1,679 research outputs found
Classification of Epileptic EEG Signals by Wavelet based CFC
Electroencephalogram, an influential equipment for analyzing humans
activities and recognition of seizure attacks can play a crucial role in
designing accurate systems which can distinguish ictal seizures from regular
brain alertness, since it is the first step towards accomplishing a high
accuracy computer aided diagnosis system (CAD). In this article a novel
approach for classification of ictal signals with wavelet based cross frequency
coupling (CFC) is suggested. After extracting features by wavelet based CFC,
optimal features have been selected by t-test and quadratic discriminant
analysis (QDA) have completed the Classification.Comment: Electroencephalogram; Wavelet Decomposition; Cross Frequency
Coupling;Quadratic Discriminant Analysis; T-test Feature Selectio
Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis
Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is
an early predictor of Parkinson's disease. This study proposes a
fully-automated framework for RBD detection consisting of automated sleep
staging followed by RBD identification. Analysis was assessed using a limited
polysomnography montage from 53 participants with RBD and 53 age-matched
healthy controls. Sleep stage classification was achieved using a Random Forest
(RF) classifier and 156 features extracted from electroencephalogram (EEG),
electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a
RF classifier was trained combining established techniques to quantify muscle
atonia with additional features that incorporate sleep architecture and the EMG
fractal exponent. Automated multi-state sleep staging achieved a 0.62 Cohen's
Kappa score. RBD detection accuracy improved by 10% to 96% (compared to
individual established metrics) when using manually annotated sleep staging.
Accuracy remained high (92%) when using automated sleep staging. This study
outperforms established metrics and demonstrates that incorporating sleep
architecture and sleep stage transitions can benefit RBD detection. This study
also achieved automated sleep staging with a level of accuracy comparable to
manual annotation. This study validates a tractable, fully-automated, and
sensitive pipeline for RBD identification that could be translated to wearable
take-home technology.Comment: 20 pages, 3 figure
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
Electroencephalography (EEG) measures the neuronal activities in different
brain regions via electrodes. Many existing studies on EEG-based emotion
recognition do not fully exploit the topology of EEG channels. In this paper,
we propose a regularized graph neural network (RGNN) for EEG-based emotion
recognition. RGNN considers the biological topology among different brain
regions to capture both local and global relations among different EEG
channels. Specifically, we model the inter-channel relations in EEG signals via
an adjacency matrix in a graph neural network where the connection and
sparseness of the adjacency matrix are inspired by neuroscience theories of
human brain organization. In addition, we propose two regularizers, namely
node-wise domain adversarial training (NodeDAT) and emotion-aware distribution
learning (EmotionDL), to better handle cross-subject EEG variations and noisy
labels, respectively. Extensive experiments on two public datasets, SEED and
SEED-IV, demonstrate the superior performance of our model than
state-of-the-art models in most experimental settings. Moreover, ablation
studies show that the proposed adjacency matrix and two regularizers contribute
consistent and significant gain to the performance of our RGNN model. Finally,
investigations on the neuronal activities reveal important brain regions and
inter-channel relations for EEG-based emotion recognition
A computer-aided diagnosis of multiple sclerosis based on mfVEP recordings.
Introduction: The aim of this study is to develop a computer-aided diagnosis system to identify subjects at differing stages of development of multiple sclerosis (MS) using multifocal visual-evoked potentials (mfVEPs). Using an automatic classifier, diagnosis is performed first on the eyes and then on the subjects.
Patients: MfVEP signals were obtained from patients with Radiologically Isolated Syndrome (RIS) (n = 30 eyes), patients with Clinically Isolated Syndrome (CIS) (n = 62 eyes), patients with definite MS (n = 56 eyes) and 22 control subjects (n = 44 eyes). The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON).
Methods: For individual eye diagnosis, a feature vector was formed with information about the intensity, latency and singular values of the mfVEP signals. A flat multiclass classifier (FMC) and a hierarchical classifier (HC) were tested and both were implemented using the k-Nearest Neighbour (k-NN) algorithm. The output of the best eye classifier was used to classify the subjects. In the event of divergence, the eye with the best mfVEP recording was selected.
Results: In the eye classifier, the HC performed better than the FMC (accuracy = 0.74 and extended Matthew Correlation Coefficient (MCC) = 0.68). In the subject classification, accuracy = 0.95 and MCC = 0.93, confirming that it may be a promising tool for MS diagnosis. Chirped-pulse φOTDR provides distributed strain measurement via a time-delay estimation process. We propose a lower bound for performance, after reducing sampling error and compensating phase-noise. We attempt to reach the limit, attaining unprecedented pε/√Hz sensitivities.
