Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals.

Epilepsy is a disease that decreases the quality of life of patients; it is also among the most common neurological diseases. Several studies have approached the classification and prediction of seizures by using electroencephalographic data and machine learning techniques. A large diversity of features has been extracted from electroencephalograms to perform classification tasks; therefore, it is important to use feature selection methods to select those that leverage pattern recognition. In this study, the performance of a set of feature selection methods was compared across different classification models; the classification task consisted of the detection of ictal activity from the CHB-MIT and Siena Scalp EEG databases. The comparison was implemented for different feature sets and the number of features. Furthermore, the similarity between selected feature subsets across classification models was evaluated. The best F1-score (0.90) was reported by the K-nearest neighbor along with the CHB-MIT dataset. Results showed that none of the feature selection methods clearly outperformed the rest of the methods, as the performance was notably affected by the classifier, dataset, and feature set. Two of the combinations (classifier/feature selection method) reporting the best results were K-nearest neighbor/support vector machine and random forest/embedded random forest.

Effect of Auditory Discrimination Therapy on Attentional Processes of Tinnitus Patients.

Tinnitus is an auditory condition that causes humans to hear a sound anytime, anywhere. Chronic and refractory tinnitus is caused by an over synchronization of neurons. Sound has been applied as an alternative treatment to resynchronize neuronal activity. To date, various acoustic therapies have been proposed to treat tinnitus. However, the effect is not yet well understood. Therefore, the objective of this study is to establish an objective methodology using electroencephalography (EEG) signals to measure changes in attentional processes in patients with tinnitus treated with auditory discrimination therapy (ADT). To this aim, first, event-related (de-) synchronization (ERD/ERS) responses were mapped to extract the levels of synchronization related to the auditory recognition event. Second, the deep representations of the scalograms were extracted using a previously trained Convolutional Neural Network (CNN) architecture (MobileNet v2). Third, the deep spectrum features corresponding to the study datasets were analyzed to investigate performance in terms of attention and memory changes. The results proved strong evidence of the feasibility of ADT to treat tinnitus, which is possibly due to attentional redirection.

The analysis of EEG coherence reflects middle childhood differences in mathematical achievement.

Symbolic numerical magnitude processing is crucial to arithmetic development, and it is thought to be supported by the functional activation of several brain-interconnected structures. In this context, EEG beta oscillations have been recently associated with attention and working memory processing that underlie math achievement. Due to that EEG coherence represents a useful measure of brain functional connectivity, we aimed to contrast the EEG coherence in forty 8-to-9-year-old children with different math skill levels (High: HA, and Low achievement: LA) according to their arithmetic scores in the Fourth Edition of the Wide Range Achievement Test (WRAT-4) while performing a symbolic magnitude comparison task (i.e. determining which of two numbers is numerically larger). The analysis showed significantly greater coherence over the right hemisphere in the two groups, but with a distinctive connectivity pattern. Whereas functional connectivity in the HA group was predominant in parietal areas, especially involving beta frequencies, the LA group showed more extensive frontoparietal relationships, with higher participation of delta, theta and alpha band frequencies, along with a distinct time-frequency domain expression. The results seem to reflect that lower math achievements in children mainly associate with cognitive processing steps beyond stimulus encoding, along with the need of further attentional resources and cognitive control than their peers, suggesting a lower degree of numerical processing automation.

Evaluation of the Relation between Ictal EEG Features and XAI Explanations.

Epilepsy is a neurological disease with one of the highest rates of incidence worldwide. Although EEG is a crucial tool for its diagnosis, the manual detection of epileptic seizures is time consuming. Automated methods are needed to streamline this process; although there are already several works that have achieved this, the process by which it is executed remains a black box that prevents understanding of the ways in which machine learning algorithms make their decisions. A state-of-the-art deep learning model for seizure detection and three EEG databases were chosen for this study. The developed models were trained and evaluated under different conditions (i.e., three distinct levels of overlap among the chosen EEG data windows). The classifiers with the best performance were selected, then Shapley Additive Explanations (SHAPs) and Local Interpretable Model-Agnostic Explanations (LIMEs) were employed to estimate the importance value of each EEG channel and the Spearman's rank correlation coefficient was computed between the EEG features of epileptic signals and the importance values. The results show that the database and training conditions may affect a classifier's performance. The most significant accuracy rates were 0.84, 0.73, and 0.64 for the CHB-MIT, Siena, and TUSZ EEG datasets, respectively. In addition, most EEG features displayed negligible or low correlation with the importance values. Finally, it was concluded that a correlation between the EEG features and the importance values (generated by SHAP and LIME) may have been absent even for the high-performance models.

