Pulse amplitude and Lempel-Ziv complexity of the cerebrospinal fluid pressure signal.

BACKGROUND: The complexity of the intracranial pressure (ICP) signal decreases with intracranial hypertension in children with acute brain injury as well as during infusion studies in adults with hydrocephalus. In this study we have analysed the pressure signal obtained in the lumbar subarachnoid space during infusion testing. The pulse amplitude rises when the ICP is increased by additional external volume. Our objective was to determine the relative influence of the pressure range and pulse amplitude on the loss of complexity observed during infusion-related intracranial hypertension. MATERIALS AND METHODS: The Lempel-Ziv (LZ) complexity of the cerebrospinal fluid pressure (CSFP) signal was analysed in 52 infusion studies performed in patients with normal pressure hydrocephalus (median age 71 years, IQR: 60-78). Four sequences during the baseline, infusion, steady plateau and recovery periods of each infusion study were selected. The mean values of the CSFP (mCSFP), pulse amplitude and LZ complexity in every sequence were measured. Correlations between LZ complexity and CSFP parameters were explored. RESULTS: Significant inverse correlations were found among LZ complexity, pulse amplitude and mCSFP during all periods of infusion testing, except at baseline. Partial correlation analysis controlling the effect of mCSFP emphasised the relationship between pulse amplitude and LZ complexity. When pulse amplitude is held constant the partial correlation between LZ complexity and mCSFP is not significant. CONCLUSIONS: The pulse amplitude of the CSFP signal seems to be a major determinant of the waveform complexity.

Characterisation of the intracranial pressure waveform during infusion studies by means of central tendency measure.

OBJECTIVE: In the present study an attempt was made to quantify and characterise the changes in the intracranial pressure (ICP) waveform over the wide pressure range covered during infusion studies by means of the central tendency measure (CTM). CTM is a non-linear approach using continuous chaotic modelling that summarises the degree of variability in a signal. METHODS: CTM of the ICP wave in the lumbar subarachnoid space was analysed in 77 infusion studies performed in patients with idiopathic and secondary forms of normal pressure hydrocephalus (median age 74 years, range 22-88). Four artefact-free epochs were selected during the baseline, infusion, plateau and relaxation stages of every infusion study. The average pressure, pulse amplitude and CTM were determined for each epoch. Correlations among these parameters were explored. RESULTS: CTM of the ICP waveform decreases, i.e. variability increases, as infusion studies progress from baseline pressure to the plateau stage. Significant correlations were found during all phases of infusion testing, except at baseline, between CTM and pressure, CTM and amplitude and pressure and amplitude. Partial correlations emphasised the relationship between CTM and amplitude. When pulse amplitude is held constant, CTM and the pressure range do not correlate. CONCLUSIONS: Volume loading leads to increased variability of the ICP signal measured by means of CTM. This finding summarises numerically the long-established phenomenon of increasing amplitude and rounding of ICP pulses associated with ICP elevation during infusion studies. CTM could be a suitable approach to quantify and characterise the pulsatile nature of the ICP wave.

Nonlinear measure of synchrony between blood oxygen saturation and heart rate from nocturnal pulse oximetry in obstructive sleep apnoea syndrome.

This study focuses on analysis of the relationship between changes in blood oxygen saturation (SaO(2)) and heart rate (HR) recordings from nocturnal pulse oximetry (NPO) in patients suspected of suffering from obstructive sleep apnoea (OSA) syndrome. Two different analyses were developed: a classical frequency analysis based on the magnitude squared coherence (MSC) and a nonlinear analysis by means of a recently developed measure of synchrony, the cross-approximate entropy (cross-ApEn). A data set of 187 subjects was studied. We found significantly higher correlation and synchrony between oximetry signals from OSA positive patients compared with OSA negative subjects. We assessed the diagnostic ability to detect OSA syndrome of both the classical and nonlinear approaches by means of receiver operating characteristic (ROC) analyses with tenfold cross-validation. The nonlinear measure of synchrony significantly improved the results obtained with classical MSC: 69.2% sensitivity, 90.9% specificity and 78.1% accuracy were reached with MSC, whereas 83.7% sensitivity, 84.3% specificity and 84.0% accuracy were obtained with cross-ApEn. Our results suggest that the use of nonlinear measures of synchrony could provide essential information from oximetry signals, which cannot be obtained with classical spectral analysis.

Predicting survival in critical patients by use of body temperature regularity measurement based on approximate entropy.

Body temperature is a classical diagnostic tool for a number of diseases. However, it is usually employed as a plain binary classification function (febrile or not febrile), and therefore its diagnostic power has not been fully developed. In this paper, we describe how body temperature regularity can be used for diagnosis. Our proposed methodology is based on obtaining accurate long-term temperature recordings at high sampling frequencies and analyzing the temperature signal using a regularity metric (approximate entropy). In this study, we assessed our methodology using temperature registers acquired from patients with multiple organ failure admitted to an intensive care unit. Our results indicate there is a correlation between the patient's condition and the regularity of the body temperature. This finding enabled us to design a classifier for two outcomes (survival or death) and test it on a dataset including 36 subjects. The classifier achieved an accuracy of 72%.

Entropy analysis of the EEG background activity in Alzheimer's disease patients.

