scholarly journals EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Bingtao Zhang ◽  
Tao Lei ◽  
Hong Liu ◽  
Hanshu Cai
Keyword(s):  
Visual Inspection ◽  
Sleep Stages ◽  
Subjective Time ◽  
Sleep Staging ◽  
Large Bulk ◽  
State Classification ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Eeg Features

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments.

scholarly journals Multi-Center Validation Study of Automated Classification of Pathological Slowing in Adult Scalp Electroencephalograms Via Frequency Features

2021 ◽  
pp. 2150016
Author(s):  
Wei Yan Peh ◽  
John Thomas ◽  
Elham Bagheri ◽  
Rima Chaudhari ◽  
Sagar Karia ◽  
...  
Keyword(s):  
Neurological Disorders ◽  
Visual Inspection ◽  
Detection System ◽  
Automated Classification ◽  
The Us ◽  
Electroencephalogram Eeg ◽  
Level Performance ◽  
Frequency Features ◽  
Channel Segment

Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose three automated approaches to detect slowing in EEG: Threshold-based Detection System (TDS), Shallow Learning-based Detection System (SLDS), and Deep Learning-based Detection System (DLDS). These systems are evaluated on channel-, segment-, and EEG-level. The three systems perform prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level. We evaluate the systems through Leave-One-Subject-Out (LOSO) cross-validation (CV) and Leave-One-Institution-Out (LOIO) CV on four datasets from the US, Singapore, and India. The DLDS achieved the best overall results: LOIO CV mean balanced accuracy (BAC) of 71.9%, 75.5%, and 82.0% at channel-, segment- and EEG-level, and LOSO CV mean BAC of 73.6%, 77.2%, and 81.8% at channel-, segment-, and EEG-level. The channel- and segment-level performance is comparable to the intra-rater agreement (IRA) of an expert of 72.4% and 82%. The DLDS can process a 30 min EEG in 4 s and can be deployed to assist clinicians in interpreting EEGs.

scholarly journals Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 988
Author(s):  
Ho-Seung Cha ◽  
Chang-Hee Han ◽  
Chang-Hwan Im
Keyword(s):  
Resting State ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Mental States ◽  
Computer Interface ◽  
Practical Applications ◽  
Eeg Data ◽  
Brain States ◽  
Electroencephalogram Eeg ◽  
Eeg Features

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

scholarly journals EyeBallGUI: A Tool for Visual Inspection and Binary Marking of Multi-channel Bio-signals

2017 ◽  
Author(s):  
Kieran S. Mohr ◽  
Bahman Nasseroleslami ◽  
Parameswaran M. Iyer ◽  
Orla Hardiman ◽  
Edmund C. Lalor
Keyword(s):  
Visual Inspection ◽  
Binary Classification ◽  
Analysis Tool ◽  
Data Retention ◽  
Wide Range ◽  
Sample Data ◽  
Electroencephalogram Eeg ◽  
Free Open Source ◽  
Selection Of

AbstractA wide range of studies in human neuroscience rely on the analysis of electrophysiological bio-signals such as electroencephalogram (EEG) where customized data analysis may require supervised artefact rejection, binary marking through visual inspection, selection of noise and artefact samples for pre-processing algorithms, and selection of clinically-relevant signal segments in neurological conditions. Nevertheless, the existing preprocessing tools do not provide the needed flexibility to handle such tasks efficiently. We therefore developed a free open-source Graphical User Interface (GUI), EyeBallGUI, that allows visualization and flexible, manual marking (binary classification) of multi-channel bio-signal data. EyeBallGUI, developed for MATLAB®, allows the user to interactively and accurately inspect and mark multi-channel digitized data with no restriction on marking periods of data in subsets of channels (a restriction in place in existing tools). The new tool facilitates precise, manual marking of bio-signals by allowing any desired segment of data to be marked in any subset of channels. It is therefore of utility in circumstances where such flexibility is essential. The developed GUI is an auxiliary analysis tool that shall facilitate neural signal (pre-)processing applications where it is desirable to perform accurate supervised artefact rejection, flexible data marking for increased data retention yield, extraction of specific signal segments by expert users from sample data, or labeling of data for clinical and scientific research purposes.

Electroencephalogram monitoring in the critically ill

2016 ◽  
Author(s):  
Paul M. Vespa
Keyword(s):  
Brain Function ◽  
Visual Inspection ◽  
Perfusion Pressure ◽  
Quantitative Eeg ◽  
Rapid Identification ◽  
Brain Ischaemia ◽  
Eeg Data ◽  
Continuous Eeg ◽  
Processed Eeg ◽  
Electroencephalogram Eeg

Electroencephalography monitoring provides a method for monitoring brain function, which can complement other forms of monitoring, such as monitoring of intracranial pressure and derived parameters, such as cerebral perfusion pressure. Continuous electroencephalogram (EEG) monitoring can be helpful in seizure detection after brain injury and coma. Seizures can be detected by visual inspection of the raw EEG and/or processed EEG data. Treatment of status epilepticus can be improved by rapid identification and abolition of seizures using continuous EEG. Quantitative EEG can also be used to detect brain ischaemia and seizures, to monitor sedation and aid prognosis.

