Subdural electroencephalogram analysis for extracting discriminating measures in epileptogenic data

This dissertation introduces an integrated algorithm for a new application dedicated at discriminating between electrodes leading to a seizure onset and those that do not, using interictal subdural EEG data. The significance of this study is in determining among all of these channels, all containing interictal spikes, why some electrodes eventually lead to seizure while others do not. A first finding in the development process of the algorithm is that these interictal spikes had to be asynchronous and should be located in different regions of the brain, before any consequential interpretations of EEG behavioral patterns are possible. A singular merit of the proposed approach is that even when the EEG data is randomly selected (independent of the onset of seizure), we are able to classify those channels that lead to seizure from those that do not. It is also revealed that the region of ictal activity does not necessarily evolve from the tissue located at the channels that present interictal activity, as commonly believed.^ The study is also significant in terms of correlating clinical features of EEG with the patient's source of ictal activity, which is coming from a specific subset of channels that present interictal activity. The contributions of this dissertation emanate from (a) the choice made on the discriminating parameters used in the implementation, (b) the unique feature space that was used to optimize the delineation process of these two type of electrodes, (c) the development of back-propagation neural network that automated the decision making process, and (d) the establishment of mathematical functions that elicited the reasons for this delineation process. ^

Alzheimer’s electroencephalogram event scalp and source localization

Alzheimer’s disease is the most common cause of dementia which causes a progressive and irreversible impairment of several cognitive functions. The aging population has been increasing significantly in recent decades and this disease affects mainly the elderly. Its diagnostic accuracy is relatively low and there is not a biomarker able to detect AD without invasive tests. Despite the progress in better understanding the disease there remains no prospect of cure at least in the near future. The electroencephalogram (EEG) test is a widely available technology in clinical settings. It may help diagnosis of brain disorders, once it can be used in patients who have cognitive impairment involving a general decrease in overall brain function or in patients with a located deficit. This study is a new approach to improve the scalp localization and the detection of brain anomalies (EEG temporal events) sources associated with AD by using the EEG.

Electroencephalogram cepstral distances in alzheimer’s disease diagnosis

Alzheimer's disease (AD) represents one ofthe greatest public health challenges worldwide nowadays, because it affects millions of people ali o ver the world and it is expected that the disease will increase considerably in the near future. This study is the first application attempt of cepstral analysis on Electroencephalogram (EEG) signals to find new parameters in arder to achieve a better differentiation belween EEGs of AD patients and Control subjects. The results show that the methodology that uses a combined Wavelet (WT) Biorthogonal (Bior) 3.5 and cepstrum analysis was able to describe the EEG dynamics with a higher discriminative power than the other WTs/spectmm methodologies m previous studies. The most important significance figures were found in cepstral distances between cepstrums oftheta and alpha bands (p=0. 00006<0. 05).

Aspecto tumoral da cisticercose intracraniana: abordagem cirúrgica

The cerebral cysticercosis can produce intracranial hypertension by inflammatory obstruction of the basal cysterns or by expansive lesion in the cerebral parenchima or ventricular cavities. In the latter and in tumor cases the clinical picture is very similar and only after surgery can the etiology be determined. We present 11 operated cases of intracranial cysticercosis which presented the clinical picture of an expansive lesion. There were 7 females and 4 males with ages between 4 and 65 years. Nine patients were admitted because of headache, vomiting and visual disturbances suggestive of intracranial hypertension. One patient was admited with lymphocytic meningitis and another with focal seizures following hemiparesis. Five patients presented focal signs and six edema of the papilla. Epileptic manifestations were present in 45.5% of the cases. A plain X-ray films of the skull failed to reveal calcificatons, however signs of chronic hypertension were present in three cases. The electroencephalogram showed slow focal waves in 8 patients The spinal fluid examination revealed lymphocytosis in 4 cases, increased protein content in another 4 and complement fixation for cysticercosis was positive in 2 cases. The expansive lesions were localized by angiograph and ventriculography. In these the location was temporal in 4, frontal in 3, parietal in 2, in the third ventricle in one and in the fourth ventricle in another. At surgery we removed a large cyst from the cerebral parenchyma in six cases. Around the cyst a thick glial reaction was present. In the other cases the cyst was small but fixed to the ventricular trigone and produced dilatation of the inferior horn of the lateral ventricle. In two cases we removed a solitary intraventricular cyst from the third and fourth ventricles. In the two children operated upon there were several small hard cysts involving the cerebral parenchyma which displayed intense gliosis. There were no postoperative complications.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).

