Interpretation of approximate entropy: analysis of intracranial pressure approximate entropy during acute intracranial hypertension

Usefulness of Artificial Intelligence in Traumatic Brain Injury: A Bibliometric Analysis and Mini-review

BACKGROUND

Traumatic brain injury (TBI) has become a major source of disability worldwide, increasing the interest in algorithms that use artificial intelligence (AI) to optimize the interpretation of imaging studies, prognosis estimation, and critical care issues. In this study we present a bibliometric analysis and mini-review on the main uses that have been developed for TBI in AI.

METHODS

The results informing this review come from a Scopus database search as of April 15, 2023. The bibliometric analysis was carried out via the mapping bibliographic metrics method. Knowledge mapping was made in the VOSviewer software (V1.6.18), analyzing the "link strength" of networks based on co-occurrence of key words, countries co-authorship, and co-cited authors. In the mini-review section, we highlight the main findings and contributions of the studies.

RESULTS

A total of 495 scientific publications were identified from 2000 to 2023, with 9262 citations published since 2013. Among the 160 journals identified, The Journal of Neurotrauma, Frontiers in Neurology, and PLOS ONE were those with the greatest number of publications. The most frequently co-occurring key words were: "machine learning", "deep learning", "magnetic resonance imaging", and "intracranial pressure". The United States accounted for more collaborations than any other country, followed by United Kingdom and China. Four co-citation author clusters were found, and the top 20 papers were divided into reviews and original articles.

CONCLUSIONS

AI has become a relevant research field in TBI during the last 20 years, demonstrating great potential in imaging, but a more modest performance for prognostic estimation and neuromonitoring.

Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms

OBJECTIVE

This study aimed to explore the potential of employing machine learning algorithms based on intracranial pressure (ICP), ICP-derived parameters, and their complexity to predict the severity and short-term prognosis of traumatic brain injury (TBI).

METHODS

A single-center prospectively collected cohort of neurosurgical intensive care unit admissions was analyzed. We extracted ICP-related data within the first 6 hours and processed them using complex algorithms. To indicate TBI severity and short-term prognosis, Glasgow Coma Scale score on the first postoperative day and Glasgow Outcome Scale-Extended score at discharge were used as binary outcome variables. A univariate logistic regression model was developed to predict TBI severity using only mean ICP values. Subsequently, 3 multivariate Random Forest (RF) models were constructed using different combinations of mean and complexity metrics of ICP-related data. To avoid overfitting, five-fold cross-validations were performed. Finally, the best-performing multivariate RF model was used to predict patients' discharge Glasgow Outcome Scale-Extended score.

RESULTS

The logistic regression model exhibited limited predictive ability with an area under the curve (AUC) of 0.558. Among multivariate models, the RF model, combining the mean and complexity metrics of ICP-related data, achieved the most robust ability with an AUC of 0.815. Finally, in terms of predicting discharge Glasgow Outcome Scale-Extended score, this model had a consistent performance with an AUC of 0.822. Cross-validation analysis confirmed the performance.

CONCLUSIONS

This study demonstrates the clinical utility of the RF model, which integrates the mean and complexity metrics of ICP data, in accurately predicting the TBI severity and short-term prognosis.

Permutation Entropy Analysis to Intracranial Hypertension from a Porcine Model

Intracranial pressure (ICP) monitoring is commonly used in the follow-up of patients in intensive care units, but only a small part of the information available in the ICP time series is exploited. One of the most important features to guide patient follow-up and treatment is intracranial compliance. We propose using permutation entropy (PE) as a method to extract non-obvious information from the ICP curve. We analyzed the results of a pig experiment with sliding windows of 3600 samples and 1000 displacement samples, and estimated their respective PEs, their associated probability distributions, and the number of missing patterns (NMP). We observed that the behavior of PE is inverse to that of ICP, in addition to the fact that NMP appears as a surrogate for intracranial compliance. In lesion-free periods, PE is usually greater than 0.3, and normalized NMP is less than 90% and p(s1)>p(s720). Any deviation from these values could be a possible warning of altered neurophysiology. In the terminal phases of the lesion, the normalized NMP is higher than 95%, and PE is not sensitive to changes in ICP and p(s720)>p(s1). The results show that it could be used for real-time patient monitoring or as input for a machine learning tool.

Slope Entropy Normalisation by Means of Analytical and Heuristic Reference Values

Slope Entropy (SlpEn) is a very recently proposed entropy calculation method. It is based on the differences between consecutive values in a time series and two new input thresholds to assign a symbol to each resulting difference interval. As the histogram normalisation value, SlpEn uses the actual number of unique patterns found instead of the theoretically expected value. This maximises the information captured by the method but, as a consequence, SlpEn results do not usually fall within the classical [0,1] interval. Although this interval is not necessary at all for time series classification purposes, it is a convenient and common reference framework when entropy analyses take place. This paper describes a method to keep SlpEn results within this interval, and improves the interpretability and comparability of this measure in a similar way as for other methods. It is based on a max-min normalisation scheme, described in two steps. First, an analytic normalisation is proposed using known but very conservative bounds. Afterwards, these bounds are refined using heuristics about the behaviour of the number of patterns found in deterministic and random time series. The results confirm the suitability of the approach proposed, using a mixture of the two methods.

Big data and predictive analytics in neurocritical care

PURPOSE OF REVIEW

To describe predictive data and workflow in the intensive care unit when managing neurologically ill patients.

RECENT FINDINGS

In the era of Big Data in medicine, intensive critical care units are data-rich environments. Neurocritical care adds another layer of data with advanced multimodal monitoring to prevent secondary brain injury from ischemia, tissue hypoxia, and a cascade of ongoing metabolic events. A step closer toward personalized medicine is the application of multimodal monitoring of cerebral hemodynamics, bran oxygenation, brain metabolism, and electrophysiologic indices, all of which have complex and dynamic interactions. These data are acquired and visualized using different tools and monitors facing multiple challenges toward the goal of the optimal decision support system. In this review, we highlight some of the predictive data used to diagnose, treat, and prognosticate the neurologically ill patients. We describe information management in neurocritical care units including data acquisition, wrangling, analysis, and visualization.

