Epilepsy identification based on EEG signal using RQA method

The influence of different tree densities on alpha waves, physical activity enjoyment, and satisfaction of late middle-aged and older adults using virtual cycling

Cave automatic virtual environment (CAVE), as a novel approach to promoting physical activity, shows great potential for improving the mental health and healthy lifestyle of older adults. Based on stress reduction theory, tree density is regarded as a main characteristic of a virtual sportscape that will affect the experience and benefits of exercising. However, the effect of tree density on the experience of exercising remains unclear. The current study was undertaken to investigate the effects of tree cover density on the alpha waves induced and the enjoyment and satisfaction derived by engaging in physical activity in a virtual environment. Eighty-seven late middle-aged and older adults were randomly assigned to one of the following conditions: a high tree density sportscape (HTDS = 36-60 %), a medium tree density sportscape, (MTDS = 20-35 %), and a control condition. Questionnaires and electroencephalogram read-outs of alpha waves were used to evaluate the changes in stress levels experienced by the participants before, during, and after 20 min of cycling. The results showed that participants exposed to an HTDS exhibited to physical activity with significantly more enjoyment and satisfaction than those in the MTDS and control groups. In contrast, the highest degree of relaxation was exhibited in the MTDS condition, suggesting that an MTDS is more effective at reducing perceived stress among late middle-aged and older adults engaging in virtual cycling. These findings demonstrate that exercising in a virtual reality setting with different densities of tree cover comes with physiological and psychological wellbeing for late middle-aged and older adults.

Quantifying instability in neurological disorders EEG based on phase space DTM function

Classifying individuals with neurological disorders and healthy subjects using EEG is a crucial area of research. The current feature extraction approach focuses on the frequency domain features in each of the EEG frequency bands and functional brain networks. In recent years, researchers have discovered and extensively studied stability differences in the electroencephalograms (EEG) of patients with neurological disorders. Based on this, this paper proposes a feature descriptor to characterize EEG instability. The proposed method starts by forming a signal point cloud through Phase Space Reconstruction (PSR). Subsequently, a pseudo-metric space is constructed, and pseudo-distances are calculated based on the consistent measure of the point cloud. Finally, Distance to Measure (DTM) Function are generated to replace the distance function in the original metric space. We calculated the relative distances in the point cloud by measuring signal similarity and, based on this, summarized the point cloud structures formed by EEG with different stabilities after PSR. This process demonstrated that Multivariate Kernel Density Estimation (MKDE) based on a Gaussian kernel can effectively separate the mappings of different stable components within the signal in the phase space. The two average DTM values are then proposed as feature descriptors for EEG instability.In the validation phase, the proposed feature descriptor is tested on three typical neurological disorders: epilepsy, Alzheimer's disease, and Parkinson's disease, using the Bonn dataset, CHB-MIT, the Florida State University dataset, and the Iowa State University dataset. DTM values are used as feature inputs for four different machine learning classifiers, and The results show that the best classification accuracy of the proposed method reaches 98.00 %, 96.25 %, 96.71 % and 95.34 % respectively, outperforming commonly used nonlinear descriptors. Finally, the proposed method is tested and analyzed using noisy signals, demonstrating its robustness compared to other methods.

Machine learning approach to detect dynamical states from recurrence measures

We integrate machine learning approaches with nonlinear time series analysis, specifically utilizing recurrence measures to classify various dynamical states emerging from time series. We implement three machine learning algorithms: Logistic Regression, Random Forest, and Support Vector Machine for this study. The input features are derived from the recurrence quantification of nonlinear time series and characteristic measures of the corresponding recurrence networks. For training and testing, we generate synthetic data from standard nonlinear dynamical systems and evaluate the efficiency and performance of the machine learning algorithms in classifying time series into periodic, chaotic, hyperchaotic, or noisy categories. Additionally, we explore the significance of input features in the classification scheme and find that the features quantifying the density of recurrence points are the most relevant. Furthermore, we illustrate how the trained algorithms can successfully predict the dynamical states of two variable stars, SX Her and AC Her, from the data of their light curves. We also indicate how the algorithms can be trained to classify data from discrete systems.

