in real time. HRV has been identified as a promising ‘electronic biomarker’ of disease severity. Traumatic brain injury (TBI) is a subset of critically ill patients admitted to ICU, with significant morbidity and mortality, and often difficult to predict outcomes. Changes of HRV for brain injured patients have been reported in several studies. This study aimed to utilize the continuous HRV collection from admission across the first 24 h in the ICU in severe TBI patients to develop a patient outcome prediction system.

Results

A feature extraction strategy was applied to measure the HRV fluctuation during time. A prediction model was developed based on HRV measures with a genetic algorithm for feature selection. The result (AUC: 0.77) was compared with earlier reported scoring systems (highest AUC: 0.76), encouraging further development and practical application.

Conclusions

The prediction models built with different feature sets indicated that HRV based parameters may help predict brain injury patient outcome better than the previously adopted illness severity scores.

The Trace Conditional Learning of the Noxious Stimulus in UWS Patients and Its Prognostic Value in a GSR and HRV Entropy Study

The assessment of the consciousness level of Unresponsive Wakefulness Syndrome (UWS) patients often depends on a subjective interpretation of the observed spontaneous and volitional behavior. To date, the misdiagnosis level is around 30%. The aim of this study was to observe the behavior of UWS patients, during the administration of noxious stimulation by a Trace Conditioning protocol, assessed by the Galvanic Skin Response (GSR) and Heart Rate Variability (HRV) entropy. We recruited 13 Healthy Control (HC) and 30 UWS patients at 31 ± 9 days from the acute event evaluated by Coma Recovery Scale–Revised (CRS-R) and Nociception Coma Scale (NCS). Two different stimuli [musical stimulus (MUS) and nociceptive stimulus (NOC)], preceded, respectively by two different tones, were administered following the sequences (A) MUS1 – NOC1 – MUS2 – MUS3 – NOC2 – MUS4 – NOC3 – NOC^*, and (B) MUS1^*, NOC1^*, NOC2^*, MUS2^*, NOC3^*, MUS3^*, NOC4^*, MUS4^*. All the (^*) indicate the only tones administration. CRS-R and NCS assessments were repeated for three consecutive weeks. MUS4, NOC3, and NOC^* were compared for GSR wave peak magnitude, time to reach the peak, and time of wave's decay by Wilcoxon's test to assess the Conditioned Response (CR). The Sample Entropy (SampEn) was recorded in baseline and both sequences. Machine Learning approach was used to identify a rule to discriminate the CR. The GSR magnitude of CR was higher comparing music stimulus (p < 0.0001) and CR extinction (p < 0.002) in nine patients and in HC. Patients with CR showed a higher SampEn in sequence A compared to patients without CR. Within the third and fourth weeks from protocol administration, eight of the nine patients (88.9%) evolved into MCS. The Machine-learning showed a high performance to differentiate presence/absence of CR (≥95%). The possibility to observe the CR to the noxious stimulus, by means of the GSR and SampEn, can represent a potential method to reduce the misdiagnosis in UWS patients.

EEG and ANS markers of attention response in vegetative state: Different responses to own vs. other names

Covert measures of information-processing are valuable tools to support assessment of patients' disorders of consciousness because of their potential in revealing what seem to be hidden. Those measures allow to overcome some limitations of traditional behavioural methods, which are often biased by difficulties in detecting reliable patients' responses. Therefore, we aimed at exploring patterns of psychophysiological responses (electroencephalography - EEG, skin conductance level - SCL, skin conductance response - SCR, heart rate - HR) marking potentially-preserved processing of personally-relevant stimuli in a sample of VS patients. In particular, we compared the processing of own vs. other names due to the intrinsic salience, relevance, and familiarity of such stimuli. Analysis of electroencephalography, skin conductance and heart rate modulations highlighted a consistent pattern of increased skin conductance and heart rate measures in response to patients' own name with respect to other names. Further, we observed increased delta and decreased alpha activity over frontal areas in response to their own name with respect to other names. Own-name stimuli might preserve their peculiar qualification even after severe brain damage and might call on residual attention orientation and preferred coding resources, suggesting the existence of partly preserved information-processing pathways that extends beyond basic auditory sensory processing.

Autonomic impairment of patients in coma with different Glasgow coma score assessed with heart rate variability

PRIMARY OBJECTIVE

The objective of this study is to assess the functional state of the autonomic nervous system in healthy individuals and in individuals in coma using measures of heart rate variability (HRV) and to evaluate its efficiency in predicting mortality.

