Classification of Alzheimer’s disease from quadratic sample entropy of electroencephalogram

Currently accepted input parameter limitations in entropy-based, non-linear signal processing methods, for example, sample entropy (SampEn), may limit the information gathered from tested biological signals. The ability of quadratic sample entropy (QSE) to identify changes in electroencephalogram (EEG) signals of 11 patients with a diagnosis of Alzheimer's disease (AD) and 11 age-matched, healthy controls is investigated. QSE measures signal regularity, where reduced QSE values indicate greater regularity. The presented method allows a greater range of QSE input parameters to produce reliable results than SampEn. QSE was lower in AD patients compared with controls with significant differences (p < 0.01) for different parameter combinations at electrodes P3, P4, O1 and O2. Subject- and epoch-based classifications were tested with leave-one-out linear discriminant analysis. The maximum diagnostic accuracy and area under the receiver operating characteristic curve were 77.27 and more than 80%, respectively, at many parameter and electrode combinations. Furthermore, QSE results across all r values were consistent, suggesting QSE is robust for a wider range of input parameters than SampEn. The best results were obtained with input parameters outside the acceptable range for SampEn, and can identify EEG changes between AD patients and controls. However, caution should be applied because of the small sample size

Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation

The magnetoencephalogram (MEG) is contaminated with undesired signals, which are called artifacts. Some of the most important ones are the cardiac and the ocular artifacts (CA and OA, respectively), and the power line noise (PLN). Blind source separation (BSS) has been used to reduce the influence of the artifacts in the data. There is a plethora of BSS-based artifact removal approaches, but few comparative analyses. In this study, MEG background activity from 26 subjects was processed with five widespread BSS (AMUSE, SOBI, JADE, extended Infomax, and FastICA) and one constrained BSS (cBSS) techniques. Then, the ability of several combinations of BSS algorithm, epoch length, and artifact detection metric to automatically reduce the CA, OA, and PLN were quantified with objective criteria. The results pinpointed to cBSS as a very suitable approach to remove the CA. Additionally, a combination of AMUSE or SOBI and artifact detection metrics based on entropy or power criteria decreased the OA. Finally, the PLN was reduced by means of a spectral metric. These findings confirm the utility of BSS to help in the artifact removal for MEG background activity

Entropy-based nonlinear analysis for electrophysiological recordings of brain activity in Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disorder in which the death of brain cells causes memory loss and cognitive decline. As AD progresses, changes in the electrophysiological brain activity take place. Such changes can be recorded by the electroencephalography (EEG) and magnetoencephalography (MEG) techniques. These are the only two neurophysiologic approaches able to directly measure the activity of the brain cortex. Since EEGs and MEGs are considered as the outputs of a nonlinear system (i.e., brain), there has been an interest in nonlinear methods for the analysis of EEGs and MEGs. One of the most powerful nonlinear metrics used to assess the dynamical characteristics of signals is that of entropy. The aim of this thesis is to develop entropy-based approaches for characterization of EEGs and MEGs paying close attention to AD. Recent developments in the field of entropy for the characterization of physiological signals have tried: 1) to improve the stability and reliability of entropy-based results for short and long signals; and 2) to extend the univariate entropy methods to their multivariate cases to be able to reveal the patterns across channels. To enhance the stability of entropy-based values for short univariate signals, refined composite multiscale fuzzy entropy (MFE - RCMFE) is developed. To decrease the running time and increase the stability of the existing multivariate MFE (mvMFE) while keeping its benefits, the refined composite mvMFE (RCmvMFE) with a new fuzzy membership function is developed here as well. In spite of the interesting results obtained by these improvements, fuzzy entropy (FuzEn), RCMFE, and RCmvMFE may still lead to unreliable results for short signals and are not fast enough for real-time applications. To address these shortcomings, dispersion entropy (DispEn) and frequency-based DispEn (FDispEn), which are based on our introduced dispersion patterns and the Shannon’s definition of entropy, are developed. The computational cost of DispEn and FDispEn is O(N) – where N is the signal length –, compared with the O(N2) for popular sample entropy (SampEn) and FuzEn. DispEn and FDispEn also overcome the problem of equal values for embedded vectors and discarding some information with regard to the signal amplitudes encountered in permutation entropy (PerEn). Moreover, unlike PerEn, DispEn and FDispEn are relatively insensitive to noise. As extensions of our developed DispEn, multiscale DispEn (MDE) and multivariate MDE (mvMDE) are introduced to quantify the complexity of univariate and multivariate signals, respectively. MDE and mvMDE have the following advantages over the existing univariate and multivariate multiscale methods: 1) they are noticeably faster; 2) MDE and mvMDE result in smaller coefficient of variations for synthetic and real signals showing more stable profiles; 3) they better distinguish various states of biomedical signals; 4) MDE and mvMDE do not result in undefined values for short time series; and 5) mvMDE, compared with multivariate multiscale SampEn (mvMSE) and mvMFE, needs to store a considerably smaller number of elements. In this Thesis, two restating-state electrophysiological datasets related to AD are analyzed: 1) 148-channel MEGs recorded from 62 subjects (36 AD patients vs. 26 age-matched controls); and 2) 16-channel EEGs recorded from 22 subjects (11 AD patients vs. 11 age-matched controls). The results obtained by MDE and mvMDE suggest that the controls’ signals are more and less complex at respectively short (scales between 1 to 4) and longer (scales between 5 to 12) scale factors than AD patients’ recordings for both the EEG and MEG datasets. The p-values based on Mann-Whitney U-test for AD patients vs. controls show that the MDE and mvMDE, compared with the existing complexity techniques, significantly discriminate the controls from subjects with AD at a larger number of scale factors for both the EEG and MEG datasets. Moreover, the smallest p-values are achieved by MDE (e.g., 0.0010 and 0.0181 for respectively MDE and MFE using EEG dataset) and mvMDE (e.g., 0.0086 and 0.2372 for respectively mvMDE and mvMFE using EEG dataset) for both the EEG and MEG datasets, illustrating the superiority of these developed entropy-based techniques over the state-of-the-art univariate and multivariate entropy approaches. Overall, the introduced FDispEn, DispEn, MDE, and mvMDE methods are expected to be useful for the analysis of physiological signals due to their ability to distinguish different types of time series with a low computation time

