Tsallis entropy as a biomarker for detection of Alzheimer's disease.

Alzheimer's disease (AD) and other forms of dementia are one of the major public health and social challenges of our time because of the large number of people affected. Early diagnosis is important for patients and their families to get maximum benefits from access to health and social care services and to plan for the future. EEG provides useful insight into brain functions and can play a useful role as a first line of decision-support tool for early detection and diagnosis of dementia. It is non-invasive, low-cost and has a high temporal resolution. The functions of brain cells are affected by damage caused by dementia and this in turn causes changes in the features of the EEG. Information theoretic methods have emerged as a potentially useful way to quantify changes in the EEG as biomarkers of dementia. Tsallis entropy has been shown to be one of the most promising information theoretic methods for quantifying changes in the EEG. In this paper, we develop the approach further. This has yielded an enhanced performance compared to existing approaches

Changes in the EEG amplitude as a biomarker for early detection of Alzheimer's disease.

The rapid increase in the number of older people with Alzheimer's disease (AD) and other forms of dementia represents one of the major challenges to the health and social care systems. Early detection of AD makes it possible for patients to access appropriate services and to benefit from new treatments and therapies, as and when they become available. The onset of AD starts many years before the clinical symptoms become clear. A biomarker that can measure the brain changes in this period would be useful for early diagnosis of AD. Potentially, the electroencephalogram (EEG) can play a valuable role in early detection of AD. Damage in the brain due to AD leads to changes in the information processing activity of the brain and the EEG which can be quantified as a biomarker. The objective of the study reported in this paper is to develop robust EEG-based biomarkers for detecting AD in its early stages. We present a new approach to quantify the slowing of the EEG, one of the most consistent features at different stages of dementia, based on changes in the EEG amplitudes (ΔEEGA). The new approach has sensitivity and specificity values of 100% and 88.88%, respectively, and outperformed the Lempel-Ziv Complexity (LZC) approach in discriminating between AD and normal subjects

Electroencephalogram Based Biomarkers for Detection of Alzheimer’s Disease

Alzheimer’s disease (AD) is an age-related progressive and neurodegenerative disorder, which is characterized by loss of memory and cognitive decline. It is the main cause of disability among older people. The rapid increase in the number of people living with AD and other forms of dementia due to the aging population represents a major challenge to health and social care systems worldwide. Degeneration of brain cells due to AD starts many years before the clinical manifestations become clear. Early diagnosis of AD will contribute to the development of effective treatments that could slow, stop, or prevent significant cognitive decline. Consequently, early diagnosis of AD may also be valuable in detecting patients with dementia who have not obtained a formal early diagnosis, and this may provide them with a chance to access suitable healthcare facilities. An early diagnosis biomarker capable of measuring brain cell degeneration due to AD would be valuable. Potentially, electroencephalogram (EEG) can play a valuable role in the early diagnosis of AD. EEG is noninvasive and low cost, and provides valuable information about brain dynamics in AD. Thus, EEG-based biomarkers may be used as a first-line decision-support tool in AD diagnosis and could complement other AD biomarkers

Cross-Frequency Coupling and Intelligent Neuromodulation.

Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field

Early diagnosis of Alzheimer's disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts

Alzheimer's disease (AD) is the most common neurodegenerative disease among the elderly with a progressive decline in cognitive function significantly affecting quality of life. Both the prevalence and emotional and financial burdens of AD on patients, their families, and society are predicted to grow significantly in the near future, due to a prolongation of the lifespan. Several lines of evidence suggest that modifications of risk-enhancing life styles and initiation of pharmacological and non-pharmacological treatments in the early stage of disease, although not able to modify its course, helps to maintain personal autonomy in daily activities and significantly reduces the total costs of disease management. Moreover, many clinical trials with potentially disease-modifying drugs are devoted to prodromal stages of AD. Thus, the identification of markers of conversion from prodromal form to clinically AD may be crucial for developing strategies of early interventions. The current available markers, including volumetric magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebral spinal fluid (CSF) analysis are expensive, poorly available in community health facilities, and relatively invasive. Taking into account its low cost, widespread availability and non-invasiveness, electroencephalography (EEG) would represent a candidate for tracking the prodromal phases of cognitive decline in routine clinical settings eventually in combination with other markers. In this scenario, the present paper provides an overview of epidemiology, genetic risk factors, neuropsychological, fluid and neuroimaging biomarkers in AD and describes the potential role of EEG in AD investigation, trying in particular to point out whether advanced analysis of EEG rhythms exploring brain function has sufficient specificity/sensitivity/accuracy for the early diagnosis of AD

Windowing compensation in Fourier based Surrogate Analysis and application to EEG signal classification

This paper shows how adding a second step of windowing after each phase randomization can reduce the False Rejection Rate in Fourier based Surrogate Analysis. Windowing techniques reduce the discontinuities at the boundaries of the periodically extended data sequence in Fourier Series. However, they add a time domain non-stationarity which affects the Surrogate Analysis. This effect is particularly problematic for short low-pass signals. Applying the same window to the surrogate data allows having the same non-stationarity. The method is tested on order 1 autoregressive process null hypothesis by Monte-Carlo simulations. Previous methods were not able to yield good performances for left-sided and right-sided tests at the same time, even less with bilateral tests. It is shown that the new method is conservative for unilateral tests as well as bilateral tests. In order to show that the proposed windowing method can be useful in real context, in this extended paper, it was applied for an EEG diagnostic problem. A dataset comprising the EEG measurements of 15 subject distributed in three groups: attention-deficit disorder primarily hyperactive-impulsive (ADHD), attention-deficit disorder primarily inattentive (ADD); and anxiety with attentional fragility (ANX) was used. Both statistical and machine learning (Naïve Bayesian) approaches were considered. The Mean Short Windowed SA (MSWSA) was used as a signal feature and its performances was studied with respect to the windowing systems. The main findings were that (i) the MSWSA feature has less variability for ADD than for ADHD or ANX, (ii) the proposed windowing method reduces bias and non-normality of the SA feature, (iii) with the proposed method and a naïve Bayesian classifier, a 93% success rate of discriminating ADD from ADHD and ANX was achieved with leave-one-out cross-validation, and (iv) the new feature could not have yielded interesting results without the proposed windowing system

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