DESIGN AND METHODS

Retrospective group comparison study of patients in coma classified into two subgroups, according to their Glasgow coma score, with a healthy control group. HRV indices were calculated from 7 min of artefact-free electrocardiograms using the Hilbert-Huang method in the spectral range 0.02-0.6 Hz. A special procedure was applied to avoid confounding factors. Stepwise multiple regression logistic analysis (SMLRA) and ROC analysis evaluated predictions.

RESULTS

Progressive reduction of HRV was confirmed and was associated with deepening of coma and a mortality score model that included three spectral HRV indices of absolute power values of very low, low and very high frequency bands (0.4-0.6 Hz). The SMLRA model showed sensitivity of 95.65%, specificity of 95.83%, positive predictive value of 95.65%, and overall efficiency of 95.74%.

CONCLUSIONS

HRV is a reliable method to assess the integrity of the neural control of the caudal brainstem centres on the hearts of patients in coma and to predict patient mortality.

EEG and autonomic responses to nociceptive stimulation in disorders of consciousness

Since behavioral responses to external stimuli of patients presenting disorders of consciousness (DoC) are often difficult to qualify, covert physiological correlates of responsivity are deemed as potentially valuable tools to help assessment procedures. While noxious stimuli seem good candidates to explore DoC patients' responsivity, autonomic and electrophysiological correlates of pain detection in DoC patients are still debated. This research aims at investigating autonomic and cortical activation as covert measure of residual somatosensory and nociceptive processes in patients in vegetative state. Twenty-one patients received touch- and pain-related stimulations while autonomic and cortical measures were recorded, with minimal stress for them. Results showed an increased frontal and parietal activation in response to both touch and pain stimuli. Pain-related stimulation was however associated with greater delta parietal response, lower left frontal activation, and increased electrodermal and heart rate measures. Present findings suggest that both somatic stimulations could induce measurable central responses, which might mirror basic attention orientation and perceptual processes. Nonetheless, the nociceptive stimulation in particular seemed to induce a more consistent and informative pattern of covert response even if we used a mild pain-induction procedure.

Using demographic and time series physiological features to classify sepsis in the intensive care unit

Sepsis, a systemic inflammatory response to infection, is a major health care problem that affects millions of patients every year in the intensive care units (ICUs) worldwide. Despite the fact that ICU patients are heavily instrumented with physiological sensors, early sepsis detection remains challenging, perhaps because clinicians identify sepsis by (i) using static scores derived from bed-side measurements individually, and (ii) deriving these scores at a much slower rate than the rate for which patient data is collected. In this study, we construct a generalized linear model (GLM) for the probability that an ICU patient has sepsis as a function of demographics and bedside measurements. Specifically, models were trained on 29 patient recordings from the MIMIC II database and evaluated on a different test set including 8 patient recordings. A classification accuracy of 62.5% was achieved using demographic measures as features. Adding physiological time series features to the model increased the classification accuracy to 75%. Although very preliminary, these results suggest that using generalized linear models incorporating real time physiological signals may be useful for an early detection of sepsis, thereby improving the chances of a successful treatment.

Continuous electrodermal activity as a potential novel neurophysiological biomarker of prognosis after cardiac arrest--A pilot study

AIMS

Neurological outcome prognosis remains challenging in patients undergoing therapeutic hypothermia (TH) after cardiac resuscitation. Technological advances allow for a novel wrist-worn device to continuously record electrodermal activity (EDA), a measure of pure sympathetic activity.

METHODS

A prospective cohort study was performed to determine the yield of continuous EDA in patients treated with TH for coma after cardiac arrest during hypothermia and normothermia. Association between EDA parameters (event-related and nonspecific electrodermal responses (ER-EDR, NS-EDR)) and outcome measures (cerebral performance category [CPC]) (Full Outline in UnResponsivenss (FOUR) score) were assessed.

RESULTS

Eighteen patients were enrolled. Total number of EDR (66.4 vs 12.0/24h, p = 0.02), ER-EDR (39.5 vs 11.2/24h, p = 0.009), median amplitude change of all EDR (0.08 vs 0.03 μSI, p = 0.03) and ER-EDR (0.14 vs 0.05 μSI, p = 0.025) were higher in patients with favorable (CPC 1-2) versus poor outcome (CPC 3-5) during hypothermia. Greater differences in EDA parameters were observed during hypothermia than normothermia. The FOUR score was correlated to the number of all EDR and median amplitudes.

CONCLUSIONS

Continuous EDA potentially opens a new avenue for autonomic function monitoring in neurocritically ill patients. It is feasible in the ICU setting, even during hypothermic states. As a measure of a complete neurophysiological circuit, it may be a novel neurophysiologic biomarker of outcome after cardiac resuscitation.

