A Scalable Automated Diagnostic Feature Extraction System for EEGs

Researchers using Electroencephalograms (“EEGs”) to diagnose clinical outcomes often run into computational complexity problems. In particular, extracting complex, sometimes nonlinear, features from a large number of time-series often require large amounts of processing time. In this paper we describe a distributed system that leverages modern cloud-based technologies and tools and demonstrate that it can effectively, and efficiently, undertake clinical research. Specifically we compare three types of clusters, showing their relative costs (in both time and money) to develop a distributed machine learning pipeline for predicting gestation time based on features extracted from these EEGs

DIAGNOSTIC PERFORMANCE OF THE AMBULATORY EEG VERSUS ROUTINE EEG AND RISK FACTORS FOR SEIZURE RECURRENCE AMONG INDIVIDUALS WITH FIRST SINGLE UNPROVOKED SEIZURES

Background and objectives: Routine electroencephalography (rEEG) remains central in the prognosis of seizure recurrence among individuals with a First Single Unprovoked seizure (FSUS). Furthermore, it is well-established that the presence of epileptiform discharge (ED) in the EEG increases the risk of further seizures among individuals with FSUS up to 3 times compared with individuals without such EEG changes. However, the rEEG has low sensitivity, leaving patients and clinicians without a fast and accurate tool for the prognosis of further seizures. This study aims to determine and compare the discriminative power, clinical predictive value, and global diagnostic accuracy of the ambulatory EEG compared with the first rEEG and second rEEG. This study also aims to determine risk factors for further seizures among individuals with FSUS, including ED in the ambulatory EEG. Methods: The study used a prospective cohort design with a total of 100 individuals with FSUS who underwent three modalities of EEG (first rEEG, second rEEG and Ambulatory EEG) and who were followed up for one year period. All the required information was available in this dataset, and further seizures were prospectively recorded. The three EEG (first, second rEEGs and ambulatory EEG) were interpreted by licensed neurologists recognized by the Royal College of Physicians and Surgeons of Canada and fully accredited by the Canadian Society of Clinical Neurophysiologists. Diagnosis of epilepsy was made based on clinical, neurophysiology and imaging tests following the definition of epilepsy by the International League Against Epilepsy 2014. Receiver-operating-characteristic (ROC) analysis was used to evaluate the results. Also, P a g e iii table-life and survival analysis were used to determine the risk for further seizures during the 52 weeks follow-up period. Results: We found that the ambulatory EEG’s diagnostic accuracy was better than the first and second EEG (0.79 vs. 0.51 and 0.54, respectively) in the population. Age group was a confounder in the association between seizure recurrence at 52 weeks and the presence of ED in the ambulatory EEG. The presence of ED in the ambulatory EEG increased the risk of seizure recurrence among individuals with FSUS 3.2 times when adjusted for use of antiseizure medication (ASM) and age group. Finally, other risk factors modifying the association between further seizures and the presence of ED in the ambulatory EEG included age group of >60 years (HR: 0.27 95%CI: 0.10,0.74) and the use of ASM (HR: 12.9, 95%CI: 5.6, 29.3). Conclusions: The overall diagnostic accuracy of the ambulatory EEG as a means of detecting ED among individuals with FSUS is better than the first and second rEEG. Furthermore, ED in the ambulatory EEG is a significant risk factor predicting further seizures after a single unprovoked seizure after adjusting for the use of ASM and age group. Significance: This study advanced our knowledge about the use of ambulatory EEG as an ancillary tool for predicting further seizures after FSUS and established that the presence of epileptiform activity in the ambulatory EEG is a risk factor for further seizures after adjusting for use of ASM and age group. The use of ambulatory EEG may reduce diagnostic errors and is also low-cost and better tool which can be used worldwide for more accurate diagnosis of epilepsy compared to rEEG

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 128, May 1974

This special bibliography lists 282 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1974

Communications Biophysics

Contains reports on five research projects.United States Air Force (Contract AF19(604)-4112)National Institute of Neurological Diseases and Blindness (B369 Physiology

The effect of rapid weight loss on cognitive function in collegiate wrestlers

This study examined the effect of rapid weight loss on cognitive function in collegiate wrestlers

Introduction of Fractal Dimension Feature and Reduction of Calculation Amount in Person Authentication Using Evoked EEG by Ultrasound

The aim of this study is to authenticate individuals using an electroencephalogram (EEG) evoked by a stimulus. EEGs are highly confidential and enable continuous authentication during the use of or access to the given information or service. However, perceivable stimulation distracts the users from the activity they are carrying out while using the service. Therefore, ultrasound stimuli were chosen for EEG evocation. In our previous study, an Equal Error Rate (EER) of 0 % was achieved; however, there were some features which had not been evaluated. In this paper, we introduce a new type of feature, namely fractal dimension, as a nonlinear feature, and evaluate its verification performance on its own and in combination with other conventional features. As a result, an EER of 0 % was achieved when using five features and 14 electrodes, which accounted for 70 support vector machine (SVM) models. However, the construction of the 70 SVM models required extensive calculations. Thus, we reduced the number of SVM models to 24 while maintaining an EER = 0 %

Calculation of the cyclic characteristics of the electroencephalogram for investigation of the electrical activity of the brain

The purpose of the study is experimental verification of the proposed EEG analysis method based on the construction of a connectivity graph of the analyzed signal, in which the amplitudes are displayed by vertices, and their relative position relative to each other by arcs. The display of the EEG signal in the graph structure causes the appearance of cyclic structures with the possibility of calculating their numerical characteristics. As a result of the study, criteria for initialization of the initial conditions of the counting algorithm have been developed. The following parameters were calculated: the number of cycles and the Euler number in the EEG recording. Coil representations of graphs are given. The proposed algorithm has a scaling parameter, the choice of which affects the final results. The second free parameter of the proposed algorithm is the degree of artificial signal coarsening. Variants of the algorithm application for multichannel EEG signals with multichannel signal processing by channel-by-channel detection of semantic units and construction of a generalized semantic connectivity graph are considered. An example of an analyzed multichannel EEG signal, which was pre-processed with reduction of all amplitudes to natural numbers in accordance with the calculated characteristics, is given. An example of an EEG of a subject with closed eyes during quiet wakefulness and an EEG of a subject with open eyes is given. In Conclusion, it is shown that the final indicators can vary significantly (from zero to tens of thousands or more) depending on the particular derivation of the EEG channel. Analysis of the cyclic structures of the electroencephalogram seems to be a potential way to assess various human states due to the possibility of distinguishing them using the proposed method. The study has a limited, pilot characte

Bayesian averaging over Decision Tree models for trauma severity scoring

Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the “gold” standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions