Extraction of features from sleep EEG for Bayesian assessment of brain development

Brain development can be evaluated by experts analysing age-related patterns in sleep electroencephalograms (EEG). Natural variations in the patterns, noise, and artefacts affect the evaluation accuracy as well as experts' agreement. The knowledge of predictive posterior distribution allows experts to estimate confidence intervals within which decisions are distributed. Bayesian approach to probabilistic inference has provided accurate estimates of intervals of interest. In this paper we propose a new feature extraction technique for Bayesian assessment and estimation of predictive distribution in a case of newborn brain development assessment. The new EEG features are verified within the Bayesian framework on a large EEG data set including 1,100 recordings made from newborns in 10 age groups. The proposed features are highly correlated with brain maturation and their use increases the assessment accuracy

Classification of newborn EEG maturity with Bayesian averaging over decision trees

EEG experts can assess a newborn’s brain maturity by visual analysis of age-related patterns in sleep EEG. It is highly desirable to make the results of assessment most accurate and reliable. However, the expert analysis is limited in capability to provide the estimate of uncertainty in assessments. Bayesian inference has been shown providing the most accurate estimates of uncertainty by using Markov Chain Monte Carlo (MCMC) integration over the posterior distribution. The use of MCMC enables to approximate the desired distribution by sampling the areas of interests in which the density of distribution is high. In practice, the posterior distribution can be multimodal, and so that the existing MCMC techniques cannot provide the proportional sampling from the areas of interest. The lack of prior information makes MCMC integration more difficult when a model parameter space is large and cannot be explored in detail within a reasonable time. In particular, the lack of information about EEG feature importance can affect the results of Bayesian assessment of EEG maturity. In this paper we explore how the posterior information about EEG feature importance can be used to reduce a negative influence of disproportional sampling on the results of Bayesian assessment. We found that the MCMC integration tends to oversample the areas in which a model parameter space includes one or more features, the importance of which counted in terms of their posterior use is low. Using this finding, we proposed to cure the results of MCMC integration and then described the results of testing the proposed method on a set of sleep EEG recordings

Biometric responses to music-rich segments in films: the CDVPlex

Summarising or generating trailers for films or movies involves finding the highlights within those films, those segments where we become most afraid, happy, sad, annoyed, excited, etc. In this paper we explore three questions related to automatic detection of film highlights by measuring the physiological responses of viewers of those films. Firstly, whether emotional highlights can be detected through viewer biometrics, secondly whether individuals watching a film in a group experience similar emotional reactions as others in the group and thirdly whether the presence of music in a film correlates with the occurrence of emotional highlights. We analyse the results of an experiment known as the CDVPlex, where we monitored and recorded physiological reactions from people as they viewed films in a controlled cinema-like environment. A selection of films were manually annotated for the locations of their emotive contents. We then studied the physiological peaks identified among participants while viewing the same film and how these correlated with emotion tags and with music. We conclude that these are highly correlated and that music-rich segments of a film do act as a catalyst in stimulating viewer response, though we don't know what exact emotions the viewers were experiencing. The results of this work could impact the way in which we index movie content on PVRs for example, paying special significance to movie segments which are most likely to be highlights

Comparative study of nonlinear properties of EEG signals of a normal person and an epileptic patient

Background: Investigation of the functioning of the brain in living systems has been a major effort amongst scientists and medical practitioners. Amongst the various disorder of the brain, epilepsy has drawn the most attention because this disorder can affect the quality of life of a person. In this paper we have reinvestigated the EEGs for normal and epileptic patients using surrogate analysis, probability distribution function and Hurst exponent. Results: Using random shuffled surrogate analysis, we have obtained some of the nonlinear features that was obtained by Andrzejak \textit{et al.} [Phys Rev E 2001, 64:061907], for the epileptic patients during seizure. Probability distribution function shows that the activity of an epileptic brain is nongaussian in nature. Hurst exponent has been shown to be useful to characterize a normal and an epileptic brain and it shows that the epileptic brain is long term anticorrelated whereas, the normal brain is more or less stochastic. Among all the techniques, used here, Hurst exponent is found very useful for characterization different cases. Conclusions: In this article, differences in characteristics for normal subjects with eyes open and closed, epileptic subjects during seizure and seizure free intervals have been shown mainly using Hurst exponent. The H shows that the brain activity of a normal man is uncorrelated in nature whereas, epileptic brain activity shows long range anticorrelation.Comment: Keywords:EEG, epilepsy, Correlation dimension, Surrogate analysis, Hurst exponent. 9 page

Reply to "Comment on 'Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy'"

We appreciate the interest of Dr Tang in our article. Certainly, our previous results should be taken with caution due to the small database size. Nevertheless, it must be noted that this limitation was clearly recognized in our article. Furthermore, our hypothesis is completely justified from the current state of the art in the analysis of electroencephalogram (EEG) signals from Alzheimer's disease (AD) patients. We evaluated whether the multiscale entropy (MSE) analysis of the EEG background activity was useful to distinguish AD patients and controls. We do believe that further discussions about risk factors or related clinicophysiological protein aspects are clearly beyond the scope of our article. For the sake of completeness, we now detail some results that complement our previous analysis. They suggest that the MSE analysis can provide relevant information about the dynamics of AD patients' EEG data. Thus, we must reaffirm our conclusions, although we again acknowledge that further studies are needed

Validation of a pediatric guideline on basic electroencephalogram interpretation for clinicians