Quantification of clinical scores through physiological recordings in low-responsive patients: a feasibility study

Clinical scores represent the gold standard in characterizing the clinical condition of patients in vegetative or minimally conscious state. However, they suffer from problems of sensitivity, specificity, subjectivity and inter-rater reliability.In this feasibility study, objective measures including physiological and neurophysiological signals are used to quantify the clinical state of 13 low-responsive patients. A linear regression method was applied in nine patients to obtain fixed regression coefficients for the description of the clinical state. The statistical model was extended and evaluated with four patients of another hospital. A linear mixed models approach was introduced to handle the challenges of data sets obtained from different locations.Using linear backward regression 12 variables were sufficient to explain 74.4% of the variability in the change of the clinical scores. Variables based on event-related potentials and electrocardiogram account for most of the variability.These preliminary results are promising considering that this is the first attempt to describe the clinical state of low-responsive patients in such a global and quantitative way. This new model could complement the clinical scores based on objective measurements in order to increase diagnostic reliability. Nevertheless, more patients are necessary to prove the conclusions of a statistical model with 12 variables.

Detecting conscious awareness from involuntary autonomic responses

Can conscious awareness be ascertained from physiological responses alone? We evaluate a novel learning-based procedure permitting detection of conscious awareness without reliance on language comprehension or behavioural responses. The method exploits a situation whereby only consciously detected violations of an expectation alter skin conductance responses (SCRs). Thirty participants listened to sequences of piano notes that, without their being told, predicted a pleasant fanfare or an aversive noise according to an abstract rule. Stimuli were presented without distraction (attended), or while distracted by a visual task to remove awareness of the rule (unattended). A test phase included occasional violations of the rule. Only participants attending the sounds reported awareness of violations and only they showed significantly greater SCR for noise occurring in violation, vs. accordance, with the rule. Our results establish theoretically significant dissociations between conscious and unconscious processing and furnish new opportunities for clinical assessment of residual consciousness in patient populations.

Non-invasive classification of severe sepsis and systemic inflammatory response syndrome using a nonlinear support vector machine: a preliminary study

Sepsis has been defined as the systemic response to infection in critically ill patients, with severe sepsis and septic shock representing increasingly severe stages of the same disease. Based on the non-invasive cardiovascular spectrum analysis, this paper presents a pilot study on the potential use of the nonlinear support vector machine (SVM) in the classification of the sepsis continuum into severe sepsis and systemic inflammatory response syndrome (SIRS) groups. 28 consecutive eligible patients attending the emergency department with presumptive diagnoses of sepsis syndrome have participated in this study. Through principal component analysis (PCA), the first three principal components were used to construct the SVM feature space. The SVM classifier with a fourth-order polynomial kernel was found to have a better overall performance compared with the other SVM classifiers, showing the following classification results: sensitivity = 94.44%, specificity = 62.50%, positive predictive value = 85.00%, negative predictive value = 83.33% and accuracy = 84.62%. Our classification results suggested that the combinatory use of cardiovascular spectrum analysis and the proposed SVM classification of autonomic neural activity is a potentially useful clinical tool to classify the sepsis continuum into two distinct pathological groups of varying sepsis severity.

Personal Interaction in the Vegetative State

Background and purpose: Brain processing at varying levels of functional complexity and emotional reactions to relatives are anecdotally reported by the caregivers of patients in a vegetative state. In this study, computer-assisted machine-learning procedures were applied to identify heart rate variability changes or galvanic skin responses to a relative’s presence. Methods: The skin conductance (galvanic skin response) and heart beats were continuously recorded in 12 patients in a vegetative state, at rest (baseline) and while approached by a relative (usually the mother; test condition) or by a nonfamiliar person (control condition). The cardiotachogram (the series of consecutive intervals between heart beats) was analyzed in the time and frequency domains by computing the parametric and nonparametric frequency spectra. A machine-learning algorithm was applied to sort out the significant spectral parameter(s). For all patients, each condition (baseline, test, control) was characterized by the values of its spectral parameters, and the association between spectral parameters values and experimental condition was tested (WEKA machine-learning software). Results and comments: A galvanic skin response was obtained in two patients. The machine-learning procedure independently selected the nu_LF spectral parameter and attributed each nu_LF measure to any of the three experimental conditions. 69.4% of attributions were correct (baseline: 58%; test condition: 75%; control. 75%). In seven patients, attribution changed when the subject was approached by the test person; specifically, sequential shifts from baseline to test condition (“the Mom effect”) to control condition were identified in four patients (30.0%); the change from test to control was attributed correctly in seven patients (58%). The observation of heart rate changes tentatively attributable to emotional reaction in a vegetative state suggest residual rudimentary personal interaction, consistent with functioning limbic and paralimbic systems after massive brain damage. Machine-learning proved applicable to sort significant measure(s) out of large samples and to control for statistical alpha inflation.

