Characterization of interdependency between intracranial pressure and heart variability signals: a causal spectral measure and a generalized synchronization measure

Using near-infrared spectroscopy and a random forest regressor to estimate intracranial pressure

SIGNIFICANCE

Intracranial pressure (ICP) measurements are important for patient treatment but are invasive and prone to complications. Noninvasive ICP monitoring methods exist, but they suffer from poor accuracy, lack of generalizability, or high cost.

AIM

We previously showed that cerebral blood flow (CBF) cardiac waveforms measured with diffuse correlation spectroscopy can be used for noninvasive ICP monitoring. Here we extend the approach to cardiac waveforms measured with near-infrared spectroscopy (NIRS).

APPROACH

Changes in hemoglobin concentrations were measured in eight nonhuman primates, in addition to invasive ICP, arterial blood pressure, and CBF changes. Features of average cardiac waveforms in hemoglobin and CBF signals were used to train a random forest (RF) regressor.

RESULTS

The RF regressor achieves a cross-validated ICP estimation of 0.937 r 2 , 2.703 - mm Hg 2 mean squared error (MSE), and 95% confidence interval (CI) of [ - 3.064    3.160 ]    mmHg on oxyhemoglobin concentration changes; 0.946 r 2 , 2.301 - mmHg 2 MSE, and 95% CI of [ - 2.841    2.866 ]    mmHg on total hemoglobin concentration changes; and 0.963 r 2 , 1.688    mmHg 2 MSE, and 95% CI of [ - 2.450    2.397 ]    mmHg on CBF changes.

CONCLUSIONS

This study provides a proof of concept for the use of NIRS in noninvasive ICP estimation.

Intracranial Pressure Is a Determinant of Sympathetic Activity

Intracranial pressure (ICP) is the pressure within the cranium. ICP rise compresses brain vessels and reduces cerebral blood delivery. Massive ICP rise leads to cerebral ischemia, but it is also known to produce hypertension, bradycardia and respiratory irregularities due to a sympatho-adrenal mechanism termed Cushing response. One still unresolved question is whether the Cushing response is a non-synaptic acute brainstem ischemic mechanism or part of a larger physiological reflex for arterial blood pressure control and homeostasis regulation. We hypothesize that changes in ICP modulates sympathetic activity. Thus, modest ICP increase and decrease were achieved in mice and patients with respectively intra-ventricular and lumbar fluid infusion. Sympathetic activity was gauged directly by microneurography, recording renal sympathetic nerve activity in mice and muscle sympathetic nerve activity in patients, and gauged indirectly in both species by heart-rate variability analysis. In mice (n = 15), renal sympathetic activity increased from 29.9 ± 4.0 bursts.s⁻¹ (baseline ICP 6.6 ± 0.7 mmHg) to 45.7 ± 6.4 bursts.s⁻¹ (plateau ICP 38.6 ± 1.0 mmHg) and decreased to 34.8 ± 5.6 bursts.s⁻¹ (post-infusion ICP 9.1 ± 0.8 mmHg). In patients (n = 10), muscle sympathetic activity increased from 51.2 ± 2.5 bursts.min⁻¹ (baseline ICP 8.3 ± 1.0 mmHg) to 66.7 ± 2.9 bursts.min⁻¹ (plateau ICP 25 ± 0.3 mmHg) and decreased to 58.8 ± 2.6 bursts.min⁻¹ (post-infusion ICP 14.8 ± 0.9 mmHg). In patients 7 mmHg ICP rise significantly increases sympathetic activity by 17%. Heart-rate variability analysis demonstrated a significant vagal withdrawal during the ICP rise, in accordance with the microneurography findings. Mice and human results are alike. We demonstrate in animal and human that ICP is a reversible determinant of efferent sympathetic outflow, even at relatively low ICP levels. ICP is a biophysical stress related to the forces within the brain. But ICP has also to be considered as a physiological stressor, driving sympathetic activity. The results suggest a novel physiological ICP-mediated sympathetic modulation circuit and the existence of a possible intracranial (i.e., central) baroreflex. Modest ICP rise might participate to the pathophysiology of cardio-metabolic homeostasis imbalance with sympathetic over-activity, and to the pathogenesis of sympathetically-driven diseases.