Thesis (M. Tech. (Clinical technology )) - Central University of technology, Free State, 2013The incidence of epilepsy is high in sub-Saharan Africa and resource poor countries (RPCs). There are few neurologists and paediatric neurologists to manage people with epilepsy (PWE). Health care is often limited, particularly technological services, including electroencephalogram (EEG), video EEG monitoring, and Neuroradiology services. All these are important in the management of PWE. Since 2008, informal electrophysiology training has been provided at the Red Cross War Memorial Hospital, in the Department of Paediatric Neurology. The Principal Investigator (PI) elected to develop a formal teaching course on EEG interpretation at the Red Cross War Memorial Hospital. A study was designed to evaluate the practical use of a handbook entitled “Handbook of Paediatric Electroencephalography: A guide to basic paediatric electroencephalogram interpretation.” This has been developed to fulfill the need for basic understanding and interpretation of EEG amongst clinicians caring for children in sub-Saharan Africa who may not have access to, or be able to afford, training at a recognized facility or on-line. In 2008, the department of Paediatric Neurology at the Red Cross War Memorial Hospital had their first African fellow from Kenya. By 2011, seven participants had undergone EEG training. A quantitative research approach and design was used in order to evaluate the handbook in terms of the accessibility of the contents and its practical use. Quantification included the recruitment of participants who constituted the population sample, a pilot study, and the collection of data from comparative assessments of participants’ use of the handbook, and from questionnaires completed by participants. This provided the researcher with the opportunity to improve and validate her knowledge of training in EEG interpretation. The researcher was able to quantify and compare the scores of participants using the handbook, as well as to compare their evaluative responses to its content and practical use. Eleven of thirteen participants completed the study. The pre-training results showed a median percentage of 50 which increased to 70 percent post-test. A comparison of the scores of trained versus not-trained revealed that those participants who had undergone one-on-one training on site at the unit fared much better both in their interpretations, conclusions, and reporting of EEG findings. The responses from the evaluative and comparative survey between the two groups showed no significant difference across all questions, the majority of the questions on the relative usefulness of the handbook being rated ‘agree’ and ‘strongly agree’, thus supporting the finding that all participants found the handbook useful whether they had received one-on-one training or not. The post-training results in EEG interpretation showed a stronger trend towards statistical significance (p<0.06) with trained participants and with the not-trained. These findings lend support to the success and usefulness of the handbook as a basic guide to paediatric EEG interpretation. The handbook was not aimed at making the electroencephalography reader an expert at a specialist level, but rather to maximize the reliability of the reading of EEG when screening electroencephalograms for important key diagnostic markers which would alter the child’s management. This is the first published handbook on paediatric EEG in South Africa. The results of this study strongly suggest that the handbook is useful as a learning and reference tool in interpretation of paediatric EEG, both for individuals with access to one-on-one training as well as those without. It is intended that the handbook, in conjunction with one-on-one training, will form part of a post-graduate diploma course offered by the University of Cape Town on “basic electrophysiology and the management of children with epilepsy” for training neurologists and child neurologists, paediatricians and health care workers in sub-Saharan Africa

Review of sensors for remote patient monitoring

Remote patient monitoring (RPM) of physiological measurements can provide an efficient method and high quality care to patients. The physiological signals measurement is the initial and the most important factor in RPM. This paper discusses the characteristics of the most popular sensors, which are used to obtain vital clinical signals in prevalent RPM systems. The sensors discussed in this paper are used to measure ECG, heart sound, pulse rate, oxygen saturation, blood pressure and respiration rate, which are treated as the most important vital data in patient monitoring and medical examination

The Correlation between Electroencephalography Amplitude and Interictal Abnormalities : Audit study

Objectives: The aim of this study was to establish the relationship between background amplitude and interictal abnormalities in routine electroencephalography (EEG). Methods: This retrospective audit was conducted between July 2006 and December 2009 at the Department of Clinical Physiology at Sultan Qaboos University Hospital (SQUH) in Muscat, Oman. A total of 1,718 electroencephalograms (EEGs) were reviewed. All EEGs were from patients who had been referred due to epilepsy, syncope or headaches. EEGs were divided into four groups based on their amplitude: group one ≤20 μV; group two 21–35 μV; group three 36–50 μV, and group four &gt;50 μV. Interictal abnormalities were defined as epileptiform discharges with or without associated slow waves. Abnormalities were identified during periods of resting, hyperventilation and photic stimulation in each group. Results: The mean age ± standard deviation of the patients was 27 ± 12.5 years. Of the 1,718 EEGs, 542 (31.5%) were abnormal. Interictal abnormalities increased with amplitude in all four categories and demonstrated a significant association (P &lt;0.05). A total of 56 EEGs (3.3%) had amplitudes that were ≤20 μV and none of these showed interictal epileptiform abnormalities. Conclusion: EEG amplitude is an important factor in determining the presence of interictal epileptiform abnormalities in routine EEGs. This should be taken into account when investigating patients for epilepsy. A strong argument is made for considering long-term EEG monitoring in order to identify unexplained seizures which may be secondary to epilepsy. It is recommended that all tertiary institutions provide EEG telemetry services

An Investigation of How Wavelet Transform can Affect the Correlation Performance of Biomedical Signals : The Correlation of EEG and HRV Frequency Bands in the frontal lobe of the brain

© 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reservedRecently, the correlation between biomedical signals, such as electroencephalograms (EEG) and electrocardiograms (ECG) time series signals, has been analysed using the Pearson Correlation method. Although Wavelet Transformations (WT) have been performed on time series data including EEG and ECG signals, so far the correlation between WT signals has not been analysed. This research shows the correlation between the EEG and HRV, with and without WT signals. Our results suggest electrical activity in the frontal lobe of the brain is best correlated with the HRV.We assume this is because the frontal lobe is related to higher mental functions of the cerebral cortex and responsible for muscle movements of the body. Our results indicate a positive correlation between Delta, Alpha and Beta frequencies of EEG at both low frequency (LF) and high frequency (HF) of HRV. This finding is independent of both participants and brain hemisphere.Final Published versio