Patient-adaptable intracranial pressure morphology analysis using a probabilistic model-based approach

OBJECTIVE

We present a framework for analyzing the morphology of intracranial pressure (ICP). The analysis of ICP signals is challenging due to the non-linear and non-Gaussian characteristics of the signal dynamics, inevitable corruption by noise and artifacts, and variations in ICP pulse morphology among individuals with different neurological conditions. Existing frameworks make unrealistic assumptions regarding ICP dynamics and are not tuned for individual patients.

APPROACH

We propose a dynamic Bayesian network for automated detection of three major ICP pulsatile components. The proposed model captures the non-linear and non-Gaussian dynamics of ICP morphology and further adapts to a patient as the individual's ICP measurements are received. To make the approach more robust, we leverage evidence reversal and present an inference algorithm to obtain the posterior distribution over the locations of pulsatile components.

MAIN RESULTS

We evaluate our approach on a dataset with over 700 h of recordings from 66 neurological patients, where the pulsatile components were annotated by prior studies. The algorithm obtains accuracies of 96.56%, 92.39%, and 94.04% for the detection of each pulsatile component in the test set, showing significant improvement over existing approaches.

SIGNIFICANCE

Continuous ICP monitoring is essential in guiding the treatment of neurological conditions such as traumatic brain injuries. An automated approach for ICP morphology analysis is a step towards enhancing patient care with minimal supervision. Compared to previous methods, our framework offers several advantages. It learns the parameters that model each patient's ICP in an unsupervised manner, resulting in an accurate morphology analysis. The Bayesian model-based framework provides uncertainty estimates and reveals interesting facts about the ICP dynamics. The framework can readily be applied to replace existing morphological analysis methods and support the use of ICP pulse morphological features to aid the monitoring of pathophysiological changes of relevance to the care of patients with acute brain injuries.

Consistency of local activation parameters at sensor- and source-level in neural signals

OBJECTIVE

Although magnetoencephalography and electroencephalography (M/EEG) signals at sensor level are robust and reliable, they suffer from different degrees of distortion due to changes in brain tissue conductivities, known as field spread and volume conduction effects. To estimate original neural generators from M/EEG activity acquired at sensor level, diverse source localisation algorithms have been proposed; however, they are not exempt from limitations and usually involve time-consuming procedures. Connectivity and network-based M/EEG analyses have been found to be affected by field spread and volume conduction effects; nevertheless, the influence of the aforementioned effects on widely used local activation parameters has not been assessed yet. The goal of this study is to evaluate the consistency of various local activation parameters when they are computed at sensor- and source-level.

APPROACH

Six spectral (relative power, median frequency, and individual alpha frequency) and non-linear parameters (Lempel-Ziv complexity, sample entropy, and central tendency measure) are computed from M/EEG signals at sensor- and source-level using four source inversion methods: weighted minimum norm estimate (wMNE), standardised low-resolution brain electromagnetic tomography (sLORETA), linear constrained minimum variance (LCMV), and dynamical statistical parametric mapping (dSPM).

MAIN RESULTS

Our results show that the spectral and non-linear parameters yield similar results at sensor- and source-level, showing high correlation values between them for all the source inversion methods evaluated and both modalities of signal, EEG and MEG. Furthermore, the correlation values remain high when performing coarse-grained spatial analyses.

SIGNIFICANCE

To the best of our knowledge, this is the first study analysing how field spread and volume conduction effects impact on local activation parameters computed from resting-state neural activity. Our findings evidence that local activation parameters are robust against field spread and volume conduction effects and provide equivalent information at sensor- and source-level even when performing regional analyses.

Non-Invasive Analysis of Actinic Keratosis before and after Topical Treatment Using a Cold Stimulation and Near-Infrared Spectroscopy

Background and objectives: The possible evolution of actinic keratoses (AKs) into invasive squamous cell carcinomas (SCC) makes their treatment and monitoring essential. AKs are typically monitored before and after treatment only through a visual analysis, lacking a quantitative measure to determine treatment effectiveness. Near-infrared spectroscopy (NIRS) is a non-invasive measure of the relative change of oxy-hemoglobin and deoxy-hemoglobin (O₂Hb and HHb) in tissues. The aim of our study is to determine if a time and frequency analysis of the NIRS signals acquired from the skin lesion before and after a topical treatment can highlight quantitative differences between the AK skin lesion area. Materials and Methods: The NIRS signals were acquired from the skin lesions of twenty-two patients, with the same acquisition protocol: baseline signals, application of an ice pack near the lesion, removal of ice pack and acquisition of vascular recovery. We calculated 18 features from the NIRS signals, and we applied multivariate analysis of variance (MANOVA) to compare differences between the NIRS signals acquired before and after the therapy. Results: The MANOVA showed that the features computed on the NIRS signals before and after treatment could be considered as two statistically separate groups, after the ice pack removal. Conclusions: Overall, the NIRS technique with the cold stimulation may be useful to support non-invasive and quantitative lesion analysis and regression after a treatment. The results provide a baseline from which to further study skin lesions and the effects of various treatments.

Intracranial Pressure Monitoring Signals After Traumatic Brain Injury: A Narrative Overview and Conceptual Data Science Framework

Continuous intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care after severe brain injuries such as traumatic brain injury and acts as a biomarker of secondary brain injury. With the rapid development of artificial intelligent (AI) approaches to data analysis, the acquisition, storage, real-time analysis, and interpretation of physiological signal data can bring insights to the field of neurocritical care bioinformatics. We review the existing literature on the quantification and analysis of the ICP waveform and present an integrated framework to incorporate signal processing tools, advanced statistical methods, and machine learning techniques in order to comprehensively understand the ICP signal and its clinical importance. Our goals were to identify the strengths and pitfalls of existing methods for data cleaning, information extraction, and application. In particular, we describe the use of ICP signal analytics to detect intracranial hypertension and to predict both short-term intracranial hypertension and long-term clinical outcome. We provide a well-organized roadmap for future researchers based on existing literature and a computational approach to clinically-relevant biomedical signal data.

Forecasting intracranial hypertension using multi-scale waveform metrics

OBJECTIVE

Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively, leading to late detection and lost time for intervention planning. A pro-active approach that predicts critical events several hours ahead of time could assist in directing attention to patients at risk.

APPROACH

We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 h. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived.

MAIN RESULTS

Our model predicted events up to 8 h in advance with an alarm recall rate of 90% at a precision of 30% in the MIMIC-III waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time series, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 h was especially important.

SIGNIFICANCE

Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain.

Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success

PURPOSE OF REVIEW

Neurocritical care combines the complexity of both medical and surgical disease states with the inherent limitations of assessing patients with neurologic injury. Artificial intelligence (AI) has garnered interest in the basic management of these complicated patients as data collection becomes increasingly automated.

RECENT FINDINGS

In this opinion article, we highlight the potential AI has in aiding the clinician in several aspects of neurocritical care, particularly in monitoring and managing intracranial pressure, seizures, hemodynamics, and ventilation. The model-based method and data-driven method are currently the two major AI methods for analyzing critical care data. Both are able to analyze the vast quantities of patient data that are accumulated in the neurocritical care unit. AI has the potential to reduce healthcare costs, minimize delays in patient management, and reduce medical errors. However, these systems are an aid to, not a replacement for, the clinician's judgment.

Interpretation of Entropy Algorithms in the Context of Biomedical Signal Analysis and Their Application to EEG Analysis in Epilepsy

Biomedical signals are measurable time series that describe a physiological state of a biological system. Entropy algorithms have been previously used to quantify the complexity of biomedical signals, but there is a need to understand the relationship of entropy to signal processing concepts. In this study, ten synthetic signals that represent widely encountered signal structures in the field of signal processing were created to interpret permutation, modified permutation, sample, quadratic sample and fuzzy entropies. Subsequently, the entropy algorithms were applied to two different databases containing electroencephalogram (EEG) signals from epilepsy studies. Transitions from randomness to periodicity were successfully detected in the synthetic signals, while significant differences in EEG signals were observed based on different regions and states of the brain. In addition, using results from one entropy algorithm as features and the k-nearest neighbours algorithm, maximum classification accuracies in the first EEG database ranged from 63% to 73.5%, while these values increased by approximately 20% when using two different entropies as features. For the second database, maximum classification accuracy reached 62.5% using one entropy algorithm, while using two algorithms as features further increased that by 10%. Embedding entropies (sample, quadratic sample and fuzzy entropies) are found to outperform the rest of the algorithms in terms of sensitivity and show greater potential by considering the fine-tuning possibilities they offer. On the other hand, permutation and modified permutation entropies are more consistent across different input parameter values and considerably faster to calculate.

Artificial intelligence in neurocritical care

BACKGROUND

Neurocritical care combines the management of extremely complex disease states with the inherent limitations of clinically assessing patients with brain injury. As the management of neurocritical care patients can be immensely complicated, the automation of data-collection and basic management by artificial intelligence systems have garnered interest.

METHODS

In this opinion article, we highlight the potential artificial intelligence has in monitoring and managing several aspects of neurocritical care, specifically intracranial pressure, seizure monitoring, blood pressure, and ventilation.

RESULTS

The two major AI methods of analytical technique currently exist for analyzing critical care data: the model-based method and data driven method. Both of these methods have demonstrated an ability to analyze vast quantities of patient data, and we highlight the ways in which these modalities of artificial intelligence might one day play a role in neurocritical care.

CONCLUSIONS

While none of these artificial intelligence systems are meant to replace the clinician's judgment, these systems have the potential to reduce healthcare costs and errors or delays in medical management.

Continuous wavelet transform in the study of the time-scale properties of intracranial pressure in hydrocephalus

Normal pressure hydrocephalus (NPH) encompasses a heterogeneous group of disorders generally characterized by clinical symptoms, ventriculomegaly and anomalous cerebrospinal fluid (CSF) dynamics. Lumbar infusion tests (ITs) are frequently performed in the preoperatory evaluation of patients who show NPH features. The analysis of intracranial pressure (ICP) signals recorded during ITs could be useful to better understand the pathophysiology underlying NPH and to assist treatment decisions. In this study, 131 ICP signals recorded during ITs were analysed using two continuous wavelet transform (CWT)-derived parameters: Jensen divergence (JD) and spectral flux (SF). These parameters were studied in two frequency bands, associated with different components of the signal: B₁(0.15-0.3 Hz), related to respiratory blood pressure oscillations; and B₂ (0.67-2.5 Hz), related to ICP pulse waves. Statistically significant differences (p < 1.70 × 10^-3, Bonferroni-corrected Wilcoxon signed-rank tests) in pairwise comparisons between phases of ITs were found using the mean and standard deviation of JD and SF. These differences were mainly found in B₂, where a lower irregularity and variability, together with less prominent time-frequency fluctuations, were found in the hypertension phase of ITs. Our results suggest that wavelet analysis could be useful for understanding CSF dynamics in NPH.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.

In situ detection of water quality contamination events based on signal complexity analysis using online ultraviolet-visible spectral sensor

The contaminant detection in water distribution systems is essential to protect public health from potentially harmful compounds resulting from accidental spills or intentional releases. As a noninvasive optical technique, ultraviolet-visible (UV-Vis) spectroscopy is investigated for detecting contamination events. However, current methods for event detection exhibit the shortcomings of noise susceptibility. In this paper, a new method that has less sensitivity to noise was proposed to detect water quality contamination events by analyzing the complexity of the UV-Vis spectrum series. The proposed method applied approximate entropy (ApEn) to measure spectrum signals' complexity, which made a distinction between normal and abnormal signals. The impact of noise was attenuated with the help of ApEn's insensitivity to signal disturbance. This method was tested on a real water distribution system data set with various concentration simulation events. Results from the experiment and analysis show that the proposed method has a good performance on noise tolerance and provides a better detection result compared with the autoregressive model and sequential probability ratio test.

Continuous Monitoring of the Complexity of Intracranial Pressure After Head Injury

Multiscale entropy (MSE) has been increasingly used to investigate the complexity of biological signals. Our previous study demonstrated that the complexity of mean intracranial pressure (ICP), assessed by MSE based on the whole recording periods, is associated with the outcome after traumatic brain injury (TBI). To improve the feasibility of MSE in a clinical setting, this study examined whether the complexity of ICP waveforms based on shorter periods could be a reliable predictor of the outcome in patients with TBI. Results showed that the complexity of ICP slow waves, calculated in 3-h moving windows, correlates with the outcome of patients with TBI. Thus, the complexity of ICP may be a promising index to be incorporated into multimodal monitoring in patients with TBI.

Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range correlations

Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse fields. However, the practical application of entropy methods is challenging, due to the variety of entropy measures and estimators and the complexity of real-world time series, including nonstationarities and long-range correlations (LRC). We conduct a systematic study on the performance, bias, and limitations of three basic measures (entropy, conditional entropy, information storage) and three traditionally used estimators (linear, kernel, nearest neighbor). We investigate the dependence of entropy measures on estimator- and process-specific parameters, and we show the effects of three types of nonstationarities due to artifacts (trends, spikes, local variance change) in simulations of stochastic autoregressive processes. We also analyze the impact of LRC on the theoretical and estimated values of entropy measures. Finally, we apply entropy methods on heart rate variability data from subjects in different physiological states and clinical conditions. We find that entropy measures can only differentiate changes of specific types in cardiac dynamics and that appropriate preprocessing is vital for correct estimation and interpretation. Demonstrating the limitations of entropy methods and shedding light on how to mitigate bias and provide correct interpretations of results, this work can serve as a comprehensive reference for the application of entropy methods and the evaluation of existing studies.

Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics

This paper evaluates the performance of first generation entropy metrics, featured by the well known and widely used Approximate Entropy (ApEn) and Sample Entropy (SampEn) metrics, and what can be considered an evolution from these, Fuzzy Entropy (FuzzyEn), in the Electroencephalogram (EEG) signal classification context. The study uses the commonest artifacts found in real EEGs, such as white noise, and muscular, cardiac, and ocular artifacts. Using two different sets of publicly available EEG records, and a realistic range of amplitudes for interfering artifacts, this work optimises and assesses the robustness of these metrics against artifacts in class segmentation terms probability. The results show that the qualitative behaviour of the two datasets is similar, with SampEn and FuzzyEn performing the best, and the noise and muscular artifacts are the most confounding factors. On the contrary, there is a wide variability as regards initialization parameters. The poor performance achieved by ApEn suggests that this metric should not be used in these contexts.

Idiopathic Intracranial Hypertension in Monozygotic Female Twins: Intracranial Pressure Dynamics and Treatment Outcome

BACKGROUND

Familial cases of idiopathic intracranial hypertension (IIH) are exceedingly rare, and its occurrence in monozygotic twins has not been reported previously.

CASE DESCRIPTION

We report monozygotic female twins who developed IIH, one at age 25 years and the other at age 28 years. Continuous intracranial pressure (ICP) monitoring confirmed elevated ICP as measured initially by lumbar puncture. In both cases, successful treatment with resolution of papilledema and symptoms relief was achieved after ventriculoperitoneal shunting.

CONCLUSIONS

This report documents the first case of IIH in monozygotic twins and the associated changes in ICP dynamics. Interestingly, almost equivalent alterations in ICP dynamics were found in the 2 patients.

Effect of infusion tests on the dynamical properties of intracranial pressure in hydrocephalus

BACKGROUND AND OBJECTIVE

Hydrocephalus comprises a number of conditions characterised by clinical symptoms, dilated ventricles and anomalous cerebrospinal fluid (CSF) dynamics. Infusion tests (ITs) are usually performed to study CSF circulation and in the preoperatory evaluation of patients with hydrocephalus. The study of intracranial pressure (ICP) signals recorded during ITs could be useful to gain insight into the underlying pathophysiology of this condition and to further support treatment decisions. In this study, two wavelet parameters, wavelet turbulence (WT) and wavelet entropy (WE), were analysed in order to characterise the variability, irregularity and similarity in spectral content of ICP signals in hydrocephalus.

METHODS

One hundred and twelve ICP signals were analysed using WT and WE. These parameters were calculated in two frequency bands: B1 (0.15-0.3 Hz) and B2 (0.67-2.5 Hz). Each signal was divided into four artefact-free epochs corresponding to the basal, early infusion, plateau and recovery phases of the IT. We calculated the mean and standard deviation of WT and WE and analysed whether these parameters revealed differences between epochs of the IT.

RESULTS

Statistically significant differences (p < 1.70⋅10(-3), Bonferroni-corrected Wilcoxon signed-rank tests) in pairwise comparisons between phases of ITs were found using the mean and standard deviation of WT and WE. These differences were mainly found in B2.

CONCLUSIONS

Wavelet parameters like WT and WE revealed changes in the signal time-scale representation during ITs. Statistically significant differences were mainly found in B2, associated with ICP pulse waves, and included a higher degree of similarity in the spectral content, together with a lower irregularity and variability in the plateau phase with respect to the basal phase.

Computerized Decision Support System for Traumatic Brain Injury Management

Mortality and morbidity related to traumatic brain injury (TBI) present a major health care burden. Patients with severe TBI must be managed rapidly and efficiently to minimize secondary brain injury potentially leading to permanent sequelae. This is especially important in young patients, whose brain is still in development, making them particularly susceptible to secondary insults. The complexity of both brain injury pathophysiology and the intensive care unit environment makes the management of these patients challenging, with a risk of delayed response and/or patient instability contributing to worsened outcome. Computerized assistance in TBI appears likely to improve patient management, by helping clinicians quickly analyze and respond to ongoing clinical changes and optimizing patient status by guiding management. Currently, computerized decision support systems (CDSSs) do not feature continuous medical assistance with individualized treatment plans. This review presents new developments in CDSSs specialized in TBI. We also present the framework for future CDSSs needed to improve TBI management in real time, taking into account individual patient characteristics.

Cerebrovascular Signal Complexity Six Hours after Intensive Care Unit Admission Correlates with Outcome after Severe Traumatic Brain Injury

Disease states are associated with a breakdown in healthy interactions and are often characterized by reduced signal complexity. We applied approximate entropy (ApEn) analysis to investigate the correlation between the complexity of heart rate (ApEn-HR), mean arterial pressure (ApEn-MAP), intracranial pressure (ApEn-ICP), and a combined ApEn-product (product of the three individual ApEns) and outcome after traumatic brain injury. In 174 severe traumatic brain injured patients, we found significant differences across groups classified by the Glasgow Outcome Score in ApEn-HR (p = 0.007), ApEn-MAP (p = 0.02), ApEn-ICP (p = 0.01), ApEn-product (p = 0.001), and pressure reactivity index (PRx) (p = 0.004) in the first 6 h. This relationship strengthened in a 24 h and 72 h analysis (ApEn-MAP continued to correlate with death but was not correlated with favorable outcome). Outcome was dichotomized as survival versus death, and favorable versus unfavorable; the ApEn-product achieved the strongest statistical significance at 6 h (F = 11.0; p = 0.001 and F = 10.5; p = 0.001, respectively) and was a significant independent predictor of mortality and favorable outcome (p < 0.001). Patients in the lowest quartile for ApEn-product were over four times more likely to die (39.5% vs. 9.3%, p < 0.001) than those in the highest quartile. ApEn-ICP was inversely correlated with PRx (r = -0.39, p < 0.000001) indicating unique information related to impaired cerebral autoregulation. Our results demonstrate that as early as 6 h into monitoring, complexity measures from easily attainable vital signs, such as HR and MAP, in addition to ICP, can help triage those who require more intensive neurological management at an early stage.