EEG-based Brain-Computer Interfaces: An Overview of Basic Concepts and Clinical Applications in Neurorehabilitation

Some patients are no longer able to communicate effectively or even interact with the outside world in ways that most of us take for granted. In the most severe cases, tetraplegic or post-stroke patients are literally `locked in` their bodies, unable to exert any motor control after, for example, a spinal cord injury or a brainstem stroke, requiring alternative methods of communication and control. But we suggest that, in the near future, their brains may offer them a way out. Non-invasive electroencephalogram (EEG)-based brain-computer interfaces (BCD can be characterized by the technique used to measure brain activity and by the way that different brain signals are translated into commands that control an effector (e.g., controlling a computer cursor for word processing and accessing the internet). This review focuses on the basic concepts of EEG-based BC!, the main advances in communication, motor control restoration and the down-regulation of cortical activity, and the mirror neuron system (MNS) in the context of BCI. The latter appears to be relevant for clinical applications in the coming years, particularly for severely limited patients. Hypothetically, MNS could provide a robust way to map neural activity to behavior, representing the high-level information about goals and intentions of these patients. Non-invasive EEG-based BCIs allow brain-derived communication in patients with amyotrophic lateral sclerosis and motor control restoration in patients after spinal cord injury and stroke. Epilepsy and attention deficit and hyperactive disorder patients were able to down-regulate their cortical activity. Given the rapid progression of EEG-based BCI research over the last few years and the swift ascent of computer processing speeds and signal analysis techniques, we suggest that emerging ideas (e.g., MNS in the context of BC!) related to clinical neuro-rehabilitation of severely limited patients will generate viable clinical applications in the near future.

Improving Alzheimer`s Disease Diagnosis with Machine Learning Techniques

There is not a specific test to diagnose Alzheimer`s disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.

The prevalence and significance of periodic leg movements during sleep in patients with congestive heart failure

The aim of this study was to evaluate (1) the prevalence of periodic leg movements during sleep (PLMs) in a consecutive sample of congestive heart failure (CHF) outpatients; (2) the presence of correlation between PLMs, subjective daytime sleepiness, and sleep architecture; and (3) the heart rate response to PLMs in CHF. Seventy-nine [50 men, age 59 +/- 11 years, body mass index (BMI) 26 +/- 5 kg/m(2)] consecutive adult stable outpatients with CHF [left ventricular ejection fraction (LVEF) 36 +/- 6%] were prospectively evaluated. The patients underwent assessment of echocardiography, sleepiness (Epworth Scale), and overnight in-lab polysomnography. Fifteen patients (19%) had PLM index > 5. These subjects were similar in sex distribution, BMI, subjective somnolence, LVEF, and apnea-hypopnea index (AHI), but were significantly older than subjects without PLMs. Sleep architecture was similar in subjects with and without PLMs. There was a small but significant elevation of heart rate after PLMs (80.1 +/- 9.4 vs. 81.5 +/- 9.2; p < 0.001). The cardiac acceleration was also present in absence of electroencephalogram activation. The prevalence of PLMs in consecutive sample of adult CHF outpatients was 19%. There were no differences in subjective daytime sleepiness, sleep architecture, AHI, and severity of CHF in subjects with and without PLMs. PLMs caused a small but statistically significant cardiac acceleration.