One dimensional local binary patterns of electroencephalogram signals for detecting Alzheimer's disease

Alzheimer’s disease (AD) is neurodegenerative, caused by the progressive death of brain cells over time. One non-invasive approach to investigate AD is to use electroencephalogram (EEG) signals. The data are usually non-stationary with a strong background activity and noise which makes the analysis difficult leading to low performance in many real world applications including the detection of AD. In this study, we present a method based on local texture changes of EEG signals to differentiate AD patients from the healthy ones, using one-dimensional local binary patterns (1D-LBPs) coupled with support vector machines (SVM). Our proposed method maps the EEG data into a less detailed representation which is less sensitive to noise. A 10 fold cross validation performed at both the epoch and subject level show the discriminancy power of 1D-LBP feature vectors with application to AD data

Influence of Duodenal-Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods

[EN] Diabetes is a disease of great and rising prevalence, with the obesity epidemic being a significant contributing risk factor. Duodenal¿jejunal bypass liner (DJBL) is a reversible implant that mimics the effects of more aggressive surgical procedures, such as gastric bypass, to induce weight loss. We hypothesized that DJBL also influences the glucose dynamics in type II diabetes, based on the induced changes already demonstrated in other physiological characteristics and parameters. In order to assess the validity of this assumption, we conducted a quantitative analysis based on several nonlinear algorithms (Lempel¿Ziv Complexity, Sample Entropy, Permutation Entropy, and modified Permutation Entropy), well suited to the characterization of biomedical time series. We applied them to glucose records drawn from two extreme cases available of DJBL implantation: before and after 10 months. The results confirmed the hypothesis and an accuracy of 86.4% was achieved with modified Permutation Entropy. Other metrics also yielded significant classification accuracy results, all above 70%, provided a suitable parameter configuration was chosen. With the Leave¿One¿Out method, the results were very similar, between 72% and 82% classification accuracy. There was also a decrease in entropy of glycaemia records during the time interval studied. These findings provide a solid foundation to assess how glucose metabolism may be influenced by DJBL implantation and opens a new line of research in this field.The Czech clinical partners were supported by DRO IKEM 000023001 and RVO VFN 64165. The Czech technical partners were supported by Research Centre for Informatics grant numbers CZ.02.1.01/0.0/16 - 019/0000765 and SGS16/231/OHK3/3T/13-Support of interactive approaches to biomedical data acquisition and processing. 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Artifact removal in magnetoencephalogram background activity with independent component analysis

The aim of this study was to assess whether independent component analysis (ICA) could be valuable to remove power line noise, cardiac, and ocular artifacts from magnetoencephalogram (MEG) background activity. The MEGs were recorded from 11 subjects with a 148-channel whole-head magnetometer. We used a statistical criterion to estimate the number of independent components. Then, a robust ICA algorithm decomposed the MEG epochs and several methods were applied to detect those artifacts. The whole process had been previously tested on synthetic data. We found that the line noise components could be easily detected by their frequency spectrum. In addition, the ocular artifacts could be identified by their frequency characteristics and scalp topography. Moreover, the cardiac artifact was better recognized by its skewness value than by its kurtosis one. Finally, the MEG signals were compared before and after artifact rejection to evaluate our method