Pneumonia and Pulmonary Thromboembolism Classification Using Electronic Health Records.

Pneumonia and pulmonary thromboembolism (PTE) are both respiratory diseases; their diagnosis is difficult due to their similarity in symptoms, medical subjectivity, and the large amount of information from different sources necessary for a correct diagnosis. Analysis of such clinical data using computational tools could help medical staff reduce time, increase diagnostic certainty, and improve patient care during hospitalization. In addition, no studies have been found that analyze all clinical information on the Mexican population in the Spanish language. Therefore, this work performs automatic diagnosis of pneumonia and pulmonary thromboembolism using machine-learning tools along with clinical laboratory information (structured data) and clinical text (unstructured data) obtained from electronic health records. A cohort of 173 clinical records was obtained from the Mexican Social Security Institute. The data were preprocessed, transformed, and adjusted to be analyzed using several machine-learning algorithms. For structured data, naïve Bayes, support vector machine, decision trees, AdaBoost, random forest, and multilayer perceptron were used; for unstructured data, a BiLSTM was used. K-fold cross-validation and leave-one-out were used for evaluation of structured data, and hold-out was used for unstructured data; additionally, 1-vs.-1 and 1-vs.-rest approaches were used. Structured data results show that the highest AUC-ROC was achieved by the naïve Bayes algorithm classifying PTE vs. pneumonia (87.0%), PTE vs. control (75.1%), and pneumonia vs. control (85.2%) with the 1-vs.-1 approach; for the 1-vs.-rest approach, the best performance was reported in pneumonia vs. rest (86.3%) and PTE vs. rest (79.7%) using naïve Bayes, and control vs. diseases (79.8%) using decision trees. Regarding unstructured data, the results do not present a good AUC-ROC; however, the best F1-score were scored for control vs. disease (72.7%) in the 1-vs.-rest approach and control vs. pneumonia (63.6%) in the 1-to-1 approach. Additionally, several decision trees were obtained to identify important attributes for automatic diagnosis for structured data, particularly for PTE vs. pneumonia. Based on the experiments, the structured datasets present the highest values. Results suggest using naïve Bayes and structured data to automatically diagnose PTE vs. pneumonia. Moreover, using decision trees allows the observation of some decision criteria that the medical staff could consider for diagnosis.

Frequency spectra characterization of noncoding human genomic sequences.

BACKGROUND: Noncoding sequences have been demonstrated to possess regulatory functions. Its classification is challenging because they do not show well-defined nucleotide patterns that can correlate with their biological functions. Genomic signal processing techniques like Fourier transform have been employed to characterize coding and noncoding sequences. This transformation in a systematic whole-genome noncoding library, such as the ENCODE database, can provide evidence of a periodic behaviour in the noncoding sequences that correlates with their regulatory functions. OBJECTIVE: The objective of this study was to classify different noncoding regulatory regions through their frequency spectra. METHODS: We computed machine learning algorithms to classify the noncoding regulatory sequences frequency spectra. RESULTS: The sequences from different regulatory regions, cell lines, and chromosomes possessed distinct frequency spectra, and that machine learning classifiers (such as those of the support vector machine type) could successfully discriminate among regulatory regions, thus correlating the frequency spectra with their biological functions CONCLUSION: Our work supports the idea that there are patterns in the noncoding sequences of the genome.

A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics.

Recent studies aiming to facilitate mathematical skill development in primary school children have explored the electrophysiological characteristics associated with different levels of arithmetic achievement. The present work introduces an alternative EEG signal characterization using graph metrics and, based on such features, a classification analysis using a decision tree model. This proposal aims to identify group differences in brain connectivity networks with respect to mathematical skills in elementary school children. The methods of analysis utilized were signal-processing (EEG artifact removal, Laplacian filtering, and magnitude square coherence measurement) and the characterization (Graph metrics) and classification (Decision Tree) of EEG signals recorded during performance of a numerical comparison task. Our results suggest that the analysis of quantitative EEG frequency-band parameters can be used successfully to discriminate several levels of arithmetic achievement. Specifically, the most significant results showed an accuracy of 80.00% (α band), 78.33% (δ band), and 76.67% (θ band) in differentiating high-skilled participants from low-skilled ones, averaged-skilled subjects from all others, and averaged-skilled participants from low-skilled ones, respectively. The use of a decision tree tool during the classification stage allows the identification of several brain areas that seem to be more specialized in numerical processing.