A review of health effects of aircraft noise

Social surveys have established dose-response relationships between aircraft noise and annoyance, with a number of psychological symptoms being positively related to annoyance. Evidence that exposure to aircraft noise is associated with higher psychiatric hospital admission rates is mixed. Some evidence exists of an association between aircraft noise exposure and use of psychotropic medications. People with a pre-existing psychological or psychiatric condition may be more susceptible to the effects of exposure to aircraft noise. Aircraft noise can produce effects on electroencephalogram sleep patterns and cause wakefulness and difficulty in sleeping. Attendances at general practitioners, self-reported health problems and use of medications, have been associated with exposure to aircraft noise, but some findings are inconsistent. Some association between aircraft noise exposure and elevated mean blood pressure has been observed in cross-sectional studies of schoolchildren, but with little confirmation from cohort studies. There is no convincing evidence to suggest that all-cause or cause-specific mortality is increased by exposure to aircraft noise. There is no strong evidence that aircraft noise has significant perinatal effects. Using the World Health Organization definition of health, which includes positive mental and social wellbeing, aircraft noise is responsible for considerable ill-health. However, population-based studies have not found strong evidence that people living near or under aircraft flight paths suffer higher rates of clinical morbidity or mortality as a consequence of exposure to aircraft noise. A dearth of high quality studies in this area precludes drawing substantive conclusions.

From lateral to mesial: The quest for a surgical cure for temporal lobe epilepsy

Purpose: A gap of more than a hundred years occurred between the first accounts of mesial temporal sclerosis and recognition of its role in the pathogenesis of psychomotor seizures. This paper reviews how the understanding and surgical treatment of temporal lobe epilepsy developed, particularly from the work of Penfield, Jasper, and their associates at the Montreal Neurological Institute (MNI). Methods: Publications on EEG and surgery for temporal lobe seizures from 1935 to 1953 were reviewed and charts of selected patients operated on at the MNI in the same period were examined. Attention was focused on the evolution of surgical techniques for temporal lobe epilepsy. Results: In the late 1930s, some EEG findings suggested deep-lying disturbances originating in the temporal lobe. However, it took another two decades before the correlation of clinical, neurophysiological, and anatomical findings provided evidence for the involvement of the mesial structures in psychomotor or temporal lobe seizures. From 1949 and onward, Penfield and his associates applied this evidence to extend the surgical resections to include the uncus and the hippocampus. Conclusion: The collaborative work of a team led by Penfield and Jasper at the MNI helped to define the role of neurophysiological studies in epilepsy surgery. As a result, the importance of removing the mesial structures in order to obtain better seizure control in patients with temporal lobe epilepsy became firmly established.

THE EFFECTS OF BROMAZEPAM ON THE PERFORMANCE OF A SENSORY-MOTOR ACTIVITY: AN ELECTROENCEPHALOGRAPHIC STUDY

Aims. To investigate the effects of using bromazepam on the relative power in alpha while performing a typing task. Bearing in mind the particularities of each brain hemisphere, our hypothesis was that measuring the relative power would allow its to investigate the effects of bromazepam oil specific areas of the cortex. More, specifically, we expected to observe different patterns of powers in sensory-motor integration, attention and activation processes. Subjects and methods. The sample was made up of 39 subjects (15 males and 24 females) with a mean age of 30 +/- 10 years. The control (placebo) and experimental (3 mg and 6 mg of bromazepam) groups were trained ill the typing task with a randomised double-blind model. Results. A three-way ANOVA and Scheffe test were used to analyse interactions between the factors condition and moment, and between condition and sector Conclusions. The doses used ill this study facilitated motor performance of the typing task. Ill this study, the use of the drug did not prevent learning of the task, but it did appear to concentrate mental effort on more restricted and specific aspects of typing. It also seemed to influence the rhythm and effectiveness of the operations performed during mechanisms related to the encoding and storage often, information. Likewise, a predominance of activity was observed in the left (dominant) frontal area in the 3 mg bromazepam group, which indicates that this close of the drug affords the subject a greater degree of directionality of cortical activity for planning and performing the task. [REV NEUROL 2009; 49: 295-9]

