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 the Multiscale Entropy (MSE). The 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 difference

Kullback-leibler entropy analysis of the electroencephalogram background activity in alzheimer's disease patients

Alzheimer's disease (AD) is the most frequent form of dementia in western countries. An early detection would be beneficial, but currently diagnostic accuracy is relatively poor. In this study, differences in information content between cortical areas in 12 AD patients and 11 control subjects were assessed with Kullback-Leibler (KL) entropy. KL entropy measures the degree of similarity between two probability distributions. EEGs were recorded from 19 scalp electrodes and KL entropy values of the EEGs in both groups were estimated for the local, distant and interhemispheric electrodes. KL entropy values were lower in AD patients than in age-matched control subjects, with significant effects for diagnosis and brain region (p < 0.05, two-way ANOVA). No significant interaction for diagnosis X region was found (p = 0.7671). Additionally a one-way ANOVA showed that KL entropy values were significantly lower in AD patients (p < 0.05) for the distant electrodes on the right hemisphere. These results suggest that KL entropy highlights information content changes in the EEG due to AD. However, further studies are needed to address the possible usefulness of KL entropy in the characterisation and early detection of AD

Reply to "Comment on 'Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy'"

We appreciate the interest of Dr Tang in our article. Certainly, our previous results should be taken with caution due to the small database size. Nevertheless, it must be noted that this limitation was clearly recognized in our article. Furthermore, our hypothesis is completely justified from the current state of the art in the analysis of electroencephalogram (EEG) signals from Alzheimer's disease (AD) patients. We evaluated whether the multiscale entropy (MSE) analysis of the EEG background activity was useful to distinguish AD patients and controls. We do believe that further discussions about risk factors or related clinicophysiological protein aspects are clearly beyond the scope of our article. For the sake of completeness, we now detail some results that complement our previous analysis. They suggest that the MSE analysis can provide relevant information about the dynamics of AD patients' EEG data. Thus, we must reaffirm our conclusions, although we again acknowledge that further studies are needed

A pilot evaluation of the practicality of the surrey virtual rehabilitation system: Perspectives from end-users

The Surrey Virtual Rehabilitation System (SVRS) was developed for clinical use in physiotherapy for children with cerebral palsy (CP). The overall aim of this work was to evaluate the practicality of the SVRS for three simple lower extremity exercises. Two children with CP, two clinical engineering trainees and a physiotherapist performed the tests whilst parents and a paediatric physiotherapist observed the exercise sessions. The feedback collected during an open-discussion and the descriptive analysis of responses to 15 closed-ended questions suggest that the participants were satisfied with the practicality of the SVRS. Outcome measures derived from data collected during the sessions indicate that the SVRS may provide clinically relevant feedback on the performance of patients for themselves and their treating clinicians. In conclusion, the SVRS appears practical for rehabilitation purposes and is worthy of further evaluation and development. Copyright © 2014 Inderscience Enterprises Ltd

Electroencephalogram background activity characterization with Detrended Moving Average in Alzheimer's disease patients

The aim of this study was to analyse the electroencephalogram (EEG) background activity in Alzheimer's disease (AD) with the Detrended Moving Average (DMA) method, a new approach to quantify correlation properties in non-stationary signals with underlying trends. EEGs were recorded from the 19 scalp loci of the international 10-20 system in 11 AD patients and 11 age-matched controls. Our results showed two scaling regions in all subjects' channels, with a clear bend when their corresponding slopes (alpha(1) and alpha(2)) were distinctly different. With the exception of electrode T4, the alpha(1) values were lower in control subjects than in AD patients, with significant differences at TS, P3, P4 and O1 (p < 0.01, Student's t-test). On the other hand, alpha(2) values were higher in control subjects than in AD patients, with significant differences only at F4. Furthermore, we evaluated the ability of alpha(2) to discriminate AD patients from control subjects at these electrodes using ROC plots. We obtained a maximum accuracy of 81.82% at O1 with alpha(1) and at F4 with alpha(2). These findings suggest that the scaling behaviour of the EEG is sensitive to AD and that the DMA method could help to increase our insight into brain dysfunction in AD

Kullback-Leibler Entropy Analysis of the Electroencephalogram Background Activity in Alzheimer's Disease Patients