Epilepsy EEG Classification Based on Convolution Support Vector Machine

2021 ◽  
Vol 11 (1) ◽  
pp. 25-32
Author(s):  
Qi Xin ◽  
Shaohai Hu ◽  
Shuaiqi Liu ◽  
Xiaole Ma ◽  
Hui Lv ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Focal Epilepsy ◽  
Principal Component ◽  
Support Vector ◽  
Eeg Signals ◽  
Eeg Classification ◽  
Eeg Data ◽  
Epilepsy Diagnosis ◽  
Electroencephalogram Eeg

Clinical Electroencephalogram (EEG) data is of great significance to realize automatable detection, recognition and diagnosis to reduce the valuable diagnosis time. To make a classification of epilepsy, we constructed convolution support vector machine (CSVM) by integrating the advantages of convolutional neural networks (CNN) and support vector machine (SVM). To distinguish the focal and non-focal epilepsy EEG signals, we firstly reduced the dimensionality of EEG signals by using principal component analysis (PCA). After that, we classified the epilepsy EEG signals by the CSVM. The accuracy, sensitivity and specificity of our method reach up to 99.56%, 99.72% and 99.52% respectively, which are competitive than the widely acceptable algorithms. The proposed automatic end to end epilepsy EEG signals classification algorithm provides a better reference for clinical epilepsy diagnosis.

scholarly journals A Study on Sensitive Bands of EEG Data under Different Mental Workloads

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 145
Author(s):  
Hongquan Qu ◽  
Zhanli Fan ◽  
Shuqin Cao ◽  
Liping Pang ◽  
Hao Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Power Spectral ◽  
Eeg Data ◽  
Vector Machines ◽  
Full Band ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads.

scholarly journals Deep learning for classification of sleep EEG data during the epidemic of Coronavirus Disease

2020 ◽  
Vol 1 (2) ◽  
pp. 01-05
Author(s):  
Bin Zhao
Keyword(s):  
The Body ◽  
Sleep Eeg ◽  
Quality Of Sleep ◽  
Eeg Signals ◽  
Eeg Data ◽  
The Impact ◽  
Electroencephalogram Eeg ◽  
Important Basis

Sleep is an important part of the body's recuperation and energy accumulation, and the quality of sleep also has a significant impact on people's physical and mental state during the epidemic of Coronavirus Disease. It has attracted increasing attention how to improve the quality of sleep and reduce the impact of sleep related diseases on health. The electroencephalogram (EEG) signals collected during sleep belong to spontaneous EEG signals. Spontaneous sleep EEG signals can reflect the body own changes, which is also an important basis for diagnosis and treatment of related diseases. Therefore, the establishment of an effective model for classifying sleep EEG signals is an important auxiliary tool for evaluating sleep.

scholarly journals A Comparison Study on Multidomain EEG Features for Sleep Stage Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yu Zhang ◽  
Bei Wang ◽  
Jin Jing ◽  
Jian Zhang ◽  
Junzhong Zou ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Time Domain ◽  
Visual Inspection ◽  
Sleep Stage ◽  
Domain Analysis ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Stage Classification ◽  
Sleep Stage Classification

Feature extraction from physiological signals of EEG (electroencephalogram) is an essential part for sleep staging. In this study, multidomain feature extraction was investigated based on time domain analysis, nonlinear analysis, and frequency domain analysis. Unlike the traditional feature calculation in time domain, a sequence merging method was developed as a preprocessing procedure. The objective is to eliminate the clutter waveform and highlight the characteristic waveform for further analysis. The numbers of the characteristic activities were extracted as the features from time domain. The contributions of features from different domains to the sleep stages were compared. The effectiveness was further analyzed by automatic sleep stage classification and compared with the visual inspection. The overnight clinical sleep EEG recordings of 3 patients after the treatment of Continuous Positive Airway Pressure (CPAP) were tested. The obtained results showed that the developed method can highlight the characteristic activity which is useful for both automatic sleep staging and visual inspection. Furthermore, it can be a training tool for better understanding the appearance of characteristic waveforms from raw sleep EEG which is mixed and complex in time domain.

scholarly journals Paroxysmal fast activity: Does this EEG pattern occur only in Lennox-Gastaut syndrome?

2019 ◽  
Author(s):  
Claudia M.P. Bento ◽  
Ana Carolina Coan ◽  
Marilisa M Guerreiro
Keyword(s):  
Spatial Distribution ◽  
Statistical Analysis ◽  
Fast Activity ◽  
Epileptic Syndromes ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Eeg Pattern ◽  
Eeg Features ◽  
Distribution Frequency

Background: Paroxysmal fast activity (PFA) is defined by fast paroxysmal events in the electroencephalogram (EEG), usually associated with Lennox-Gastaut syndrome (LGS). Our aims were to verify the frequency of LGS and non-LGS in EEGs with PFA; and to correlate the EEG features (spatial distribution, frequency, amplitude and duration) between the two clinical groups. Methods: We analyzed 38 EEG tracings with PFA from 38 patients. We evaluated the spatial distribution, frequency, amplitude and duration of fast paroxysms. The two clinical groups (LGS and non-LGS) were statistically compared relative to the EEG data. Results: With regard to epileptic syndromes, 23 patients (60%) were classified as LGS and 15 patients (40%) as non-LGS. Concerning spatial distribution, our results showed that 86.8% of the examinations showed symmetrical PFA and 13.2% showed asymmetrical PFA. The statistical analysis did not show any difference between the two groups regarding the EEG spatial distribution or other EEG data. Conclusions: PFA can occur in other epileptic syndromes apart from LGS. The EEG features did not offer any distinction between the two clinical groups. The PFA is not a specific EEG marker of LGS.  

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