Involvement of the central nervous system in the chronic form of Chagas` disease

Background and purpose: Apart from the central nervous system parasitic invasion in chagasic immunodeficient patients and strokes due to heart lesions provoked by the disease, the typical neurological syndromes of the chronic phase of Chagas` disease (CD) have not yet been characterized, although involvement of the peripheral nervous system has been well documented. This study aims at investigating whether specific signs of central nervous system impairment might be associated with the disease. Methods: Twenty-seven patients suffering from the chronic form of Chagas` disease (CCD) and an equal number of controls matched for sex, age, educational and socio-cultural background, and coming from the same geographical regions, were studied using neurological examinations, magnetic resonance images, and electroencephalographic frequency analysis. Results: Nineteen patients were at the stage A of the cardiac form of the disease (without documented structural lesions or heart failure). Dizziness, brisk reflexes, and ankle and knee areflexia were significantly more prevalent in the patients than in the controls. The significant findings in quantitative electroencephalogram were an increase in the theta relative power and a decrease in the theta dominant frequency at temporal-occipital derivations. Subcortical, white matter demyelination was associated with diffuse theta bursts and theta-delta slowing in two patients. Conclusions: Our findings suggest a discrete and unspecific functional cortical disorder and possible white matter lesions in CD. The focal nervous system abnormalities in CD documented here did not seem to cause significant functional damage or severely alter the patient`s quality of life. (C) 2008 Elsevier B.V. All rights reserved.

Bispectral monitoring in dogs subjected to ovariohysterectomy and anesthetized with halothane, isoflurane or sevoflurane

Objective To assess the effect of halothane (H), isoflurane (I) or sevoflurane (S) on the bispectral index (BIS), and the effect of the addition of meperidine in dogs subjected to ovariohysterectomy. Study design Prospective, randomized, blinded, clinical trial. Animals Forty-eight female mixed-breed dogs, with weights varying from 10 to 25 kg. Methods All dogs were premedicated with acepromazine (A) (0.1 mg kg(-1) IM) or A and meperidine (M) (3 mg kg(-1) IM) and they were divided into six groups of eight animals (AH, AMH, AI, AMI, AS, and AMS). Fifteen minutes after premedication they were anesthetized with propofol (5 mg kg(-1) IV) and then orotracheally intubated. Anesthesia was maintained with halothane, isoflurane or sevoflurane, respectively. The BIS, E`(anest) variables were recorded at 15 minutes after administering pre-anesthetic medication (T0); 10 minutes of anesthesia maintenance (T1); right ovarian pedicle ligation (T2); muscle suturing (T3); skin suture (T4) and 10 minutes after terminating the inhalant anesthetic (T5), respectively. Results BIS values were decreased at all times when compared to the baseline values in all groups (p < 0.05). In the comparative assessment between groups, the values obtained at T0 and T1 were similar for all groups. At T2, the values in AMH were lower than those obtained in AI, AMI and AS (p < 0.05). At the same time significantly higher values were found for AI when compared to AMS (p < 0.01). There was a correlation between the bispectral index and the expired anesthetic fraction in all groups. Conclusions and clinical relevance Within groups given the same inhalant anesthetic the bispectral index was a good indicator for the degree of hypnosis in dogs, indicating a good correlation with the amount of anesthetic and the nociceptive stimulation. BIS was a less reliable indicator of relative anesthetic depth when comparing equipotent end-tidal concentrations between the three inhalants.

Preprocessing and time-frequency analysis of newborn EEG seizures

Neurological disease or dysfunction in newborn infants is often first manifested by seizures. Prolonged seizures can result in impaired neurodevelopment or even death. In adults, the clinical signs of seizures are well defined and easily recognized. In newborns, however, the clinical signs are subtle and may be absent or easily missed without constant close observation. This article describes the use of adaptive signal processing techniques for removing artifacts from newborn electroencephalogram (EEG) signals. Three adaptive algorithms have been designed in the context of EEG signals. This preprocessing is necessary before attempting a fine time-frequency analysis of EEG rhythmical activities, such as electrical seizures, corrupted by high amplitude signals. After an overview of newborn EEG signals, the authors describe the data acquisition set-up. They then introduce the basic physiological concepts related to normal and abnormal newborn EEGs and discuss the three adaptive algorithms for artifact removal. They also present time-frequency representations (TFRs) of seizure signals and discuss the estimation and modeling of the instantaneous frequency related to the main ridge of the TFR.