Alzheimer's disease (AD) is the most frequent form of dementia in western countries. An early detection would be beneficial, but currently diagnostic accuracy is relatively poor. In this study, differences in information content between cortical areas in 12 AD patients and 11 control subjects were assessed with Kullback-Leibler (KL) entropy. KL entropy measures the degree of similarity between two probability distributions. EEGs were recorded from 19 scalp electrodes and KL entropy values of the EEGs in both groups were estimated for the local, distant and interhemispheric electrodes. KL entropy values were lower in AD patients than in age-matched control subjects, with significant effects for diagnosis and brain region (p < 0.05, two-way ANOVA). No significant interaction for diagnosis X region was found (p = 0.7671). Additionally a one-way ANOVA showed that KL entropy values were significantly lower in AD patients (p < 0.05) for the distant electrodes on the right hemisphere. These results suggest that KL entropy highlights information content changes in the EEG due to AD. However, further studies are needed to address the possible usefulness of KL entropy in the characterisation and early detection of AD

On the application of the auto mutual information rate of decrease to biomedical signals

The auto mutual information function (AMIF) evaluates the signal predictability by assessing linear and non-linear dependencies between two measurements taken from a single time series. Furthermore, the AMIF rate of decrease (AMIFRD) is correlated with signal entropy. This metric has been used to analyze biomedical data, including cardiac and brain activity recordings. Hence, the AMIFRD can be a relevant parameter in the context of biomedical signal analysis. Thus, in this pilot study, we have analyzed a synthetic sequence (a Lorenz system) and real biosignals (electroencephalograms recorded with eyes open and closed) with the AMIFRD. We aimed at illustrating the application of this parameter to biomedical time series. Our results show that the AMIFRD can detect changes in the non-linear dynamics of a sequence and that it can distinguish different physiological conditions

Lempel-Ziv Complexity Analysis for the Evaluation of Atrial Fibrillation Organization

The Lempel-Ziv (LZ) complexity is a non-linear time series analysis metric that reflects the arising rate of new patterns along with the sequence. Thus, it captures its temporal sequence and, quite conveniently, it can be computed with short data segments. In the present work, a detailed analysis on LZ complexity is presented within the context of atrial fibrillation (AF) organization estimation. As the analysed time series depend on the original sampling rate (fs), we evaluated the relationship between LZ complexity and fs. Furthermore, different implementations of LZ complexity were tested. Our results show the usefulness of LZ complexity to estimate AF organization and suggest that the signals from a terminating paroxysmal AF group are more organized (i.e. less complex) than those from the non-terminating paroxysmal AF group. However, the diagnostic accuracy was not as high as that obtained with sample entropy (SampEn), another non-linear metric, with the same database in a previous study (92% vs. 96%). Nevertheless, the LZ complexity analysis of AF organization with sampling frequencies higher than 2048 Hz, or even its combination with SampEn or other non-linear metrics, might improve the prediction of spontaneous AF termination

Optimized assessment of atrial fibrillation organization through suitable parameters of Sample Entropy

Sample Entropy (SampEn) is a nonlinear regularity index that requires the a priori selection of three parameters: the length of the sequences to be compared, m, the patterns similarity tolerance, r, and the number of samples under analysis, N. Appropriate values for m, r and N have been recommended in some cases, such as heart rate, hormonal data, etc., but no guidelines exist for the selection of that values. Hence, an optimal parameters study should be required for the application of SampEn to not previously analyzed biomedical signals. In this work, a thorough analysis on the optimal SampEn parameter values within two different scenarios of AF organization estimation, such as the prediction of paroxysmal AF termination and the electrical cardioversion outcome in persistent AF, is presented. Results indicated that, (i) the proportion between N and the sampling rate (ƒ(s)) should be higher than one second and ƒ(s) ≥ 256 Hz, (ii) overlapping between adjacent N-length windows does not improve organization estimation and (iii) values of m and r maximizing classification should be considered within a range wider than the proposed in the literature for heart rate analysis, i. e. m = 1 and m = 2 and r between 0.1 and 0.25 times the standard deviation of the data

Financial crisis and income-related inequalities in the universal provision of a public service: the case of healthcare in Spain

Background The objective of this paper is to analyse whether the recent recession has altered health care utilisation patterns of different income groups in Spain. Methods Based on information concerning individuals ‘income and health care use, along with health need indicators and demographic characteristics (provided by the Spanish National Health Surveys from 2006/07 and 2011/12), econometric models are estimated in two parts (mixed logistic regressions and truncated negative binominal regressions) for each of the public health services studied (family doctor appointments, appointments with specialists, hospitalisations, emergencies and prescription drug use). Results The results show that the principle of universal access to public health provision does not in fact prevent a financial crisis from affecting certain income groups more than others in their utilisation of public health services. Conclusions Specifically, in relative terms the recession has been more detrimental to low-income groups in the cases of specialist appointments and hospitalisations, whereas it has worked to their advantage in the cases of emergency services and family doctor appointments