Tarik Al-ani

Prefrontal tDCS Decreases Pain in Patients with Multiple Sclerosis.

Background: In the last few years, transcranial direct current stimulation (tDCS) has emerged as an appealing therapeutic option to improve brain functions. Promising data support the role of prefrontal tDCS in augmenting cognitive performance and ameliorating several neuropsychiatric symptoms, namely pain, fatigue, mood disturbances, and attentional impairment. Such symptoms are commonly encountered in patients with multiple sclerosis (MS). Objective: The main objective of the current work was to evaluate the tDCS effects over the left dorsolateral prefrontal cortex (DLPFC) on pain in MS patients.Our secondary outcomes were to study its influence on attention, fatigue, and mood. Materials and Methods: Sixteen MS patients with chronic neuropathic pain were enrolled in a randomized, sham-controlled, and cross-over study.Patients randomly received two anodal tDCS blocks (active or sham), each consisting of three consecutive daily tDCS sessions, and held apart by 3 weeks. Evaluations took place before and after each block. To evaluate pain, we used the Brief Pain Inventory (BPI) and the Visual Analog Scale (VAS). Attention was assessed using neurophysiological parameters and the Attention Network Test (ANT). Changes in mood and fatigue were measured using various scales. Results: Compared to sham, active tDCS yielded significant analgesic effects according to VAS and BPI global scales.There were no effects of any block on mood, fatigue, or attention. Conclusion: Based on our results, anodal tDCS over the left DLPFC appears to act in a selective manner and would ameliorate specific symptoms, particularly neuropathic pain. Analgesia might have occurred through the modulation of the emotional pain network. Attention, mood, and fatigue were not improved in this work. This could be partly attributed to the short protocol duration, the small sample size, and the heterogeneity of our MS cohort. Future large-scale studies can benefit from comparing the tDCS effects over different cortical sites, changing the stimulation montage, prolonging the duration of protocol, and coupling tDCS with neuroimaging techniques for a better understanding of its possible mechanism of action.

Phase space and power spectral approaches for EEG-based automatic sleep-wake classification in humans: A comparative study using short and standard epoch lengths

Sleep disorders in humans have become a public health issue in recent years. Sleep can be analysed by studying the electroencephalogram (EEG) recorded during a nights sleep. Alternating between sleep-wake stages gives information related to the sleep quality and quantity since this alternating pattern is highly affected during sleep disorders. Spectral composition of EEG signals varies according to sleep stages, alternating phases of high energy associated to low frequency (deep sleep) with periods of low energy associated to high frequency (wake and light sleep). The analysis of sleep in humans is usually made on periods (epochs) of 30-s length according to the original Rechtschaffen and Kales sleep scoring manual. In this work, we propose a new phase space-based (mainly based on Poincaré plot) algorithm for automatic classification of sleep-wake states in humans using EEG data gathered over relatively short-time periods. The effectiveness of our approach is demonstrated through a series of experiments involving EEG data from seven healthy adult female subjects and was tested on epoch lengths ranging from 3-s to 30-s. The performance of our phase space approach was compared to a 2-dimensional state space approach using the power spectral (PS) in two selected human-specific frequency bands. These powers were calculated by dividing integrated spectral amplitudes at selected human-specific frequency bands. The comparison demonstrated that the phase space approach gives better performance in the case of short as well as standard 30-s epoch lengths.

Automatic removal of high-amplitude stimulus artefact from neuronal signal recorded in the subthalamic nucleus

Over the last few years, deep brain stimulation (DBS) with targets such as the subthalamic nucleus or the pallidum were found to be beneficial in the treatment of Parkinsons disease and dystonia. The investigation of the mechanisms of action of DBS by recording concomitant neural activities in basal ganglia is hampered by the large stimulus artefacts (SA). Approaches to remove the SA with conventional filters, or other conventional digital methods, are not always effective due to the significant overlap between the spectral contents of the neuronal signal and the SA. Thus, such approaches may produce a significant residual SA or alter the neuronal signal dynamics by removing its frequency contents. In this work, we propose a method based on an on-line SA template extraction and on the Ensemble empirical mode decomposition (EEMD) to automatically detect and remove the dynamics of the SA without altering the embedded dynamics of the neuronal signal during stimulation. The results, based on real signals recorded in the subthalamic nucleus during Motor cortex stimulation (MCS) experiments, show that this technique, which may be applied on-line, effectively identifies, separates and removes the SA, and uncovers neuronal potentials superimposed on the artefact.

Using hidden Markov models for sleep disordered breathing identification

Abstract In this work, an automatic diagnosis system based on hidden Markov models (HMMs) is proposed to help clinicians in the diagnosis of sleep apnea syndrome. Our system offers the advantage of being based on solid probabilistic principles rather than a predefined set of rules. Conventional and new simulated annealing based methods for the training of HMMs are incorporated. The inference method of this system translates parameter values into interpretations of physiological and pathophysiological states. The interpretation is extended to sequences of states in time to obtain a state-space trajectory. Some of the measurements of the respiratory activity issued by the technique of polysomnography (brain activity, respiratory activity, oxygen levels, and cardiac activity) are considered for off-line and on-line detection of the different sleep apnea syndromes: obstructive, central and hypopnea. Experimental results using respiratory clinical data and some future perspectives of our work are presented.