Time course of EEG complexity reflects attentional engagement during listening to speech in noise

Auditory distractions are recognized to considerably challenge the quality of information encoding during speech comprehension. This study explores electroencephalography (EEG) microstate dynamics in ecologically valid, noisy settings, aiming to uncover how these auditory distractions influence the process of information encoding during speech comprehension. We examined three listening scenarios: (1) speech perception with background noise (LA), (2) focused attention on the background noise (BA), and (3) intentional disregard of the background noise (BUA). Our findings showed that microstate complexity and unpredictability increased when attention was directed towards speech compared with tasks without speech (LA > BA & BUA). Notably, the time elapsed between the recurrence of microstates increased significantly in LA compared with both BA and BUA. This suggests that coping with background noise during speech comprehension demands more sustained cognitive effort. Additionally, a two-stage time course for both microstate complexity and alpha-to-theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the initial stage is primarily driven by sensory processes and information gathering, while the second stage involves higher level cognitive engagement, including mnemonic binding and memory encoding.

Region-Wise Brain Response Classification of ASD Children Using EEG and BiLSTM RNN

AbstractAutism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in sensory modulation. These sensory modulation deficits would ultimately lead them to difficulties in adaptive behavior and intellectual functioning. The purpose of this study was to observe changes in the nervous system with responses to auditory/visual and only audio stimuli in children with autism and typically developing (TD) through electroencephalography (EEG). In this study, 20 children with ASD and 20 children with TD were considered to investigate the difference in the neural dynamics. The neural dynamics could be understood by non-linear analysis of the EEG signal. In this research to reveal the underlying nonlinear EEG dynamics, recurrence quantification analysis (RQA) is applied. RQA measures were analyzed using various parameter changes in RQA computations. In this research, the cosine distance metric was considered due to its capability of information retrieval and the other distance metrics parameters are compared for identifying the best biomarker. Each computational combination of the RQA measure and the responding channel was analyzed and discussed. To classify ASD and TD, the resulting features from RQA were fed to the designed BiLSTM (bi-long short-term memory) network. The classification accuracy was tested channel-wise for each combination. T3 and T5 channels with neighborhood selection as FAN (fixed amount of nearest neighbors) and distance metric as cosine is considered as the best-suited combination to discriminate between ASD and TD with the classification accuracy of 91.86%, respectively.

An automated drug dependence detection system based on EEG

OBJECTIVE

Substance abuse causes damage to the brain structure and function. This research aim is to design an automated drug dependence detection system based on EEG signals in a Multidrug (MD) abuser.

METHODS

EEG signals were recorded from participants categorized into MD-dependents (n = 10) and Healthy Control (HC) (n = 12). The Recurrence Plot investigates the dynamic characteristics of the EEG signal. The entropy index (ENTR) measured from the Recurrence Quantification Analysis was considered the complexity index of the delta, theta, alpha, beta, gamma, and all-band EEG signals. Statistical analysis was performed by t-test. The support vector machine technique was used for the data classification.

RESULTS

The results show decreased ENTR indices in the delta, alpha, beta, gamma, and all-band EEG signal and increased theta band in MD abusers compared to the HC group. That indicated the reduction of complexity in the delta, alpha, beta, gamma, and all-band EEG signals in the MD group. Additionally, the SVM classifier distinguished the MD group from the HC group with 90% accuracy, 89.36% sensitivity, 90.7% specificity, and 89.8% F1 score.

CONCLUSIONS AND SIGNIFICANCE

The nonlinear analysis of brain data was used to build an automatic diagnostic aid system that could identify HC people apart from those who abuse MD.

Recurrence quantification analysis of isokinetic strength tests: A comparison of the anterior cruciate ligament reconstructed and the uninjured limb

BACKGROUND

Despite widespread use of return to sport testing following anterior cruciate ligament reconstruction, studies suggest inadequacy in current testing criteria, such as limb symmetry index calculations, to determine athletes' readiness to return to play. Recurrence quantification analysis, an emerging non-linear data analysis tool, may reveal subtle neuromuscular differences between the injured and uninjured limb that are not captured by traditional testing. We hypothesized that isokinetic torque curve data of the injured limb would demonstrate lower determinism and entropy as compared to the uninjured limb.