Data collection and interpretation

Patient monitoring is routinely performed in all patients who receive neurocritical care. The combined use of monitors, including the neurologic examination, laboratory analysis, imaging studies, and physiological parameters, is common in a platform called multi-modality monitoring (MMM). However, the full potential of MMM is only beginning to be realized since for the most part, decision making historically has focused on individual aspects of physiology in a largely threshold-based manner. The use of MMM now is being facilitated by the evolution of bio-informatics in critical care including developing techniques to acquire, store, retrieve, and display integrated data and new analytic techniques for optimal clinical decision making. In this review, we will discuss the crucial initial steps toward data and information management, which in this emerging era of data-intensive science is already shifting concepts of care for acute brain injury and has the potential to both reshape how we do research and enhance cost-effective clinical care.

Variations in task constraints shape emergent performance outcomes and complexity levels in balancing

This study investigated the extent to which specific interacting constraints of performance might increase or decrease the emergent complexity in a movement system, and whether this could affect the relationship between observed movement variability and the central nervous system's capacity to adapt to perturbations during balancing. Fifty-two healthy volunteers performed eight trials where different performance constraints were manipulated: task difficulty (three levels) and visual biofeedback conditions (with and without the center of pressure (COP) displacement and a target displayed). Balance performance was assessed using COP-based measures: mean velocity magnitude (MVM) and bivariate variable error (BVE). To assess the complexity of COP, fuzzy entropy (FE) and detrended fluctuation analysis (DFA) were computed. ANOVAs showed that MVM and BVE increased when task difficulty increased. During biofeedback conditions, individuals showed higher MVM but lower BVE at the easiest level of task difficulty. Overall, higher FE and lower DFA values were observed when biofeedback was available. On the other hand, FE reduced and DFA increased as difficulty level increased, in the presence of biofeedback. However, when biofeedback was not available, the opposite trend in FE and DFA values was observed. Regardless of changes to task constraints and the variable investigated, balance performance was positively related to complexity in every condition. Data revealed how specificity of task constraints can result in an increase or decrease in complexity emerging in a neurobiological system during balance performance.

Multimodality monitoring: informatics, integration data display and analysis

The goal of multimodality neuromonitoring is to provide continuous, real-time assessment of brain physiology to prevent, detect, and attenuate secondary brain injury. Clinical informatics deals with biomedical data, information, and knowledge including their acquisition, storage, retrieval, and optimal use for clinical decision-making. An electronic literature search was conducted for English language articles describing the use of informatics in the intensive care unit setting from January 1990 to August 2013. A total of 64 studies were included in this review. Clinical informatics infrastructure should be adopted that enables a wide range of linear and nonlinear analytical methods be applied to patient data. Specific time epochs of clinical interest should be reviewable. Analysis strategies of monitor alarms may help address alarm fatigue. Ergonomic data display that present results from analyses with clinical information in a sensible uncomplicated manner improve clinical decision-making. Collecting and archiving the highest resolution physiologic and phenotypic data in a comprehensive open format data warehouse is a crucial first step toward information management and two-way translational research for multimodality monitoring. The infrastructure required is largely the same as that needed for telemedicine intensive care applications, which under the right circumstances improves care quality while reducing cost.

Optimizing human activity patterns using global sensitivity analysis

Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule's regularity for a population. We show how to tune an activity's regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.

Hypoglycemia-related electroencephalogram changes assessed by multiscale entropy

BACKGROUND

Several clinical studies have shown that low blood glucose (BG) levels affect electroencephalogram (EEG) rhythms through the quantification of traditional indicators based on linear spectral analysis. Nonlinear measures used in the last decades to characterize the EEG in several physiopathological conditions have never been assessed in hypoglycemia. The present study investigates if properties of the EEG signal measured by nonlinear entropy-based algorithms are altered in a significant manner when a state of hypoglycemia is entered.

SUBJECTS AND METHODS

EEG was acquired from 19 patients with type 1 diabetes during a hyperinsulinemic-euglycemic-hypoglycemic clamp experiment. In parallel, BG was frequently monitored by the standard YSI glucose and lactate analyzer and used to identify two 1-h intervals corresponding to euglycemia and hypoglycemia, respectively. In each subject, the P3-C3 EEG derivation in the two glycemic intervals was assessed using the multiscale entropy (MSE) approach, obtaining measures of sample entropy (SampEn) at various temporal scales. The comparison of how signal irregularity measured by SampEn varies as the temporal scale increases in the two glycemic states provides information on how EEG complexity is affected by hypoglycemia.

RESULTS

For both glycemic states, the MSE analysis showed that SampEn increases at small time scales and then monotonically decreases as the time scale becomes larger. Comparing the two conditions, SampEn was higher in hypoglycemia only at medium time scales.

CONCLUSIONS

A decrease in the complexity of EEG occurs when a state of hypoglycemia is entered, because of a degradation of the EEG long-range temporal correlations. Thanks to its ability to assess nonlinear dynamics of the EEG signal, the MSE approach seems to be a useful tool to complement information brought by standard linear indicators and provide new insights on how hypoglycemia affects brain functioning.

Selection of entropy-measure parameters for knowledge discovery in heart rate variability data

Background

Heart rate variability is the variation of the time interval between consecutive heartbeats. Entropy is a commonly used tool to describe the regularity of data sets. Entropy functions are defined using multiple parameters, the selection of which is controversial and depends on the intended purpose. This study describes the results of tests conducted to support parameter selection, towards the goal of enabling further biomarker discovery.