Simultaneous Transients of Intracranial Pressure and Heart Rate in Traumatic Brain Injury: Methods of Analysis

OBJECTIVES

The detection of increasing intracranial pressure (ICP) is important in preventing secondary brain injuries. Before mean ICP increases critically, transient ICP elevations may be observed. We have observed ICP transients of less than 10 min duration ,which occurred simultaneously with transient increases in heart rate (HR). These simultaneous events in HR and ICP suggest a direct interaction or communication between the heart and the brain.

METHODS

This chapter describes four mathematical methods and their applicability in detecting the above heart-brain cross-talk events during long-term monitoring of ICP.

RESULTS

Recurrence plots, cross-correlation function and wavelet analysis confirmed the relationship between ICP and HR time series. Using the peaks detection algorithm with a sliding window approach we found an average of 37 cross-talk events (± SD 39). The number of events detected varied among patients, from 1 to more than 150 events.

CONCLUSION

Our analysis suggested that the peaks detection algorithm based on a sliding window approach is feasible for detecting simultaneous peaks, e.g. cross-talk events in the ICP and HR signals.

A multiplex network approach for the analysis of intracranial pressure and heart rate data in traumatic brain injured patients

BACKGROUND

We present a multiplex network model for the analysis of Intracranial Pressure (ICP) and Heart Rate (HR) behaviour after severe brain traumatic injuries in pediatric patients. The ICP monitoring is of vital importance for checking life threathening conditions, and understanding the behaviour of these parameters is crucial for a successful intervention of the clinician. Our own observations, exhibit cross-talks interaction events happening between HR and ICP, i.e. transients in which both the ICP and the HR showed an increase of 20% with respect to their baseline value in the window considered. We used a complex event processing methodology, to investigate the relationship between HR and ICP, after traumatic brain injuries (TBI). In particular our goal has been to analyse events of simultaneous increase by HR and ICP (i.e. cross-talks), modelling the two time series as a unique multiplex network system (Lacasa et al., Sci Rep 5:15508-15508, 2014).

METHODS AND DATA

We used a complex network approach based on visibility graphs (Lacasa et al., Sci Rep 5:15508-15508, 2014) to model and study the behaviour of our system and to investigate how and if network topological measures can give information on the possible detection of crosstalks events taking place in the system. Each time series was converted as a layer in a multiplex network. We therefore studied the network structure, focusing on the behaviour of the two time series in the cross-talks events windows detected. We used a dataset of 27 TBI pediatric patients, admitted to Addenbrooke's Hospital, Cambridge, Pediatric Intensive Care Unit (PICU) between August 2012 and December 2014.

RESULTS

Following a preliminary statistical exploration of the two time series of ICP and HR, we analysed the multiplex network proposed, focusing on two standard topological network metrics: the mutual interaction, and the average edge overlap (Lacasa et al., Sci Rep 5:15508-15508, 2014). We compared results obtained for these two indicators, considering windows in which a cross talks event between HR and ICP was detected with windows in which cross talks events were not present. The analysis of such metrics gave us interesting insights on the time series behaviour. More specifically we observed an increase in the value of the mutual interaction in the case of cross talk as compared to non cross talk. This seems to suggest that mutual interaction could be a potentially interesting "marker" for cross talks events.

Heart Rate Variability in Newborns

Heart rate (HR) and heart rate variability (HRV) in newborns is influenced by genetic determinants, gestational and postnatal age, and other variables. Premature infants have a reduced HRV. In neonatal HRV evaluated by spectral analysis, a dominant activity can be found in low frequency (LF) band (combined parasympathetic and sympathetic component). During the first postnatal days the activity in the high frequency (HF) band (parasympathetic component) rises, together with an increase in LF band and total HRV. Hypotrophy in newborn can cause less mature autonomic cardiac control with a higher contribution of sympathetic activity to HRV as demonstrated by sequence plot analysis. During quiet sleep (QS) in newborns HF oscillations increase – a phenomenon less expressed or missing in premature infants. In active sleep (AS), HRV is enhanced in contrast to reduced activity in HF band due to the rise of spectral activity in LF band. Comparison of the HR and HRV in newborns born by physiological vaginal delivery, without (VD) and with epidural anesthesia (EDA) and via sectio cesarea (SC) showed no significant differences in HR and in HRV time domain parameters. Analysis in the frequency domain revealed, that the lowest sympathetic activity in chronotropic cardiac chronotropic regulation is in the VD group. Different neonatal pathological states can be associated with a reduction of HRV and an improvement in the health conditions is followed by changes in HRV what can be use as a possible prognostic marker. Examination of heart rate variability in neonatology can provide information on the maturity of the cardiac chronotropic regulation in early postnatal life, on postnatal adaptation and in pathological conditions about the potential dysregulation of cardiac function in newborns, especially in preterm infants.

Early Asymmetric Cardio-Cerebral Causality and Outcome after Severe Traumatic Brain Injury

The brain and heart are two vital systems in health and disease, increasingly recognized as a complex, interdependent network with constant information flow in both directions. After severe traumatic brain injury (TBI), the causal, directed interactions between the brain, heart, and autonomic nervous system have not been well established. Novel methods are needed to probe unmeasured, potentially prognostic information in complex biological networks that are not revealed by traditional means. In this study, we examined potential bidirectional causality between intracranial pressure (ICP), mean arterial pressure (MAP), and heart rate (HR) and its relationship to mortality in a 24-h period early post-TBI. We applied Granger causality (GC) analysis to cardio-cerebral monitoring data from 171 severe TBI patients admitted to a single neurocritical care center over a 10-year period. There was significant bidirectional causality between ICP and MAP, MAP and HR, and ICP and HR in the majority of patients (p < 0.01). MAP influenced both ICP and HR to a greater extent (higher GC, p < 0. 00001), but there was no dominant unidirectional causality between ICP and HR (p = 0.85). Those who died had significantly lower GC for ICP causing MAP and HR causing ICP (p = 0.006 and p = 0.004, respectively) and were predictors of mortality independent of age, sex, and traditional intracranial variables (ICP, cerebral perfusion pressure, GCS, and pressure reactivity index). Examining the brain and heart with GC-based features for the first time in severe TBI patients has confirmed strong interdependence and reveals a significant relationship between select causality pairs and mortality. These results support the notion that impaired causal information flow between the cerebrovascular, autonomic, and cardiovascular systems are of central importance in severe TBI.

A new approach for investigating intracranial pressure signal: filtering and morphological features extraction from continuous recording

Nowadays, the Intracranial Pressure (ICP) monitoring has become the most common method of investigation for both traumatic and chronic neural pathologies. ICP signals are typically triphasic, that is, in a single waveform, three subpeaks can be identified. This work outlines a new algorithm to identify subpeaks from the ICP recordings and to extract a number of 20 meaningful parameter trends. The validity of the implemented method has been proved through a comparison between the automatic subpeaks identification by the algorithm and the manually marked subpeaks by a neurosurgeon. The automatic marking system has identified subpeaks for the 63.74% (mean value) of pulse waves, providing the position and amplitude of each identified subpeak within a tolerance of ±7 samples. This automatic system provides a feature set to be used by classification software to obtain more precise and easier diagnosis in all those cases that involve brain damages or diseases.

A systematic study of linear dynamic modeling of intracranial pressure dynamics

Our group has proposed a generic time series data mining framework and demonstrated its potential as a noninvasive intracranial pressure (ICP) assessment approach. The linear dynamic model (LDM) was used in our previous work without rigorous justification. In the current study, we performed a systematic study of the practical performance of the LDM for ICP dynamics by investigating three important aspects to consider in using the LDM to model ICP dynamics. Those three aspects include the fitness of the LDM to data, the generalizability of the models, and the choice of input signals to the models. Our study results show that the fitness of the LDM to data is excellent and the LDM for ICP dynamics is well generalizable, which is of particular interest to adopting our time series data mining framework for noninvasive ICP assessment.

INDEX OF CEREBROSPINAL COMPENSATORY RESERVE IN HYDROCEPHALUS

OBJECTIVE

An index of cerebrospinal compensatory reserve (RAP) has been introduced as a potential descriptor of neurological deterioration after head trauma. It is numerically computed as a linear correlation coefficient between the mean intracranial pressure and the pulse amplitude of the pressure waveform. We explore how RAP varies with different forms of physiological or nonphysiological intracranial volume loads in adult hydrocephalus, with and without a functioning cerebrospinal fluid (CSF) shunt.