METHODS

102 patients (44 M, 58F, 10 ± 1 months post-anterior cruciate ligament reconstruction) underwent isokinetic quadriceps strength testing using a HumacNorm dynamometer. Patients completed maximum effort knee extension and flexion at 60°/sec. Data were post-processed with a MATLAB CRQA Graphical User Interface and determinism and entropy values were extracted. Paired-sample t-tests (α = 0.05) were used to compare data from the injured and uninjured limb.

FINDINGS

Determinism and entropy values in the torque curves were lower in the injured limb than the uninjured limb (p < 0.001). Our findings indicate there is less predictability and complexity present in the torque signals of injured limbs.

INTERPRETATION

Recurrence quantification analysis can be used to assess neuromuscular differences between limbs in patients who have undergone anterior cruciate ligament reconstruction. Our findings offer further evidence that there are changes to the neuromuscular system which persist following reconstruction. Further investigation is needed to establish thresholds of determinism and entropy values needed for safe return to sport and to evaluate the utility of recurrence quantification analysis as a return to sport criterion.

Complexity analysis of heartbeat-related signals in brain MRI time series as a potential biomarker for ageing and cognitive performance

Getting older affects both the structure of the brain and some cognitive capabilities. Until now, magnetic resonance imaging (MRI) approaches have been unable to give a coherent reflection of the cognitive declines. It shows the limitation of the contrast mechanisms used in most MRI investigations, which are indirect measures of brain activities depending on multiple physiological and cognitive variables. However, MRI signals may contain information of brain activity beyond these commonly used signals caused by the neurovascular response. Here, we apply a zero-spin echo (ZSE) weighted MRI sequence, which can detect heartbeat-evoked signals (HES). Remarkably, these MRI signals have properties only known from electrophysiology. We investigated the complexity of the HES arising from this sequence in two age groups; young (18-29 years) and old (over 65 years). While comparing young and old participants, we show that the complexity of the HES decreases with age, where the stability and chaoticity of these HES are particularly sensitive to age. However, we also found individual differences which were independent of age. Complexity measures were related to scores from different cognitive batteries and showed that higher complexity may be related to better cognitive performance. These findings underpin the affinity of the HES to electrophysiological signals. The profound sensitivity of these changes in complexity shows the potential of HES for understanding brain dynamics that need to be tested in more extensive and diverse populations with clinical relevance for all neurovascular diseases.

Supplementary Information

The online version contains supplementary material available at 10.1140/epjs/s11734-022-00696-2.

Multi-Level Classification of Lung Pathologies in Neonates using Recurrence Features

The use of Lung Ultrasound (LUS) as a tool to diagnose and monitor lung diseases in neonates has increased in urban hospitals. LUS's main advantages compared to chest CT or X-rays is that it is less expensive, more accessible, and does not expose the patient to radiation. Performing LUS on neonates and diagnosing the LUS images require highly trained medical professional and clinicians. While availability of such specialists in general is not an issue in urban areas, there is lack of such personnel in rural and remote communities. Hence, an automated computer-aided screening approach as a first level diagnosis assistance in such scenarios might be of significant value. Many of the image morphologies used by clinicians in diagnosing the LUS have strong recurrence characteristics. Building upon this knowledge, in this paper, we propose a feature extraction method designed to quantify such recurrent features for classification of LUS images into 6 common neonatal lung conditions. These conditions were normal lung, chronic lung disease (CLD), transient tachypnea of the newborn (TTN), pneumothorax (PTX), respiratory distress syndrome (RDS), and consolidation (CON) that could be pneumonia or atelectasis. The proposed method extracts virtual scanlines from the LUS images and converts them into signals. Then using recurrence quantification analysis (RQA), features were extracted and fed to pattern classifiers. Using a simple linear classifier the proposed features can achieve a classification accuracy of 69.3% without clinical features and 77.6% with clinical features. Clinical Relevance- Development of an automated computer-aided screening tool for first level diagnosis assistance in neonatal LUS pathologies. Such a tool will be of significant value in rural and remote medical communities.

Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Experts usually inspect electroencephalogram (EEG) recordings page-by-page in order to identify epileptic seizures, which leads to heavy workloads and is time consuming. However, the efficient extraction and effective selection of informative EEG features is crucial in assisting clinicians to diagnose epilepsy accurately. In this paper, a determinant of covariance matrix (Cov-Det) model is suggested for reducing EEG dimensionality. First, EEG signals are segmented into intervals using a sliding window technique. Then, Cov-Det is applied to each interval. To construct a features vector, a set of statistical features are extracted from each interval. To eliminate redundant features, the Kolmogorov-Smirnov (KST) and Mann-Whitney U (MWUT) tests are integrated, the extracted features ranked based on KST and MWUT metrics, and arithmetic operators are adopted to construe the most pertinent classified features for each pair in the EEG signal group. The selected features are then fed into the proposed AdaBoost Back-Propagation neural network (AB_BP_NN) to effectively classify EEG signals into seizure and free seizure segments. Finally, the AB_BP_NN is compared with several classical machine learning techniques; the results demonstrate that the proposed mode of AB_BP_NN provides insignificant false positive rates, simpler design, and robustness in classifying epileptic signals. Two datasets, the Bern-Barcelona and Bonn datasets, are used for performance evaluation. The proposed technique achieved an average accuracy of 100% and 98.86%, respectively, for the Bern-Barcelona and Bonn datasets, which is considered a noteworthy improvement compared to the current state-of-the-art methods.

Nasal Air Puff Promotes Default Mode Network Activity in Mechanically Ventilated Comatose Patients: A Noninvasive Brain Stimulation Approach

OBJECTIVES

Coma state and loss of consciousness are associated with impaired brain activity, particularly gamma oscillations, that integrate functional connectivity in neural networks, including the default mode network (DMN). Mechanical ventilation (MV) in comatose patients can aggravate brain activity, which has decreased in coma, presumably because of diminished nasal airflow. Nasal airflow, known to drive functional neural oscillations, synchronizing distant brain networks activity, is eliminated by tracheal intubation and MV. Hence, we proposed that rhythmic nasal air puffing in mechanically ventilated comatose patients may promote brain activity and improve network connectivity.

MATERIALS AND METHODS

We recorded electroencephalography (EEG) from 15 comatose patients (seven women) admitted to the intensive care unit because of opium poisoning and assessed the activity, complexity, and connectivity of the DMN before and during the nasal air-puff stimulation. Nasal cavity air puffing was done through a nasal cannula controlled by an electrical valve (open duration of 630 ms) with a frequency of 0.2 Hz (ie, 12 puff/min).

RESULTS

Our analyses demonstrated that nasal air puffing enhanced the power of gamma oscillations (30-100 Hz) in the DMN. In addition, we found that the coherence and synchrony between DMN regions were increased during nasal air puffing. Recurrence quantification and fractal dimension analyses revealed that EEG global complexity and irregularity, typically seen in wakefulness and conscious state, increased during rhythmic nasal air puffing.

CONCLUSIONS

Rhythmic nasal air puffing, as a noninvasive brain stimulation method, opens a new window to modifying the brain connectivity integration in comatose patients. This approach may potentially influence comatose patients' outcomes by increasing brain reactivity and network connectivity.

Automatic Diagnosis of Epileptic Seizures in EEG Signals Using Fractal Dimension Features and Convolutional Autoencoder Method

This paper proposes a new method for epileptic seizure detection in electroencephalography (EEG) signals using nonlinear features based on fractal dimension (FD) and a deep learning (DL) model. Firstly, Bonn and Freiburg datasets were used to perform experiments. The Bonn dataset consists of binary and multi-class classification problems, and the Freiburg dataset consists of two-class EEG classification problems. In the preprocessing step, all datasets were prepossessed using a Butterworth band pass filter with 0.5–60 Hz cut-off frequency. Then, the EEG signals of the datasets were segmented into different time windows. In this section, dual-tree complex wavelet transform (DT-CWT) was used to decompose the EEG signals into the different sub-bands. In the following section, in order to feature extraction, various FD techniques were used, including Higuchi (HFD), Katz (KFD), Petrosian (PFD), Hurst exponent (HE), detrended fluctuation analysis (DFA), Sevcik, box counting (BC), multiresolution box-counting (MBC), Margaos-Sun (MSFD), multifractal DFA (MF-DFA), and recurrence quantification analysis (RQA). In the next step, the minimum redundancy maximum relevance (mRMR) technique was used for feature selection. Finally, the k-nearest neighbors (KNN), support vector machine (SVM), and convolutional autoencoder (CNN-AE) were used for the classification step. In the classification step, the K-fold cross-validation with k = 10 was employed to demonstrate the effectiveness of the classifier methods. The experiment results show that the proposed CNN-AE method achieved an accuracy of 99.736% and 99.176% for the Bonn and Freiburg datasets, respectively.