Conclusion: In addition to amplitude (axonal loss) and latency (demyelination), it has shown that the singular values of the mfVEP signals provide discriminatory information that may be used to identify subjects with differing degrees of the disease.Secretaría de Estado de Investigación, Desarrollo e InnovaciónInstituto de Salud Carlos II
Components of Soft Computing for Epileptic Seizure Prediction and Detection
Components of soft computing include machine learning, fuzzy logic, evolutionary computation, and probabilistic theory. These components have the cognitive ability to learn effectively. They deal with imprecision and good tolerance of uncertainty. Components of soft computing are needed for developing automated expert systems. These systems reduce human interventions so as to complete a task essentially. Automated expert systems are developed in order to perform difficult jobs. The systems have been trained and tested using soft computing techniques. These systems are required in all kinds of fields and are especially very useful in medical diagnosis. This chapter describes the components of soft computing and review of some analyses regarding EEG signal classification. From those analyses, this chapter concludes that a number of features extracted are very important and relevant features for classifier can give better accuracy of classification. The classifier with a suitable learning method can perform well for automated epileptic seizure detection systems. Further, the decomposition of EEG signal at level 4 is sufficient for seizure detection
Operator State Estimation for Adaptive Aiding in Uninhabited Combat Air Vehicles
This research demonstrated the first closed-loop implementation of adaptive automation using operator functional state in an operationally relevant environment. In the Uninhabited Combat Air Vehicle (UCAV) environment, operators can become cognitively overloaded and their performance may decrease during mission critical events. This research demonstrates an unprecedented closed-loop system, one that adaptively aids UCAV operators based on their cognitive functional state A series of experiments were conducted to 1) determine the best classifiers for estimating operator functional state, 2) determine if physiological measures can be used to develop multiple cognitive models based on information processing demands and task type, 3) determine the salient psychophysiological measures in operator functional state, and 4) demonstrate the benefits of intelligent adaptive aiding using operator functional state. Aiding the operator actually improved performance and increased mission effectiveness by 67%
Integrated Machine Learning Approaches to Improve Classification performance and Feature Extraction Process for EEG Dataset
Epileptic seizure or epilepsy is a chronic neurological disorder that occurs due to brain neurons\u27 abnormal activities and has affected approximately 50 million people worldwide. Epilepsy can affect patients’ health and lead to life-threatening emergencies. Early detection of epilepsy is highly effective in avoiding seizures by intervening in treatment. The electroencephalogram (EEG) signal, which contains valuable information of electrical activity in the brain, is a standard neuroimaging tool used by clinicians to monitor and diagnose epilepsy. Visually inspecting the EEG signal is an expensive, tedious, and error-prone practice. Moreover, the result varies with different neurophysiologists for an identical reading. Thus, automatically classifying epilepsy into different epileptic states with a high accuracy rate is an urgent requirement and has long been investigated. This PhD thesis contributes to the epileptic seizure detection problem using Machine Learning (ML) techniques.
Machine learning algorithms have been implemented to automatically classifying epilepsy from EEG data. Imbalance class distribution problems and effective feature extraction from the EEG signals are the two major concerns towards effectively and efficiently applying machine learning algorithms for epilepsy classification. The algorithms exhibit biased results towards the majority class when classes are imbalanced, while effective feature extraction can improve classification performance.
In this thesis, we presented three different novel frameworks to effectively classify epileptic states while addressing the above issues. Firstly, a deep neural network-based framework exploring different sampling techniques was proposed where both traditional and state-of-the-art sampling techniques were experimented with and evaluated for their capability of improving the imbalance ratio and classification performance. Secondly, a novel integrated machine learning-based framework was proposed to effectively learn from EEG imbalanced data leveraging the Principal Component Analysis method to extract high- and low-variant principal components, which are empirically customized for the imbalanced data classification. This study showed that principal components associated with low variances can capture implicit patterns of the minority class of a dataset. Next, we proposed a novel framework to effectively classify epilepsy leveraging summary statistics analysis of window-based features of EEG signals. The framework first denoised the signals using power spectrum density analysis and replaced outliers with k-NN imputer. Next, window level features were extracted from statistical, temporal, and spectral domains. Basic summary statistics are then computed from the extracted features to feed into different machine learning classifiers. An optimal set of features are selected leveraging variance thresholding and dropping correlated features before feeding the features for classification.
Finally, we applied traditional machine learning classifiers such as Support Vector Machine, Decision Tree, Random Forest, and k-Nearest Neighbors along with Deep Neural Networks to classify epilepsy. We experimented the frameworks with a benchmark dataset through rigorous experimental settings and displayed the effectiveness of the proposed frameworks in terms of accuracy, precision, recall, and F-beta score
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