Autonomic reactivity to sensory stimulation is related to consciousness level after severe traumatic brain injury

OBJECTIVE

To examine changes in the activity of the autonomic nervous system (ANS) that are related to recovery to consciousness in the post-acute phase after severe traumatic brain injury (sTBI).

METHODS

Skin conductance and heart rate reactivity to sensory stimulation were recorded every 2 weeks for an average period of 3.5 months in 16 adolescent patients, during the assessment of their level of consciousness (LoC), and their cognitive and functional behaviour.

RESULTS

Both heart rate variability (HRV) and skin conductance level (SCL) in reaction to sensory stimulation changed with recovery to consciousness. Indices of HRV and SCL that represent sympathetic activity of the autonomic nervous system (ANS) increased with recovery, whereas indices that represent parasympathetic activity decreased. In addition, we observed an increase in sympathovagal balance of the ANS with recovery.

CONCLUSIONS

Recovery to consciousness determined by clinical observation in sTBI in the post-acute phase is related to changes in SCL and HRV during sensory stimulation. ANS reactivity to environmental stimulation can therefore give objective supplementary information about the clinical state of sTBI patients, and can contribute to decision-making in the treatment policy of unresponsive patients.

SIGNIFICANCE

These findings demonstrate that autonomic reactivity can be informative concerning how a severely damaged nervous system reacts to environmental stimulation and how, in a recovering nervous system, this reactivity changes.

Neuropsychologische Frührehabilitation

Zusammenfassung: Die neurologische Frührehabilitation hat im letzten Jahrzehnt stark an Bedeutung gewonnen. In diesem Artikel wird ein neuropsychologisches Behandlungskonzept für Patienten mit schwersten Bewusstseinsstörungen (Wachkoma, akinetischer Mutismus, stuporartige Antriebsstörung und Somnolenz) zur Diskussion gestellt. Schwerpunkt des Konzepts ist eine Konzentration auf die Handlungsebene und eine Interpretation der Syndrome als schwere Form der Negativsymptomatik, während die übliche Einordnung als Aufmerksamkeitsstörung in Frage gestellt wird. Die für die einzelnen Syndrome vorhandene Literatur zu neuroanatomischen Ursachen, zu neuropsychologischen Modellvorstellungen und zu funktionellen bzw. pharmakologischen Behandlungsansätzen wird jeweils kurz dargestellt und es werden daraus spezifische neuropsychologische Therapievorschläge abgeleitet. Aus der Analyse folgt, dass die Neuropsychologie einen wesentlichen Beitrag zur Behandlung dieser schwerst beeinträchtigten Patienten der Phase B der neurologischen Rehabilitation leisten könnte, bis heute aber kaum empirische Daten über die Wirksamkeit vorliegen.

Could heart rate variability predict outcome in patients with severe head injury? A pilot study

Despite major improvements in the resuscitation of patients with head injury, the outcome of patients with head trauma often remains poor and difficult to establish. Heart rate variability (HRV) analysis is a noninvasive tool used to measure autonomic nervous system (ANS) activity. The aim of this prospective study was to investigate whether HRV analysis might be a useful adjunct for predicting outcome in patients with severe head injury. Twenty patients with severe head trauma (Glasgow Coma Scale [GCS] <or= 8) underwent 24-hour electrocardiogram recording 1 day after trauma and again 48 hours after withdrawal of sedative drugs. Heart rate variability was assessed, in both time domain and spectral domain. The authors initially compared (on Day 1) HRV in patients who progressed to brain death to HRV in survivors; then during the awakening period compared HRV in surviving patients with good recovery (GCS >or= 10) to HRV in patients characterized by a worsened neurologic state (GCS < 10). Statistical analysis used the Kruskal-Wallis test, P < .05. To assess whether HRV could predict evolution to brain death, receiver operating characteristic (ROC) curves were generated the day after trauma for Total Power, natural logarithm of high-frequency component of spectral analysis (LnHF), natural logarithm of low-frequency component of spectral analysis (LnLF), and root mean square for successive interval differences (rMSSD). Seven patients died between Day 1 and Day 5 after trauma. Six of those had progressed to brain death. In these six patients, at Day 1, Global HRV and parasympathetic tone were significantly higher. Referring to the area under the rMSSD ROC curve, HRV might provide useful information in predicting early evolution of patients with severe head trauma. During the awakening period, global HRV and the parasympathetic tone were significantly lower in the worsened neurologic state group. In conclusion, HRV could be helpful as a predictor of imminent brain death and a useful adjunct for predicting the outcome of patients with severe head injury.