On-line Automatic Detection of Human Activity in Home Using Wavelet and Hidden Markov Models Scilab Toolkits

In this work, a pilot study of on-line automatic detection of human activity in home using wavelet and hidden Markov models Scilab toolkits was carried out. The collected raw data are provided by a biaxial accelerometer ADXL202E attached to the person. Several activities were simulated by the researchers (walking slowly , walking quickly, sitting down-getting up, fall during walking, fall from a position upright, ...). The feature vectors of these data were then used to build different hidden Markov models of these activities with different persons. The built models were employed for online detection of these activities. The obtained results are very promising.

Effects of transcranial random noise stimulation (tRNS) on affect, pain and attention in multiple sclerosis

PURPOSE Pain and cognitive impairment are frequent symptoms in patients with multiple sclerosis (MS). Neglecting experimental pain and paying attention to demanding tasks is reported to decrease the pain intensity. Little is known about the interaction between chronic neuropathic pain and attention disorders in MS. Recently, transcranial direct current stimulation (tDCS) was used to modulate various cognitive and motor symptoms in MS. We aimed to study the effects of transcranial random noise stimulation (tRNS), a form of transcranial electric stimulation, over the left dorsolateral prefrontal cortex (DLPFC) on attention and neuropathic pain in MS patients. METHODS 16 MS patients were included in a randomized, sham-controlled, cross-over study. Each patient randomly received two tRNS blocks, separated by three weeks of washout interval. Each block consisted of three consecutive daily sessions of either active or sham tRNS. The patients were evaluated for pain, attention and mood and further underwent an electrophysiological evaluation. RESULTS Compared to sham, tRNS showed a trend to decrease the N2-P2 amplitudes of pain related evoked potentials and improve pain ratings. Attention performance and mood scales did not change after stimulations. CONCLUSIONS This study suggests the role of tRNS in pain modulation, which could have been more evident with longer stimulation protocols.

Automatic recognition of obstructive sleep apnoea syndrome using power spectral analysis of electrocardiogram and Hidden Markov Models

Obstructive sleep apnoea syndrome (OSA) is a very common disorder in breathing during sleep. OSA is considered as clinically relevant when the breath stops during more than 10 seconds and occurs more than five times per sleep hour. In this work, we investigate a noninvasive automatic approach to classify sleep apnoea events based on power spectral analysis for the feature extraction of the ECG records and hidden Markov models (HMMs). Based on Bayesian inference criterion (BIC), the proposed HMM training algorithm is able to select the optimal number of states corresponding to each set of training features. For every state number, each iteration is initialized by the most appropriate model using data clustering, and by the rejection of the least probable state of the previous iteration. Both off-line and on-line schemes have been proposed. Only electrocardiogram (ECG) records are considered for the detection of OSA. In this preliminary work, we report training procedures and validation results of the models on whole night digitized ECG signals recorded from 70 subjects with normal and OSA breathing events obtained from the physionet database.

P300 based brain-computer interface using Hidden Markov Models

This paper reports on preliminary work on the use of hidden Markov models (HMMs) approach for tasks classification in P300-based brain-computer interface (BCI) system. Every HMM is trained on a set of electroencephalogram (EEG) records issued from different sessions corresponding to the same task. The HMMs that has been built take into account the variability of EEGs during different sessions. Based on Bayesian inference criterion (BIC), the proposed HMM training algorithm is able to select the optimal number of states corresponding to each set of EEG training records. For every state number, each iteration is initialized by the most appropriate model using data clustering, and by the rejection of the least probable state of the previous iteration. Consequently, every training iteration begin by a more precise model. We report training procedures and validation results of the models. The obtained results give a correct and promising classification rates for all subjects which is the objective of this work.

An integrated environment for hidden Markov models-a Scilab toolbox

A hidden Markov model toolbox is presented within the Scilab environment. In this toolbox popular methods for the resolution of HMM problems are incorporated. These methods cover the training and recognition phases. Models may be used with discret and continuous observations. This toolbox includes conventional methods as well as extensions.

An automatic electroencephalography blinking artefact detection and removal method based on template matching and ensemble empirical mode decomposition

Electrooculographic (EOG) artefacts are one of the most common causes of Electroencephalogram (EEG) distortion. In this paper, we propose a method for EOG Blinking Artefacts (BAs) detection and removal from EEG. Normalized Correlation Coefficient (NCC), based on a predetermined BA template library was used for detecting the BA. Ensemble Empirical Mode Decomposition (EEMD) was applied to the contaminated region and a statistical algorithm determined which Intrinsic Mode Functions (IMFs) correspond to the BA. The proposed method was applied in simulated EEG signals, which were contaminated with artificially created EOG BAs, increasing the Signal-to-Error Ratio (SER) of the EEG Contaminated Region (CR) by 35dB on average.