A unified weighted minimum norm solution for the reference inverse problem in EEG.

A well known problem in EEG recordings deals with the unknown potential of the reference electrode. In the last years several authors presented comparisons among the most popular solutions, the global conclusion being that the traditional Average Reference (AR) and the Reference Standardization Technique (REST) are the best approximations (Nunez, 2010; Kayser and Tenke, 2010; Liu et al., 2015; Chella et al., 2016). In this work we do not aim to further compare these techniques but to support the fact that both solutions can be derived from a general inverse problem formalism for reference estimation (Hu et al., 2019; Hu et al., 2018; Salido-Ruiz et al., 2011). Using the alternative approach of least squares, our findings are consistent with the theoretical findings in Hu et al. (2019) and Hu et al. (2018) showing that the AR is the minimum norm solution, while REST is a weighted minimum norm including some approximate propagation model. AR is thus a particular case of REST, which itself uses a particular formulation of the source estimation inverse problem. With a different derivation, we provide the additional powerful evidences to reinforce the cited findings.

Genomic signal processing for DNA sequence clustering.

Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.

Neuromarkers based on EEG Statistics in Time and Frequency Domains to Detect Tinnitus / Neuromarcadores basados en Estadística de EEG en el Dominio Temporal y Frecuencial para Detectar Acufeno

Abstract: Tinnitus detection and characterization requires a carefully elaborated diagnosis mainly owing to its heterogeneity nature. The present investigation aims to find features in Electroencephalographic (EEG) signals from time and frequency domain analysis that could distinguish between healthy and tinnitus sufferers with different levels of hearing loss. For this purpose, 24 volunteers were recruited and equally divided into four groups: 1) controls, 2) slow tinnitus, 3) middle tinnitus and 4) high tinnitus. EEG signals were registered in two states, with eyes closed and opened for 60 seconds. EEG analysis was focused on two bandwidths: delta and alpha band. For time domain, the EEG features estimated were mean, standard deviation, kurtosis, maximum peak, skewness and shape. For frequency domain, the EEG features obtained were mean, skewness, power spectral density. Normality of EEG data was evaluated by the Lilliefors test, and as a result, the nonparametric technique Kruskal-Wallis H statistic to test significance was applied. Results show that EEG features are more differentiable between tinnitus sufferers and controls in frequency domain than in time domain. EEG features from tinnitus patients with high HL are significantly different from the rest of the groups in alpha frequency band activity when shape and skewness are computed.

Resumen: La detección y caracterización del acúfeno requiere un diagnóstico cuidadosamente elaborado debido principalmente a su naturaleza heterogénea. La presente investigación tiene como objetivo encontrar características en las señales electroencefalográficas (EEG) a partir del análisis del dominio del tiempo y frecuencia que podrían distinguir entre pacientes sanos y con acúfeno con diferentes niveles de pérdida auditiva. Para ello, se reclutaron 24 voluntarios y se dividieron por igual en cuatro grupos: 1) controles, 2) acúfeno bajo, 3) acúfeno medio y 4) acufeno alto. La actividad EEG se registró en reposo en dos condiciones: ojos cerrados y abiertos durante un minuto. El análisis de EEG se centró en anchos de banda delta y alfa. Para el dominio del tiempo, las características del EEG estimadas fueron la media, la desviación estándar, la curtosis, el pico máximo, la asimetría y la forma. Para el dominio de la frecuencia, las características de EEG obtenidas fueron media, asimetría, densidad espectral de potencia. La normalidad de los datos del EEG se evaluó mediante la prueba de Lilliefors y, como resultado, se aplicó la técnica no paramétrica del estadístico H de Kruskal-Wallis para probar la significación. Los resultados muestran que las características del EEG son más diferenciables entre los pacientes con acúfeno y los controles en el dominio de la frecuencia que en el dominio del tiempo. Las características del EEG de los pacientes con acúfeno con alta pérdida de audición son significativamente diferentes del resto de los grupos en la actividad de la banda de alfa cuando se calculan la forma y la asimetría.