Methods

This study deals with approximate, sample, fuzzy, and fuzzy measure entropies. All data were obtained from PhysioNet, a free-access, on-line archive of physiological signals, and represent various medical conditions. Five tests were defined and conducted to examine the influence of: varying the threshold value r (as multiples of the sample standard deviation σ, or the entropy-maximizing r_Chon), the data length N, the weighting factors n for fuzzy and fuzzy measure entropies, and the thresholds r_F and r_L for fuzzy measure entropy. The results were tested for normality using Lilliefors' composite goodness-of-fit test. Consequently, the p-value was calculated with either a two sample t-test or a Wilcoxon rank sum test.

Results

The first test shows a cross-over of entropy values with regard to a change of r. Thus, a clear statement that a higher entropy corresponds to a high irregularity is not possible, but is rather an indicator of differences in regularity. N should be at least 200 data points for r = 0.2 σ and should even exceed a length of 1000 for r = r_Chon. The results for the weighting parameters n for the fuzzy membership function show different behavior when coupled with different r values, therefore the weighting parameters have been chosen independently for the different threshold values. The tests concerning r_F and r_L showed that there is no optimal choice, but r = r_F = r_L is reasonable with r = r_Chon or r = 0.2σ.

Conclusions

Some of the tests showed a dependency of the test significance on the data at hand. Nevertheless, as the medical conditions are unknown beforehand, compromises had to be made. Optimal parameter combinations are suggested for the methods considered. Yet, due to the high number of potential parameter combinations, further investigations of entropy for heart rate variability data will be necessary.

Development of a tonometric sensor for measurement and recording of arterial pressure waveform

There are many techniques and devices for measurement and recording of arterial blood pressure. Some of them allow (enable) additionally to observe the shape of a wave of arterial pressure, which can be regarded as one of the most important diagnostic parameters of human cardiovascular system. This paper presents a novel design and prototype of a new, non-invasive blood waveform measuring device. It expounds theoretical and experimental (including latest preliminary clinical) results obtained during several years of extensive investigations into blood pressure waveform measuring and monitoring problem. According to investigations performed in the laboratory and preliminary clinical evaluation, the sensor has linear steady-state characteristics and satisfactory dynamic properties. It is an efficient and accurate tool for blood pressure waveform monitoring and assessing the cardiovascular condition of the patients. The novelty of this solution is that the device is equipped with a pneumatic pressure sensor based on the pneumatic nozzle flapper amplifier principle with negative feedback. Due to such a technical solution, the device does not require any cuff, which remains an essential component of the majority of contemporary non-invasive blood pressure measurement devices; therefore, it can be used on the artery where the application of a cuff would be impossible (e.g., carotid artery). This advantage makes possible to obtain an accurate shape of blood pressure waveform with high fidelity, comparable to a direct measurement method. Moreover, during the measurement the device converts directly "pressure into pressure." Such a principle of operation makes possible to eliminate additional calibration (at the current stage of research we have eliminated the necessity of calibration in laboratory conditions).

Spectral analysis of intracranial pressure signals recorded during infusion studies in patients with hydrocephalus

Hydrocephalus includes a number of disorders characterised by clinical symptoms, enlarged ventricles (observable using neuroimaging techniques) and altered cerebrospinal fluid (CSF) dynamics. Infusion tests are one of the available procedures to study CSF circulation in patients with clinical and radiological features of hydrocephalus. In them, intracranial pressure (ICP) is deliberately raised and CSF circulation disorders evaluated through measurements of the resulting ICP. In this study, we analysed seventy-seven ICP signals recorded during infusion tests using four spectral-based parameters: median frequency (MF) and relative power (RP) in three frequency bands. These measures provide a novel perspective for the analysis of ICP signals in the frequency domain. Each signal was divided into four artefact-free epochs (corresponding to the basal, early infusion, plateau and recovery phases of the infusion study). The four spectral parameters were calculated for each epoch. We analysed differences between epochs of the infusion test and correlations between these epochs and patient data. Statistically significant differences (p < 1.7 × 10(-3), Bonferroni-corrected Wilcoxon signed-rank tests) were found between epochs of the infusion test using MF and RP. Furthermore, some spectral parameters (MF in the basal phase, RP for the first frequency band and in the early infusion phase, RP for the second frequency band and in all phases of the infusion study and RP in the third frequency band and in the basal phase) revealed significant correlations (p < 0.01) between epochs of the infusion test and signal amplitude in the basal and plateau phases. Our results suggest that spectral analysis of ICP signals could be useful for understanding CSF dynamics in hydrocephalus.

Analysis of Intracranial Pressure

The monitoring of intracranial pressure (ICP) is an important tool in medicine for its ability to portray the brain’s compliance status. The bedside monitor displays the ICP waveform and intermittent mean values to guide physicians in the management of patients, particularly those having sustained a traumatic brain injury. Researchers in the fields of engineering and physics have investigated various mathematical analysis techniques applicable to the waveform in order to extract additional diagnostic and prognostic information, although they largely remain limited to research applications. The purpose of this review is to present the current techniques used to monitor and interpret ICP and explore the potential of using advanced mathematical techniques to provide information about system perturbations from states of homeostasis. We discuss the limits of each proposed technique and we propose that nonlinear analysis could be a reliable approach to describe ICP signals over time, with the fractal dimension as a potential predictive clinically meaningful biomarker. Our goal is to stimulate translational research that can move modern analysis of ICP using these techniques into widespread practical use, and to investigate to the clinical utility of a tool capable of simplifying multiple variables obtained from various sensors.

Intracranial pressure pulse waveform correlates with aqueductal cerebrospinal fluid stroke volume

This study identifies a novel relationship between cerebrospinal fluid (CSF) stroke volume through the cerebral aqueduct and the characteristic peaks of the intracranial pulse (ICP) waveform. ICP waveform analysis has become much more advanced in recent years; however, clinical practice remains restricted to mean ICP, mainly due to the lack of physiological understanding of the ICP waveform. Therefore, the present study set out to shed some light on the physiological meaning of ICP morphological metrics derived by the morphological clustering and analysis of continuous intracranial pulse (MOCAIP) algorithm by investigating their relationships with a well defined physiological variable, i.e., the stroke volume of CSF through the cerebral aqueduct. Seven patients received both overnight ICP monitoring along with a phase-contrast MRI (PC-MRI) of the cerebral aqueduct to quantify aqueductal stroke volume (ASV). Waveform morphological analysis of the ICP signal was performed by the MOCAIP algorithm. Following extraction of morphological metrics from the ICP signal, nine temporal ICP metrics and two amplitude-based metrics were compared with the ASV via Spearman's rank correlation. Of the nine temporal metrics correlated with the ASV, only the width of the P2 region (ICP-Wi2) reached significance. Furthermore, both ICP pulse pressure amplitude and mean ICP did not reach significance. In this study, we showed the width of the second peak (ICP-Wi2) of an ICP pulse wave is positively related to the volume of CSF movement through the cerebral aqueduct. This finding is an initial step in bridging the gap between ICP waveform morphology research and clinical practice.