Clinical efficacy and safety of removing blood stasis and removing phlegm in the treatment of epilepsy with cognitive impairment: A protocol for systematic review and meta-analysis

BACKGROUND

Epilepsy is a chronic encephalopathy caused by abnormal discharge of neurons in the brain, resulting in brain dysfunction. Cognitive impairment is one of the most common complications of epilepsy. The current treatment of epilepsy in the control of symptoms at the same time cause a lot of side effects, especially the aggravation of cognitive impairment. Many literatures have stated that the efficacy and safety of integrated traditional Chinese and western medicine in the treatment of epilepsy with cognitive impairment is superior to that of western medicine alone. In this systematic review, we intend to evaluate the clinical efficacy and safety of removing stasis and resolving phlegm in the treatment of epilepsy with cognitive impairment.

METHODS

We will search The Cochrane Library, EMbase, Pubmed, Web of Science, Chinese Journal Full-Text Database (CNKI), Wanfang Database, and VIP database. Simultaneously we will retrieval relevant meeting minutes, eligible research reference lists, symposium abstracts, and gray literatures. We will not apply any restrictions to the language and publication date. All randomized controlled trials about the efficacy and safety of removing blood stasis and phlegm in the treatment of epilepsy with cognitive impairment will be included. Two authors will independently carry out. Any objections will be worked out by a third author through consultation. We will use the Revman 5.3 and Stata 13.0 software for data synthesis, sensitivity analysis, meta regression, subgroup analysis, and risk of bias assessment. The grading of recommendations assessment, development, and evaluation standard will be used to evaluate the quality of evidence.

RESULTS

This systematic review will synthesize the data from the present eligible high quality randomized controlled trials to assess whether the treatment of removing blood stasis and phlegm is effective and safety for epilepsy with cognitive impairment from various evaluation aspects including clinical efficacy of epilepsy, EEG improvement rate, MOCA score, QOLIE-31 cognitive function score, traditional Chinese medicine symptom score, incidence of adverse reactions, frequency of seizures of epilepsy, and duration of seizure of epilepsy.

CONCLUSION

The systematic review will provide evidence to assess the efficacy and safety of removing blood stasis and phlegm in the treatment of patients with epilepsy with cognitive impairment.

PROSPERO REGISTRATION NUMBER

CRD42021224893.

Multiscale Permutation Lempel-Ziv Complexity Measure for Biomedical Signal Analysis: Interpretation and Application to Focal EEG Signals

This paper analyses the complexity of electroencephalogram (EEG) signals in different temporal scales for the analysis and classification of focal and non-focal EEG signals. Futures from an original multiscale permutation Lempel–Ziv complexity measure (MPLZC) were obtained. MPLZC measure combines a multiscale structure, ordinal analysis, and permutation Lempel–Ziv complexity for quantifying the dynamic changes of an electroencephalogram (EEG). We also show the dependency of MPLZC on several straight-forward signal processing concepts, which appear in biomedical EEG activity via a set of synthetic signals. The main material of the study consists of EEG signals, which were obtained from the Bern-Barcelona EEG database. The signals were divided into two groups: focal EEG signals (n = 100) and non-focal EEG signals (n = 100); statistical analysis was performed by means of non-parametric Mann–Whitney test. The mean value of MPLZC results in the non-focal group are significantly higher than those in the focal group for scales above 1 (p < 0.05). The result indicates that the non-focal EEG signals are more complex. MPLZC feature sets are used for the least squares support vector machine (LS-SVM) classifier to classify into the focal and non-focal EEG signals. Our experimental results confirmed the usefulness of the MPLZC method for distinguishing focal and non-focal EEG signals with a classification accuracy of 86%.