Alzheimer's disease (AD) is the most common neurodegenerative disorder. Although a definite diagnosis is only possible by necropsy, a differential diagnosis with other types of dementia and with major depression should be attempted. The aim of this study was to analyse the electroencephalogram (EEG) background activity of AD patients to test the hypothesis that the regularity of the AD patients' EEG is higher than that of age-matched controls. We recorded the EEG from 19 scalp electrodes in 11 AD patients and 11 age-matched controls. Two different methods were used to estimate the regularity of the EEG background activity: spectral entropy (SpecEn) and sample entropy (SampEn). We did not find significant differences between AD patients and control subjects' EEGs with SpecEn. On the other hand, AD patients had significantly lower SampEn values than control subjects (p < 0.01) at electrodes P3, P4, O1 and O2. Our results show an increase of EEG regularity in AD patients. These findings suggest that nonlinear analysis of the EEG with SampEn could yield essential information and may contribute to increasing the insight into brain dysfunction in AD in ways which are not possible with more classical and conventional statistical methods.

Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy.

The aim of this study was to analyse the electroencephalogram (EEG) background activity of Alzheimer's disease (AD) patients using multiscale entropy (MSE). MSE is a recently developed method that quantifies the regularity of a signal on different time scales. These time scales are inspected by means of several coarse-grained sequences formed from the analysed signals. We recorded the EEGs from 19 scalp electrodes in 11 AD patients and 11 age-matched controls and estimated the MSE profile for each epoch of the EEG recordings. The shape of the MSE profiles reveals the EEG complexity, and it suggests that the EEG contains information in deeper scales than the smallest one. Moreover, the results showed that the EEG background activity is less complex in AD patients than control subjects. We found significant differences between both subject groups at electrodes F3, F7, Fp1, Fp2, T5, T6, P3, P4, O1 and O2 (p-value < 0.01, Student's t-test). These findings indicate that the EEG complexity analysis performed on deeper time scales by MSE may be a useful tool in order to increase our knowledge of AD.

Nonlinear characteristics of blood oxygen saturation from nocturnal oximetry for obstructive sleep apnoea detection.

Nocturnal oximetry is an attractive option for the diagnosis of obstructive sleep apnoea (OSA) syndrome because of its simplicity and low cost compared to polysomnography (PSG). The present study assesses nonlinear analysis of blood oxygen saturation (SaO(2)) from nocturnal oximetry as a diagnostic test to discriminate between OSA positive and OSA negative patients. A sample of 187 referred outpatients, clinically suspected of having OSA, was studied using nocturnal oximetry performed simultaneously with complete PSG. A positive OSA diagnosis was found for 111 cases, while the remaining 76 cases were classified as OSA negative. The following oximetric indices were obtained: cumulative time spent below a saturation of 90% (CT90), oxygen desaturation indices of 4% (ODI4), 3% (ODI3) and 2% (ODI2) and the delta index (Delta index). SaO(2) records were subsequently processed applying two nonlinear methods: central tendency measure (CTM) and Lempel-Ziv (LZ) complexity. Significant differences (p < 0.01) were found between OSA positive and OSA negative patients. Using CTM we obtained a sensitivity of 90.1% and a specificity of 82.9%, while with LZ the sensitivity was 86.5% and the specificity was 77.6%. CTM and LZ accuracies were higher than those provided by ODI4, ODI3, ODI2 and CT90. The results suggest that nonlinear analysis of SaO(2) signals from nocturnal oximetry could yield useful information in OSA diagnosis.

Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer's disease patients.

We analysed the electroencephalogram (EEG) from Alzheimer's disease (AD) patients with two nonlinear methods: approximate entropy (ApEn) and auto mutual information (AMI). ApEn quantifies regularity in data, while AMI detects linear and nonlinear dependencies in time series. EEGs from 11 AD patients and 11 age-matched controls were analysed. ApEn was significantly lower in AD patients at electrodes O1, O2, P3 and P4 (p < 0.01). The EEG AMI decreased more slowly with time delays in patients than in controls, with significant differences at electrodes T5, T6, O1, O2, P3 and P4 (p < 0.01). The strong correlation between results from both methods shows that the AMI rate of decrease can be used to estimate the regularity in time series. Our work suggests that nonlinear EEG analysis may contribute to increase the insight into brain dysfunction in AD, especially when different time scales are inspected, as is the case with AMI.

Heart rate regularity analysis obtained from pulse oximetric recordings in the diagnosis of obstructive sleep apnea.

Approximate entropy (ApEn) is a technique that can be used to quantify the irregularity or variability of time series. We prospectively evaluated the validity of ApEn of heart rate data obtained from pulse oximetric recordings as a diagnostic test for obstructive sleep apnea (OSA) in patients clinically suspected of suffering this disease. A sample of 187 referred outpatients (147 men and 40 women), with a mean age of 57.9+/-12.8 years and a body mass index of 29.5+/-5.5 kg/m(2), clinically suspected of having OSA were studied using nocturnal pulse oximetric recording performed simultaneously with complete polysomnography. A diagnosis of OSA was confirmed in 111 (59.3%). Patients with OSA presented significantly higher ApEn levels than those without OSA (1.334+/-0.189 vs 1.167+/-0.182). Chronic obstructive pulmonary disease (COPD) was diagnosed for 42 patients. Among these patients, 22 (52.4%) were diagnosed with OSA. COPD patients with OSA showed significantly higher ApEn levels than COPD patients without OSA (1.337+/-0.193 vs 1.184+/-0.173; p=0.01). ApEn correlated significantly with apnea-hypopnea index (r=0.38; p=0.000). There was no significant correlation between ApEn and either age or body mass index. No significant changes were observed in ApEn throughout the night in OSA patients. Using receiver operating characteristic curve analysis, we obtained a diagnostic sensitivity of 71.2%, specificity of 78.9%, positive predictive value of 81.3%, and negative predictive value of 66% at a threshold of 1.272. We conclude that ApEn analysis of heart rate data obtained from pulse oximetric recordings could be a useful tool in the study of OSA.