Characterization and classification of kinesthetic motor imagery levels.

Objective.Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals.Approach.A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN).Main results.A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN.Significance.Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

Memory in Ion Channel Kinetics.

Ion channels are transport proteins present in the lipid bilayers of biological membranes. They are involved in many physiological processes, such as the generation of nerve impulses, hormonal secretion, and heartbeat. Conformational changes in the ion channel-forming protein allow the opening or closing of pores to control the ionic flux through the cell membranes. The opening and closing of the ion channel have been classically treated as a random kinetic process, known as a Markov process. Here the time the channel remains in a given state is assumed to be independent of the condition it had in the previous state. More recently, however, several studies have shown that this process is not random but a deterministic one, where both the open and closed dwell-times and the ionic current flowing through the channel are history-dependent. This property is called long memory or long-range correlation. However, there is still much controversy regarding how this memory originates, which region of the channel is responsible for this property, and which models could best reproduce the memory effect. In this article, we provide a review of what is, where it is, its possible origin, and the mathematical methods used to analyze the long-term memory present in the kinetic process of ion channels.

Estabilidad de la actividad eléctrica no lineal durante condiciones basales con los ojos cerrados / Nonlinear electrical activity stability in basal, eyes closed conditions

Introducción: El electroencefalograma permite registrar la actividad eléctrica cerebral en estado de reposo y durante la ejecución de tareas cognitivas. Objetivo: Evaluar si la actividad cerebral, analizada como dinámica no lineal, se mantiene estable durante diferentes ventanas temporales en una condición basal con ojos cerrados. Métodos: Se realizaron registros con electroencefalograma durante dos minutos a 14 estudiantes universitarios varones. Posteriormente, se compararon las medias de índices de Hurst (H) en ventanas temporales de 60, 30 y 10 segundos. Resultados: Las medias de los índices H son estables a través de diferentes ventanas temporales en las regiones prefrontales, temporales y occipitales. Conclusiones: Los registros de electroencefalograma en condiciones basales con los ojos cerrados son válidos para comparar protocolos experimentales de resolución de problemas cognitivos utilizando el exponente de Hurst en los sujetos de la muestra y en otros con características similares(AU)

Introduction: Electroencephalography makes it possible to record brain electrical activity at rest and during the performance of cognitive tasks. Objective: Determine whether brain activity analyzed as nonlinear dynamics remains stable during various time windows in basal, eyes closed conditions. Methods: Electroencephalographic records of 14 male university students were taken during two minutes. Hurst's index means (H) were then compared in time windows of 60, 30 and 10 seconds. Results: H index means are stable throughout the various time windows in the prefrontal, temporal and occipital regions. Conclusions: Electroencephalographic records in basal, eyes closed conditions are valid to compare experimental protocols for cognitive problem solving using the Hurst exponent in subjects from the sample as well as others of similar characteristics(AU)

Comportamiento estocástico de la COVID-19 en la República Bolivariana de Venezuela. ¿Persistencia o antipersistencia en los contagios? / Stochastic behavior of covid-19 in the Bolivarian Republic of Venezuela: persistence or antipersistence in infections?