METHODS

A database of intracranial pressure recordings during CSF infusion studies and overnight monitoring in hydrocephalic patients was reviewed for clinical comparison of homogeneous subgroups of patients with hypothetical differences of pressure-volume compensatory reserve. The database includes 980 patients of mixed etiology: idiopathic normal pressure hydrocephalus (NPH), 47%; postsubarachnoid hemorrhage NPH, 12%; noncommunicating hydrocephalus, 22%; others, 19%. All CSF compensatory parameters were calculated by using intracranial pressure waveforms.

RESULTS

In NPH, RAP correlated strongly with the resistance to CSF outflow (rs = 0.35; P = 0.045), but weakly correlated with ventriculomegaly (rs = 0.13; P = 0.41). In idiopathic nonshunted NPH patients, RAP did not correlate significantly with elasticity calculated from the CSF infusion test (rs = 0.11; P = 0.21). During infusion studies, RAP increased in comparison to values recorded at baseline (from a median of 0.45–0.86, P = 0.14 * 10−8), indicating a narrowing of the volume-pressure compensatory reserve. During B-waves associated with the REM (rapid eye movement) phase of sleep, RAP increased from a median of 0.53 to 0.89; P = 1.2 * 10−5. After shunting, RAP decreased (median before shunting, 0.59; median after shunting, 0.34; P = 0.0001). RAP also showed the ability to reflect the functional state of the shunt (patent shunt median, 0.36; blocked shunt median, 0.84; P = 0.0002).

CONCLUSION

RAP appears to characterize pressure-volume compensatory reserve in patients with hydrocephalus.

Morphological clustering and analysis of continuous intracranial pressure

The continuous measurement of intracranial pressure (ICP) is an important and established clinical tool that is used in the management of many neurosurgical disorders such as traumatic brain injury. Only mean ICP information is used currently in the prevailing clinical practice, ignoring the useful information in ICP pulse waveform that can be continuously acquired and is potentially useful for forecasting intracranial and cerebrovascular pathophysiological changes. The present study introduces and validates an algorithm of performing automated analysis of continuous ICP pulse waveform. This algorithm is capable of enhancing ICP signal quality, recognizing nonartifactual ICP pulses, and optimally designating the three well-established subcomponents in an ICP pulse. Validation of the proposed algorithm is done by comparing nonartifactual pulse recognition and peak designation results from a human observer with those from automated analysis based on a large signal database built from 700 h of recordings from 66 neurosurgical patients. An accuracy of 97.84% is achieved in recognizing nonartifactual ICP pulses. An accuracy of 90.17%, 87.56%, and 86.53% was obtained for designating each of the three established ICP subpeaks. These results show that the proposed algorithm can be reliably applied to process continuous ICP recordings from real clinical environment to extract useful morphological features of ICP pulses.

Adaptive computation of approximate entropy and its application in integrative analysis of irregularity of heart rate variability and intracranial pressure signals

The present study introduces an adaptive calculation of approximate entropy (ApEn) by exploiting sample-by-sample construction and update of nearest neighborhoods in an n-dimensional space. The algorithm is first validated with a standard numerical test set. It is then applied to electrocardiogram R wave interval (RR) and beat-to-beat intracranial pressure signals recorded from 12 patients undergoing normal pressure hydrocephalus diagnosis. The ApEn time series are further processed using the causal coherence analysis to study the interaction between ICP and RR interval. Numerical validation demonstrates that the proposed algorithm reproduces the known time-varying patterns in the test set and better tracks abrupt signal changes. It is also demonstrated that occurrences of large-amplitude ICP oscillation are associated with decreased ICP ApEn and RR ApEn for all 12 patients. The causal coherence analysis of ApEn time series shows that coherence between RR ApEn and ICP ApEn, after mathematically decoupling RR effect on ICP, is enhanced for the oscillatory ICP state and so is the amplitude of transfer function between ICP and RR interval. However, no enhanced coherence is observed after mathematically decoupling ICP effect on RR interval. In conclusion, the adaptive ApEn algorithm can be used to track nonstationary signal characteristics. Furthermore, interactions between dynamic systems could be studied by using ApEn time series of the direct observations of systems.