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

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

THE USE OF SPECTRAL ENTROPY OF EEG TO PREDICT ANESTHESIA DEPTH ON THE DSPIC BASED SYSTEM

Anestezi derinliği ölçmek için birçok EEG (Elektroensaflogram) analiz yöntemi kullanılmaktadır. Bu yöntemlerin tümü çevrim dışı tabanlı olduğundan, gerçek zaman tabanlı yöntemlerin geliştirilmesine hala gereksinim vardır. Çalışmadaki amaç, derin anestezi ve uyanıklık durumlarının değerlendirilmesi için gerçek zaman dsPIC tabanlı EEG analiz sistemi geliştirmektir. Gerçek hipnogram değerleri ve hesaplanmış EEG tabanlı ölçümler arasında benzerlikler olduğu gösterilmiştir. Sonuç olarak bu çalışmanın anestezi izleme aygıtı geliştirmek için yeterli birikime sahip olduğu söylenebilir. Many of the EEG analysis methods are used to monitor the depth of anesthesia. All of these methods are off-line based; therefore, the real-time based methods still need to be developed. Our aim is to develop a real-time dsPIC based EEG analysis system for evaluating of deep anesthesia and of awake states. It was shown that there were quite similarities between the actual hypnogram values and the calculated EEG based measures. Consequently, we could say that this study has enough potential application to develop an anesthesia monitoring device

Decreased Lempel-Ziv complexity in Alzheimer's disease patients' magnetoencephalograms

The aim of the present research is to study the magnetoencephalogram (MEG) background activity in patients with Alzheimer's disease (AD) using the Lempel-Ziv (LZ) complexity. We recorded the MEG with a 148-channel whole-head magnetometer (MAGNES 2500 WH, 4D Neuroimaging) in 10 patients with probable AD and 10 age-matched control subjects, during five minutes. Artefact-free epochs were selected for the non-linear analysis. In all MEG channels, the AD patients had lower complexity than control subjects. In 77 of them the differences were statistically significant (p < 0.01). These preliminary results suggest that cognitive dysfunction in AD is associated with a decreased complexity in certain regions of the brain

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

Parliamentary roll-call voting as a complex dynamical system:The case of Chile

A method is proposed to study the temporal variability of legislative roll-call votes in a parliament from the perspective of complex dynamical systems. We studied the Chilean Chamber of Deputies’ by analyzing the agreement ratio and the voting outcome of each vote over the last 19 years with a Recurrence Quantification Analysis and an entropy analysis (Sample Entropy). Two significant changes in the temporal variability were found: one in 2014, where the voting outcome became more recurrent and with less entropy, and another in 2018, where the agreement ratio became less recurrent and with higher entropy. These changes may be directly related to major changes in the Chilean electoral system and the composition of the Chamber of Deputies, given that these changes occurred just after the first parliamentary elections with non-compulsory voting (2013 elections) and the first elections with a proportional system in conjunction with an increase in the number of deputies (2017 elections) were held.</p

Entropy and Complexity Analyses in Alzheimer’s Disease: An MEG Study

Alzheimer’s disease (AD) is one of the most frequent disorders among elderly population and it is considered the main cause of dementia in western countries. This irreversible brain disorder is characterized by neural loss and the appearance of neurofibrillary tangles and senile plaques. The aim of the present study was the analysis of the magnetoencephalogram (MEG) background activity from AD patients and elderly control subjects. MEG recordings from 36 AD patients and 26 controls were analyzed by means of six entropy and complexity measures: Shannon spectral entropy (SSE), approximate entropy (ApEn), sample entropy (SampEn), Higuchi’s fractal dimension (HFD), Maragos and Sun’s fractal dimension (MSFD), and Lempel-Ziv complexity (LZC). SSE is an irregularity estimator in terms of the flatness of the spectrum, whereas ApEn and SampEn are embbeding entropies that quantify the signal regularity. The complexity measures HFD and MSFD were applied to MEG signals to estimate their fractal dimension. Finally, LZC measures the number of different substrings and the rate of their recurrence along the original time series. Our results show that MEG recordings are less complex and more regular in AD patients than in control subjects. Significant differences between both groups were found in several brain regions using all these methods, with the exception of MSFD (p-value < 0.05, Welch’s t-test with Bonferroni’s correction). Using receiver operating characteristic curves with a leave-one-out cross-validation procedure, the highest accuracy was achieved with SSE: 77.42%. We conclude that entropy and complexity analyses from MEG background activity could be useful to help in AD diagnosis