La novedosa pandemia por coronavirus, etiquetada por la Organización Mundial de la Salud OMS, 2020) como la covid-19, se reportó por primera vez en Wuhan, China, el 31 de diciembre de 2019 y a la fecha, según estimaciones de la misma OMS (2020), en la medida en que se ha extendido a nivel planetario, ha infectado a más de 9,2 millones de personas, de las cuales se reportan más de 500.000 fallecidos y 5.2 millones de pacientes recuperados. En este estudio, aplicaremos el exponente de Hurst (1951) asociado con la estadística fractal para simular la propagación de la covid-19, considerando series temporales de fluctuaciones de nuevos casos diarios de la enfermedad, disponibles a través de un sitio web de referencia de la República Bolivariana de Venezuela, como lo es el Observatorio Nacional de Ciencia, Tecnología e Innovación (ONCTI). Se parte de la hipótesis de que la propagación de la covid-19, puede analizarse en función de las fluctuaciones del crecimiento de nuevos casos diarios de contagio. Para ello, se empleó un método de rango reescalado R/S que permitió calcular el Exponente de Hurst, parámetro estocástico cuyo valor permitió inferir sobre la presencia de correlaciones de largo alcance en la transmisión del virus entre la población. Estudiaremos los efectos de correlación en la propagación de COVID-19 en Venezuela mediante el análisis de las series temporales de nuevos casos después del decreto de Alerta dictado por el Ejecutivo Nacional que convocó a la ciudadanía a "quedarse en casa" mediante una cuarentena social obligatoria. Simularemos el comportamiento a mediano plazo (180 días) considerando las fluctuaciones de los nuevos casos de contagio diarios sobre la base de dos factores: los casos de contagio importados y los contagios comunitarios. En consecuencia, inicialmente examinaremos el origen de correlaciones con grandes fluctuaciones, y posteriormente analizaremos en base a las series de tiempo de nuevos casos diarios de la covid-19 en Venezuela, para luego establecer las correlaciones de largo alcance e inferir sobre la posible la persistencia o antipersistencia de la misma(AU)

The novel coronavirus pandemic, labeled by the World Health Organization (WHO) as Covid-19, was first reported in Wuhan, China, on December 31, 2019. To date, according to the WHO's estimates, it has infected more than 9.2 million people, of whom more than 500,000 are reported dead, and 5.2 million patients have recovered. To deepen in its study, we apply the Hurst exponent, associated with fractal statistics to simulate the spread of Covid-19, considering time series of fluctuations of new daily cases, which are available in a site reference website of the Bolivarian Republic of Venezuela, such as the National Observatory for Science, Technology, and Innovation (ONCTI). This work starts from the hypothesis that the spread of Covid-19 can be analyzed based on the fluctuations in the increase of new daily cases. For this, an R / S rescaled range method was used that allowed calculating the Hurst Exponent, a stochastic parameter whose value allowed inferring the presence of long-range correlations in the virus transmission among the population. We will study the correlation effects in the spread of COVID-19 in Venezuela by analyzing the time series of new cases after the alert decree issued by the Executive branch, which called on citizens to "stay at home" through a mandatory social quarantine. Consequently, initially, we will examine the origin of correlations with large fluctuations, followed by an analysis based on the time series of new daily cases of Covid-19 in Venezuela, in order to establish the long-range correlations and infer about the possible persistence or anti persistence of it(AU)

Evaluation of genome similarities using a wavelet-domain approach

Abstract INTRODUCTION: Tuberculosis is listed among the top 10 causes of deaths worldwide. The resistant strains causing this disease have been considered to be responsible for public health emergencies and health security threats. As stated by the World Health Organization (WHO), around 558,000 different cases coupled with resistance to rifampicin (the most operative first-line drug) have been estimated to date. Therefore, in order to detect the resistant strains using the genomes of Mycobacterium tuberculosis (MTB), we propose a new methodology for the analysis of genomic similarities that associate the different levels of decomposition of the genome (discrete non-decimated wavelet transform) and the Hurst exponent. METHODS: The signals corresponding to the ten analyzed sequences were obtained by assessing GC content, and then these signals were decomposed using the discrete non-decimated wavelet transform along with the Daubechies wavelet with four null moments at five levels of decomposition. The Hurst exponent was calculated at each decomposition level using five different methods. The cluster analysis was performed using the results obtained for the Hurst exponent. RESULTS: The aggregated variance, differenced aggregated variance, and aggregated absolute value methods presented the formation of three groups, whereas the Peng and R/S methods presented the formation of two groups. The aggregated variance method exhibited the best results with respect to the group formation between similar strains. CONCLUSION: The evaluation of Hurst exponent associated with discrete non-decimated wavelet transform can be used as a measure of similarity between genome sequences, thus leading to a refinement in the analysis.