Dataset on the EEG time-frequency representation in children with different levels of mathematical achievement.

This article presents the data related to the research paper entitled "The analysis of EEG coherence reflects middle childhood differences in mathematical achievement" (González-Garrido et al., 2018). The dataset is derived from the electroencephalographic (EEG) records registered from a total of 60 8-9-years-old children with different math skill levels (High: HA, Average: AA, and Low Achievement: LA) while performing a symbolic magnitude comparison task. The average brain patterns are shown through Time-Frequency Representations (TFR) for each group, and also grand-mean amplitudes within specific EEG epochs in a 19-electrode array are provided. Making this information publicly available for further analyses could significantly contribute to a better understanding on how math achievement in children associates with cognitive processing strategies.

On DNA numerical representations for genomic similarity computation.

Genomic signal processing (GSP) refers to the use of signal processing for the analysis of genomic data. GSP methods require the transformation or mapping of the genomic data to a numeric representation. To date, several DNA numeric representations (DNR) have been proposed; however, it is not clear what the properties of each DNR are and how the selection of one will affect the results when using a signal processing technique to analyze them. In this paper, we present an experimental study of the characteristics of nine of the most frequently-used DNR. The objective of this paper is to evaluate the behavior of each representation when used to measure the similarity of a given pair of DNA sequences.

Vibrotactile Discrimination Training Affects Brain Connectivity in Profoundly Deaf Individuals.

Early auditory deprivation has serious neurodevelopmental and cognitive repercussions largely derived from impoverished and delayed language acquisition. These conditions may be associated with early changes in brain connectivity. Vibrotactile stimulation is a sensory substitution method that allows perception and discrimination of sound, and even speech. To clarify the efficacy of this approach, a vibrotactile oddball task with 700 and 900 Hz pure-tones as stimuli [counterbalanced as target (T: 20% of the total) and non-target (NT: 80%)] with simultaneous EEG recording was performed by 14 profoundly deaf and 14 normal-hearing (NH) subjects, before and after a short training period (five 1-h sessions; in 2.5-3 weeks). A small device worn on the right index finger delivered sound-wave stimuli. The training included discrimination of pure tone frequency and duration, and more complex natural sounds. A significant P300 amplitude increase and behavioral improvement was observed in both deaf and normal subjects, with no between group differences. However, a P3 with larger scalp distribution over parietal cortical areas and lateralized to the right was observed in the profoundly deaf. A graph theory analysis showed that brief training significantly increased fronto-central brain connectivity in deaf subjects, but not in NH subjects. Together, ERP tools and graph methods depicted the different functional brain dynamic in deaf and NH individuals, underlying the temporary engagement of the cognitive resources demanded by the task. Our findings showed that the index-fingertip somatosensory mechanoreceptors can discriminate sounds. Further studies are necessary to clarify brain connectivity dynamics associated with the performance of vibrotactile language-related discrimination tasks and the effect of lengthier training programs.

Electrophysiological dynamic brain connectivity during symbolic magnitude comparison in children with different mathematics achievement levels.

Children with mathematical difficulties usually have an impaired ability to process symbolic representations. Functional MRI methods have suggested that early frontoparietal connectivity can predict mathematic achievements; however, the study of brain connectivity during numerical processing remains unexplored. With the aim of evaluating this in children with different math proficiencies, we selected a sample of 40 children divided into two groups [high achievement (HA) and low achievement (LA)] according to their arithmetic scores in the Wide Range Achievement Test, 4th ed.. Participants performed a symbolic magnitude comparison task (i.e. determining which of two numbers is numerically larger), with simultaneous electrophysiological recording. Partial directed coherence and graph theory methods were used to estimate and depict frontoparietal connectivity in both groups. The behavioral measures showed that children with LA performed significantly slower and less accurately than their peers in the HA group. Significantly higher frontocentral connectivity was found in LA compared with HA; however, when the connectivity analysis was restricted to parietal locations, no relevant group differences were observed. These findings seem to support the notion that LA children require greater memory and attentional efforts to meet task demands, probably affecting early stages of symbolic comparison.