A nonlinear dynamical approach to analysis of emotions using EEG signals based on the Poincaré map function and recurrence plots

Dynamic variations of electroencephalogram (EEG) contain significant information in the study of human emotional states. Transient time methods are well suited to evaluate short-term dynamic changes in brain activity. Human affective states, however, can be more appropriately analyzed using chaotic dynamical techniques, in which temporal variations are considered over longer durations. In this study, we have applied two different recurrence-based chaotic schemes, namely the Poincaré map function and recurrence plots (RPs), to analyze the long-term dynamics of EEG signals associated with state space (SS) trajectory of the time series. Both approaches determine the system dynamics based on the Poincaré recurrence theorem as well as the trajectory divergence producing two-dimensional (2D) characteristic plots. The performance of the methods is compared with regard to their ability to distinguish between levels of valence, arousal, dominance and liking using EEG data from the "dataset for emotion analysis using physiological" database. The differences between the levels of emotional feelings were investigated based on the analysis of variance (ANOVA) test and Spearman's statistics. The results obtained from the RP features distinguish between the emotional ratings with a higher level of statistical significance as compared with those produced by the Poincaré map function. The scheme based on RPs was particularly advantageous in identifying the levels of dominance. Out of the 32 EEG electrodes examined, the RP-based approach distinguished the dominance levels in 23 electrodes, while the approach based on the Poincaré map function was only able to discriminate dominance levels in five electrodes. Furthermore, based on nonlinear analysis, significant correlations were observed over a wider area of the cortex for all affective states as compared with that reported based on the analysis of EEG power bands.

Which subject-related variables contribute to movement variability during a simulated repetitive and standardised occupational task? Recurrence quantification analysis of surface electromyographic signals

Movement variability is a component of human movement. This study applied recurrence quantification analysis (RQA) on electromyographic signals to determine the effects of two types of variables on movement variability during a short, simulated repetitive and standardised occupational clip-fitting task. The electrical activity of six muscles in the dominant upper limb was recorded in 21 participants. Variables related to the task performance (insertion force and movements performed when fitting clips) affected RQA measures: recurrence rate (RR), percentage of determinism (DET) and diagonal line length entropy (ENT). Variables related to participant's characteristics (sex, age, and BMI) affected only DET and ENT. A constrasting variability was observed such as a high-DET value combined with a high-ENT value and inversely. Variables affected mainly the recurrences organisation of the more distal muscles. Even if movement variability is complex, it should be considered by ergonomists and work place designers to better understanding of operators' movements. Practitioner summary: It is essential to consider the complexity of operators' movement variability to understand their activities. Based on intrinsic movement variability knowledge, ergonomists and work place designers will be able to modulate the movement variability by acting on workstation designs and occupational organisation with the aim of preserving operators' health. Abbreviations: RR: recurrence rate; DET: percentage of determinism; ENT: diagonal line length entropy; BMI: body mass index; FDS: flexor digitorum superficialis; EXT: extensor digitorum communis; BIC: biceps brachii; TRI: triceps brachii; DEL: deltoideus anterior; TRA: trapezius pars descendens; F: female; M: male; S: supinated; P: pronated; CM: continuous movement; DM: discontinuous movement.

A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks

This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on simulated EEGs obtained mixing source signals generated under different coupling conditions, showing its ability to retrieve source information dynamics from the scalp signals. Then, it was applied to investigate scalp and source brain connectivity in a group of children manifesting episodes of focal and generalized epilepsy; the analysis was performed on EEG signals lasting 5 s, collected in two consecutive windows preceding and one window following each ictal episode. Our results show that generalized seizures are associated with a significant decrease from pre-ictal to post-ictal periods of the information stored in the signals and of the information transferred among them, reflecting reduced self-predictability and causal connectivity at the level of both scalp and source brain dynamics. On the contrary, in the case of focal seizures the scalp EEG activity was not discriminated across conditions by any information measure, while source analysis revealed a tendency of the measures of information transfer to increase just before seizures and to decrease just after seizures. These results suggest that focal epileptic seizures are associated with a reorganization of the topology of EEG brain networks which is only visible analyzing connectivity among the brain sources. Our findings emphasize the importance of EEG modeling approaches able to deal with the adverse effects of volume conduction on brain connectivity analysis, and their potential relevance to the development of strategies for prediction and clinical treatment of epilepsy.