Ventricular arrhythmias in the Chagas disease are not random phenomena: Long-term monitoring in Chagas arrhythmias.

BACKGROUND: Variability of ventricular arrhythmias among days in patients with Chagas disease is not detected by 24 hours of Holter monitoring. OBJECTIVE: To analyze whether ventricular arrhythmias are a random phenomenon or have a reproducible behavior in patients with Chagas cardiomyopathy. METHOD: Holter monitoring was recorded in 16 subjects with a mean age of 52 ± 8 years. They were clinically stable and had ventricular couplets, isolated premature ventricular contractions (PVCs), and nonsustained ventricular tachycardia (NSVT). The recordings occurred for 7 days. Hurst exponent (HE) evaluated randomness and predictability index (PI) and repeated analysis of variance (ANOVA) assessed reproducibility. RESULTS: The HE was significantly greater than 0.5 in all 16 patients, which confirms the nonrandomness of arrhythmias in this Chagas sample. The PI for ventricular couplets and isolated PVCs was, on average, 38% and 54%, respectively. ANOVA with repeated measurement showed significant differences in the daily frequency of ventricular couplets (n = 15, P ≤ .05), isolated PVC (n = 12, P ≤ .05), and NSVT (n = 7, P ≤ .05). CONCLUSION: Ventricular arrhythmias in Chagas cardiomyopathy are not random. Dissimilarities in arrhythmias frequency make unlikely that 24 hours of Holter recording can capture this variability.

A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer's disease using EEG signals.

BACKGROUND: EEG signals obtained from Mild Cognitive Impairment (MCI) and the Alzheimer's disease (AD) patients are visually indistinguishable. NEW METHOD: A new methodology is presented for differential diagnosis of MCI and the AD through adroit integration of a new signal processing technique, the integrated multiple signal classification and empirical wavelet transform (MUSIC-EWT), different nonlinear features such as fractality dimension (FD) from the chaos theory, and a classification algorithm, the enhanced probabilistic neural network model of Ahmadlou and Adeli using the EEG signals. RESULTS: Three different FD measures are investigated: Box dimension (BD), Higuchi's FD (HFD), and Katz's FD (KFD) along with another measure of the self-similarities of the signals known as the Hurst exponent (HE). The accuracy of the proposed method was verified using the monitored EEG signals from 37 MCI and 37 AD patients. COMPARISON WITH EXISTING METHODS: The proposed method is compared with other methodologies presented in the literature recently. CONCLUSIONS: It was demonstrated that the proposed method, MUSIC-EWT algorithm combined with nonlinear features BD and HE, and the EPNN classifier can be employed for differential diagnosis of MCI and AD patients with an accuracy of 90.3%.

A Stochastic Phylogenetic Algorithm for Mitochondrial DNA Analysis.

This paper presents an exploratory analysis of the mitochondrial DNA (mtDNA) of 32 species in the subphylum Vertebrata, divided in 7 taxonomic classes. Multiple stochastic parameters, such as the Hurst and detrended fluctuation analysis (DFA) exponents, Shannon entropy, and Chargaff ratio are computed for each DNA sequence. The biological interpretation of these parameters leads to defining a triplet of novel indices. These new functions incorporate the long-range correlations, the probability of occurrence of nucleic bases, and the ratio of pyrimidines-to-purines. Results suggest that relevant regions in mtDNA can be located using the proposed indices. Furthermore, early results from clustering algorithms indicate that the indices introduced might be useful in phylogenetic studies.

[An improved electroencephalogram feature extraction algorithm and its application in emotion recognition].

