An optimal feature set for seizure detection systems for newborn EEG signals

A novel automated method is applied to Electroencephalogram (EEG) data to detect seizure events in newborns. The detection scheme is based on observing the changing behavior of the wavelet coefficients (WCs) of the EEG signal at different scales. An optimizing technique based on mutual information feature selection (MIFS) is employed. This technique evaluates a set of candidate features extracted from the WCs to select an informative subset. This subset is used as an input to an artificial neural network (ANN) classifier. The classifier organizes the EEG signal into seizure or non-seizure activities. The training and test sets are obtained from EEG data acquired from 1 and 5 other neonates, respectively, with ages ranging from 2 days to 2 weeks. The optimized results show an average seizure detection rate of 94%

Time-frequency methodologies in neurosciences

This chapter presents a number of time-frequency (t,f) techniques that can provide advanced solutions to several problems in neuro-sciences with focus on the monitoring of brain abnormalities using EEG and other physiological modalities (t,f) characteristics as a diagnosis and prognosis tool. The methods presented illustrate the improved performance obtained by using a time-frequency approach to process EEG data, including a focus on detecting abnormalities in sick newborns in a Neonatal Intensive Care Unit (NICU) as well as mental health issues in elderlies. The chapter starts by presenting methods for the assessment of Newborn EEG and ECG abnormalities using a time-frequency identification approach (Section 16.1). Next, the important question of (t,f) modeling of nonstationary signals is discussed with illustration on newborn EEGs (Section 16.2); Then, the use of (t,f) features for nonstationary signal classification is illustrated on an application to newborn EEG burst-suppression detection (Section 16.3); an application relevant to the elderly is described where a time-varying analysis of brain networks uses the EEG for the detection of Alzheimer disease (Section 16.4). Another method of time-frequency analysis is described that involves EEG noise reduction using the empirical mode decomposition(Section 16.5). Finally the chapter concludes with a discussion on other perspectives of using advanced (t,f) methods for medical diagnosis and prognosis in other areas of neurosciences (Section 16.6).Scopu

Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers’ Mental Workload Classification

In the pursuit of reducing traffic accidents, drivers’ mental workload (MWL) has been considered as one of the vital aspects. To measure MWL in different driving situations Electroencephalography (EEG) of the drivers has been studied intensely. However, in the literature, mostly, manual analytic methods are applied to extract and select features from the EEG signals to quantify drivers’ MWL. Nevertheless, the amount of time and effort required to perform prevailing feature extraction techniques leverage the need for automated feature extraction techniques. This work investigates deep learning (DL) algorithm to extract and select features from the EEG signals during naturalistic driving situations. Here, to compare the DL based and traditional feature extraction techniques, a number of classifiers have been deployed. Results have shown that the highest value of area under the curve of the receiver operating characteristic (AUC-ROC) is 0.94, achieved using the features extracted by convolutional neural network autoencoder (CNN-AE) and support vector machine. Whereas, using the features extracted by the traditional method, the highest value of AUC-ROC is 0.78 with the multi-layer perceptron. Thus, the outcome of this study shows that the automatic feature extraction techniques based on CNN-AE can outperform the manual techniques in terms of classification accuracy

Converging Subjective and Psychophysiological Measures of Cognitive Load to Study the Effects of Instructor‐Present Video

Many online videos feature an instructor on the screen to improve learners\u27 engagement; however, the influence of this design on learners\u27 cognitive load is underexplored. This study investigates the effects of instructor presence on learners\u27 processing of information using both subjective and psychophysiological measures of cognitive load. Sixty university students watched a statistics instructional video either with or without instructor presence, while the spontaneous electrical activity of their brain was recorded using electroencephalography (EEG). At the conclusion of the video, they also self-reported overall load, intrinsic load, extraneous load, and germane load they experienced during the video. Learning from the video was assessed via tests of retention and transfer. Results suggested the instructor-present video improved learners\u27 ability to transfer information and was associated with a lower self-reported intrinsic and extraneous load. Event-related changes in theta band activity also indicated lower cognitive load with instructor-present video.</p

Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%)

On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

The measurement of the mental workload during real tasks by means of neurophysiological signals is still challenging. The employment of Machine Learning techniques has allowed a step forward in this direction, however, most of the work has dealt with binary classification. This study proposed to examine the surveys already performed in the context of EEG-based workload classification and to test different machine learning algorithms on real multitasking activity like the Air Traffic Management. The results obtained on 35 professional Air Traffic Controllers showed that a KNN algorithm allows discriminating up to three workload levels (low, medium and high) with more than 84% of accuracy on average. Moreover, in such realistic employment it emerges how important is to opportunely choose the set of features to ward off that task-related confounds could affect the workload assessment