Complexity of intracranial pressure correlates with outcome after traumatic brain injury

This study applied multiscale entropy analysis to investigate the correlation between the complexity of intracranial pressure waveform and outcome after traumatic brain injury. Intracranial pressure and arterial blood pressure waveforms were low-pass filtered to remove the respiratory and pulse components and then processed using a multiscale entropy algorithm to produce a complexity index. We identified significant differences across groups classified by the Glasgow Outcome Scale in intracranial pressure, pressure-reactivity index and complexity index of intracranial pressure (P < 0.0001; P = 0.001; P < 0.0001, respectively). Outcome was dichotomized as survival/death and also as favourable/unfavourable. The complexity index of intracranial pressure achieved the strongest statistical significance (F = 28.7; P < 0.0001 and F = 17.21; P < 0.0001, respectively) and was identified as a significant independent predictor of mortality and favourable outcome in a multivariable logistic regression model (P < 0.0001). The results of this study suggest that complexity of intracranial pressure assessed by multiscale entropy was significantly associated with outcome in patients with brain injury.

Intracranial hypertension prediction using extremely randomized decision trees

Intracranial pressure (ICP) elevation (intracranial hypertension, IH) in neurocritical care is typically treated in a reactive fashion; it is only delivered after bedside clinicians notice prolonged ICP elevation. A proactive solution is desirable to improve the treatment of intracranial hypertension. Several studies have shown that the waveform morphology of the intracranial pressure pulse holds predictors about future intracranial hypertension and could therefore be used to alert the bedside clinician of a likely occurrence of the elevation in the immediate future. In this paper, a computational framework is proposed to predict prolonged intracranial hypertension based on morphological waveform features computed from the ICP. A key contribution of this work is to exploit an ensemble classifier method based on extremely randomized decision trees (Extra-Trees). Experiments on a representative set of 30 patients admitted for various intracranial pressure related conditions demonstrate the effectiveness of the predicting framework on ICP pulses acquired under clinical conditions and the superior results of the proposed approach in comparison to linear and AdaBoost classifiers.

Statistical model for cardiovascular signals with independent respiratory modulation for tracking pulse pressure variation

Cardiovascular signals including the electrocardiogram, pressure signals, and photoplethysmographs such as those used in pulse oximetry contain a wealth of information. Statistical models of these signals provide a means of representing and quantifying this information, and often lead to natural and optimal estimation algorithms. One powerful statistical model uses a Fourier approach to model cardiovascular signals as a harmonic sum of sinusoids with a fundamental frequency, amplitudes, and phases that vary slowly over time. We have further developed this model to incorporate respiratory effects including an additive component, pulse pressure variation (PPV), and respiratory sinus arrhythmia. PPV may be viewed as a form of amplitude modulation of the cardiovascular signal due to respiration. Current models do not explain the asymmetry between the upper and lower envelopes observed in cardiovascular pressure signals, and consequently are not appropriate for PPV estimation. We propose a model in which each of the cardiac harmonics is independently modulated by the respiratory signal. This improves the estimation accuracy and permits more accurate cardiovascular tracking and estimation. The proposed model is more accurate in PPV estimation applications.

Comparative study of approximate entropy and sample entropy robustness to spikes

OBJECTIVE

There is an ongoing research effort devoted to characterize the signal regularity metrics approximate entropy (ApEn) and sample entropy (SampEn) in order to better interpret their results in the context of biomedical signal analysis. Along with this line, this paper addresses the influence of abnormal spikes (impulses) on ApEn and SampEn measurements.

METHODS

A set of test signals consisting of generic synthetic signals, simulated biomedical signals, and real RR records was created. These test signals were corrupted by randomly generated spikes. ApEn and SampEn were computed for all the signals under different spike probabilities and for 100 realizations.

RESULTS

The effect of the presence of spikes on ApEn and SampEn is different for test signals with narrowband line spectra and test signals that are better modeled as broadband random processes. In the first case, the presence of extrinsic spikes in the signal results in an ApEn and SampEn increase. In the second case, it results in an entropy decrease. For real RR records, the presence of spikes, often due to QRS detection errors, also results in an entropy decrease.

CONCLUSIONS

Our findings demonstrate that both ApEn and SampEn are very sensitive to the presence of spikes. Abnormal spikes should be removed, if possible, from signals before computing ApEn or SampEn. Otherwise, the results can lead to misunderstandings or misclassification of the signal regularity.

Interpretation of the approximate entropy using fixed tolerance values as a measure of amplitude variations in biomedical signals

A new method for the quantification of amplitude variations in biomedical signals through moving approximate entropy is presented. Unlike the usual method to calculate the approximate entropy (ApEn), in which the tolerance value (r) varies based on the standard deviation of each moving window, in this work ApEn has been computed using a fixed value of r. We called this method, moving approximate entropy with fixed tolerance values: ApEn(f). The obtained results indicate that ApEn(f) allows determining amplitude variations in biomedical data series. These amplitude variations are better determined when intermediate values of tolerance are used. The study performed in diaphragmatic mechanomyographic signals shows that the ApEn(f) curve is more correlated with the respiratory effort than the standard RMS amplitude parameter. Furthermore, it has been observed that the ApEn(f) parameter is less affected by the existence of impulsive, sinusoidal, constant and Gaussian noises in comparison with the RMS amplitude parameter.

Clinical applications of heart rate variability in the triage and assessment of traumatically injured patients

Heart rate variability (HRV) is a method of physiologic assessment which uses fluctuations in the RR intervals to evaluate modulation of the heart rate by the autonomic nervous system (ANS). Decreased variability has been studied as a marker of increased pathology and a predictor of morbidity and mortality in multiple medical disciplines. HRV is potentially useful in trauma as a tool for prehospital triage, initial patient assessment, and continuous monitoring of critically injured patients. However, several technical limitations and a lack of standardized values have inhibited its clinical implementation in trauma. The purpose of this paper is to describe the three analytical methods (time domain, frequency domain, and entropy) and specific clinical populations that have been evaluated in trauma patients and to identify key issues regarding HRV that must be explored if it is to be widely adopted for the assessment of trauma patients.

Comparison of different threshold values r for approximate entropy: application to investigate the heart rate variability between heart failure and healthy control groups

Approximate entropy (ApEn) is widely accepted as a complexity measure of the heart rate variability (HRV) signal, but selecting the criteria for the threshold value r is controversial. This paper aims to verify whether Chon's method of forecasting the r(max) is an appropriate one for the HRV signal. The standard limb lead ECG signals of 120 subjects were recorded for 10 min in a supine position. The subjects were divided into two groups: the heart failure (22 females and 38 males, median age 62.4 ± 12.6) and healthy control group (33 females and 27 males, median age 51.5 ± 16.9). Three types of ApEn were calculated: the ApEn(0.2) using the recommended constant r = 0.2, the ApEn(chon) using Chon's method and the ApEn(max) using the true r(max). A Wilcoxon rank sum test showed that the ApEn(0.2) (p = 0.267) and the ApEn(max) (p = 0.813) had no statistical differences between the two groups, while the ApEn(chon) (p = 0.040) had. We generated a synthetic database to study the effect of two influential factors (the signal length N and the ratio of short- and long-term variability sd(1)/sd(2)) on the empirical formula in Chon's method (Chon et al 2009 IEEE Eng. Med. Biol. Mag. 28 18-23). The results showed that the empirical formula proposed by Chon et al is a good method for analyzing the random signal, but not an appropriate tool for analyzing nonlinear signals, such as the logistic or HRV signals.

Forecasting ICP elevation based on prescient changes of intracranial pressure waveform morphology

Interventions of intracranial pressure (ICP) elevation in neurocritical care is currently delivered only after healthcare professionals notice sustained and significant mean ICP elevation. This paper uses the morphological clustering and analysis of ICP (MOCAIP) algorithm to derive 24 metrics characterizing morphology of ICP pulses and test the hypothesis that preintracranial hypertension (Pre-IH) segments of ICP can be differentiated, using these morphological metrics, from control segments that were not associated with any ICP elevation or at least 1 h prior to ICP elevation. Furthermore, we investigate whether a global optimization algorithm could effectively find the optimal subset of these morphological metrics to achieve better classification performance as compared to using full set of MOCAIP metrics. The results showed that Pre-IH segments, using the optimal subset of metrics found by the differential evolution algorithm, can be differentiated from control segments at a specificity of 99% and sensitivity of 37% for these Pre-IH segments 5 min prior to the ICP elevation. While the sensitivity decreased to 21% for Pre-IH segments, 20 min prior to ICP elevation, the high specificity of 99% was retained. The performance using the full set of MOCAIP metrics was shown inferior to results achieved using the optimal subset of metrics. This paper demonstrated that advanced ICP pulse analysis combined with machine learning could potentially leads to the forecasting of ICP elevation so that a proactive ICP management could be realized based on these accurate forecasts.

Complexity analysis of the cerebrospinal fluid pulse waveform during infusion studies

PURPOSE

Nonlinear dynamics has enhanced the diagnostic abilities of some physiological signals. Recent studies have shown that the complexity of the intracranial pressure waveform decreases during periods of intracranial hypertension in paediatric patients with acute brain injury. We wanted to assess changes in the complexity of the cerebrospinal fluid (CSF) pressure signal over the large range covered during the study of CSF circulation with infusion studies.

METHODS

We performed 37 infusion studies in patients with hydrocephalus of various types and origin (median age 71 years; interquartile range 60-77 years). After 5 min of baseline measurement, infusion was started at a rate of 1.5 ml/min until a plateau was reached. Once the infusion finished, CSF pressure was recorded until it returned to baseline. We analysed CSF pressure signals using the Lempel-Ziv (LZ) complexity measure. To characterise more accurately the behaviour of LZ complexity, the study was segmented into four periods: basal, early infusion, plateau and recovery.

RESULTS

The LZ complexity of the CSF pressure decreased in the plateau of the infusion study compared to the basal complexity (p=0.0018). This indicates loss of complexity of the CSF pulse waveform with intracranial hypertension. We also noted that the level of complexity begins to increase when the infusion is interrupted and CSF pressure drops towards the initial values.

CONCLUSIONS

The LZ complexity decreases when CSF pressure reaches the range of intracranial hypertension during infusion studies. This finding provides further evidence of a phenomenon of decomplexification in the pulsatile component of the pressure signal during intracranial hypertension.

Measuring body temperature time series regularity using Approximate Entropy and Sample Entropy

Approximate Entropy (ApEn) and Sample Entropy (SampEn) have proven to be a valuable analyzing tool for a number of physiological signals. However, the characterization of these metrics is still lacking. We applied ApEn and SampEn to body temperature time series recorded from patients in critical state. This study was aimed at finding the optimal analytical configuration to best distinguish between survivor and non-survivor records, and at gaining additional insight into the characterization of such tools. A statistical analysis of the results was conducted to support the parameter and metric selection criteria for this type of physiological signal.

A subspace decomposition approach toward recognizing valid pulsatile signals

Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for the prediction of critical intracranial and cerebrovascular pathophysiological variations during the management of many neurological disorders. A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses. The algorithm is capable of enhancing the quality of ICP signals, recognizing valid (not contaminated with noise or artifacts) ICP pulses and designating the locations of the three ICP sub-peaks in a pulse. This paper extends the algorithm by proposing a singular value decomposition (SVD) technique to replace the correlation-based approach originally utilized in recognizing valid ICP pulses. The validation of the proposed method is conducted on a large database of ICP signals built from 700 h of recordings from 67 neurosurgical patients. A comparative analysis of the valid ICP recognition using the proposed SVD technique and the correlation-based method demonstrates a significant improvement in terms of (1) accuracy (61.96% reduction in the false positive rate while keeping the true positive rate as high as 99.08%) and (2) computational time (91.14% less time consumption), all in favor of the proposed method. Finally, this SVD-based valid pulse recognition can be potentially applied to process pulsatile signals other than ICP because no proprietary ICP features are incorporated in the algorithm.