The result of the emotional state induced by music may provide theoretical support and help for assisted music therapy. The key to assessing the state of emotion is feature extraction of the emotional electroencephalogram (EEG). In this paper, we study the performance optimization of the feature extraction algorithm. A public multimodal database for emotion analysis using physiological signals (DEAP) proposed by Koelstra et al. was applied. Eight kinds of positive and negative emotions were extracted from the dataset, representing the data of fourteen channels from the different regions of brain. Based on wavelet transform, δ, θ, α and ß rhythms were extracted. This paper analyzed and compared the performances of three kinds of EEG features for emotion classification, namely wavelet features (wavelet coefficients energy and wavelet entropy), approximate entropy and Hurst exponent. On this basis, an EEG feature fusion algorithm based on principal component analysis (PCA) was proposed. The principal component with a cumulative contribution rate more than 85% was retained, and the parameters which greatly varied in characteristic root were selected. The support vector machine was used to assess the state of emotion. The results showed that the average accuracy rates of emotional classification with wavelet features, approximate entropy and Hurst exponent were respectively 73.15%, 50.00% and 45.54%. By combining these three methods, the features fused with PCA possessed an accuracy of about 85%. The obtained classification accuracy by using the proposed fusion algorithm based on PCA was improved at least 12% than that by using single feature, providing assistance for emotional EEG feature extraction and music therapy.

Arrhythmia detection from heart rate variability by SDFA method.

The smoothed detrended fluctuation analysis (SDFA) based on DFA and the principal of wavelet shrinkage procedures is a scaling analysis method to represent the correlation properties of a time series. Since there is not a specific rule for the choice of the numbers of regressors in SDFA Method, we present here an asymptotic optimal choice. We carried out some Monte Carlo simulations on fractional Gaussian noise (FGN) models, to investigate the effect of the numbers of regressors in SDFA Method. We analyze the long dependence property in view of the SDFA method to compare 10 healthy and 10 unhealthy (with cardiac arrhythimia) RR time series randomly selected from databases of the PhysioBank. It is proposed that utilizing Hurst estimator by SDFA method, as an additional diagnostic tool may provide an indication of cardiac arrhythmia.

Leucoencefalitis hemorrágica aguda: manifestaciones clínicas, hallazgos radiológicos y neuropatológicos / Acute hemorrhagic leukoencephalitis: clinical presentation, radiologic and neuropathologic features

La Leucoencefalitis Hemorrágica Aguda o enfermedad de Hurst es una enfermedad rara, caracterizada clínicamente por inicio súbito, curso clínico severo, usualmente fatal que se presenta posterior a una infección viral o vacunación. Patológicamente se caracteriza por desmielinización perivenular y necrosis hemorrágica difusa del sistema nervioso central. Se considera que representa una forma hiperaguda y severa de la Encefalomielitis Aguda Diseminada, la cual es una entidad inflamatoria con una base fisiopatológica autoinmune postinfecciosa. A continuación, se expone el caso de una paciente adulta, que ingresó al servicio de urgencias con cuadro clínico típico de migraña y antecedente de cefaleas previas de similares características. Quien doce horas posterior a su ingreso desarrolló de forma rápidamente progresiva depresión del estado de conciencia, signos neurológicos focales y signos de hipertensión de fosa posterior, que llevaron a desenlace fatal en tan solo 96 horas del inicio del cuadro clínico con hallazgos patológicos postmortem que confirman leucoencefalitis hemorrágica aguda. Se revisan las características clínicas, los hallazgos radiológicos y patológicos de esta entidad clínico-patológica poco común.

Acute hemorrhagic leukoencephalitis or Hurst disease is a rare disorder characterized by its severe neurological involvement, rapid progression and fatal outcome in a few days. The disease is usually a post infectious condition. Under microscope, it is identified by a perivenular demyelination and a diffuse hemorrhagic necrosis. This entity is thought to represent a hyperacute severe form of acute disseminated encephalomyelitis, which is an inflammatory autoimmune post infectious disorder. We describe the case of an adult woman, who visits the emergency room with migraine-like symptoms and a previous clinical history of similar headaches. Twelve hours later she developed focal neurologic findings, stupor and signs of endocraneal hypertension, her clinical status continued to worsen and in 96 hours she succumbed. The autopsy confirm acute hemorrhagic Leukoencephalitis. Reviewed clinical, radiological and pathological characteristics of this uncommon disease.