Epileptic seizure recognition using EEG wavelet decomposition based on nonlinear and statistical features with support vector machine classification

Epileptic seizure (ES) is a neurological brain dysfunction. ES can be detected using the electroencephalogram (EEG) signal. However, visual inspection of ES using long-time EEG recordings is a difficult, time-consuming and a costly procedure. Thus, automatic epilepsy recognition is of primary importance. In this paper, a new method is proposed for automatic ES recognition using short-time EEG recordings. The method is based on first decomposing the EEG signals on sub-signals using discrete wavelet transform. Then, from the obtained sub-signals, different non-linear parameters such as approximate entropy (ApEn), largest Lyapunov exponents (LLE) and statistical parameters are determined. These parameters along with phase entropies, calculated through higher order spectrum analysis, are used as an input vector of a multi-class support vector machine (MSVM) for ES recognition. The proposed method is evaluated using the standard EEG database developed by the Department of Epileptology, University of Bonn, Germany. The evaluation is carried out through a large number of classification experiments. Different statistical metrics namely Sensitivity (Se), Specificity (Sp) and classification accuracy (Ac) are calculated and compared to those obtained in the scientific research literature. The obtained results show that the proposed method achieves high accuracies, which are as good as the best existing state-of-the-art methods studied using the same EEG database.

Psychological and Physiological Responses in Patients with Generalized Anxiety Disorder: The Use of Acute Exercise and Virtual Reality Environment

Virtual exercise therapy is considered a useful method by which to encourage patients with generalized anxiety disorder (GAD) to engage in aerobic exercise in order to reduce stress. This study was intended to explore the psychological and physiological responses of patients with GAD after cycling in a virtual environment containing natural images. Seventy-seven participants with GAD were recruited in the present study and randomly assigned to a virtual nature (VN) or a virtual abstract painting (VAP) group. Their electroencephalogram alpha activity, perceived stress, and levels of restorative quality and satisfaction were assessed at baseline and after an acute bout of 20 min of moderate-intensity aerobic exercise. The results showed that both the VN and VAP groups showed significantly higher alpha activity post-exercise as compared to pre-exercise. The VN group relative to the VAP group exhibited higher levels of stress-relief, restorative quality, and personal satisfaction. These findings imply that a virtual exercise environment is an effective way to induce a relaxing effect in patients with GAD. However, they exhibited more positive psychological responses when exercising in such an environment with natural landscapes.

Case of catatonia misdiagnosed with coma

Catatonia is a state of high-degree psychomotor inhibition in which patients often maintain a constant fixed posture, and generally have unconscious obstacles and various reflex preservation. Patients suffering from severe catatonia will become stiff. Catatonia generally manifests unconsciousness while various reflexes are preserved. Patients show reticence, no food or drink intake, and immobility as signs of complete suppression of speech and movement, and even incontinence. Patients are often first diagnosed in non-psychiatric departments and are more likely to be misdiagnosed as having ‘coma’ or ‘epilepsy’, thus delaying treatment. This article reports a case of a 19-year-old female patient who was misdiagnosed with ‘catatonia’. A month ago, she was admitted to a general hospital of our city because of “intermittent attacks of nausea, vomiting, stupor for 15 years, with one week of exacerbation”. During her hospitalisation, she suddenly appeared was mute, had no food or drink intake, and showed immobility and incontinence, presenting a ‘coma state’. She was transferred to a general hospital in Wuhan to further investigate the cause of her “coma”. After 7 days in the hospital, no abnormal examination results were found and the symptoms were not alleviated. Later, she was transferred to the mental health centre for hospitalisation in this city. The patient was diagnosed as having (1) ‘epileptic psychosis’ and (2) ‘catatonic stupor’. After 3 days of treatment, the patient's recovered consciousness. She was clinically cured and discharged half a month later. We hereby report this case.