scholarly journals EOG feature relevance determination for microsleep detection

2017 ◽  
Vol 3 (2) ◽  
pp. 815-818
Author(s):  
Martin Golz ◽  
Sebastian Wollner ◽  
David Sommer ◽  
Sebastian Schnieder
Keyword(s):  
Cross Correlation ◽  
Feature Reduction ◽  
Support Vector ◽  
Spectral Bands ◽  
Automatic Relevance Determination ◽  
Signal Features ◽  
Power Spectral ◽  
Vector Machines ◽  
Power Spectral Densities ◽  
Feature Relevance

AbstractAutomatic relevance determination (ARD) was applied to two-channel EOG recordings for microsleep event (MSE) recognition. 10 s immediately before MSE and also before counterexamples of fatigued, but attentive driving were analysed. Two type of signal features were extracted: the maximum cross correlation (MaxCC) and logarithmic power spectral densities (PSD) averaged in spectral bands of 0.5 Hz width ranging between 0 and 8 Hz. Generalised learn-ing vector quantisation (GRLVQ) was used as ARD method to show the potential of feature reduction. This is compared to support-vector machines (SVM), in which the feature reduction plays a much smaller role. Cross validation yielded mean normalised relevancies of PSD features in the range of 1.6 - 4.9 % and 1.9 - 10.4 % for horizontal and vertical EOG, respectively. MaxCC relevancies were 0.002 - 0.006 % and 0.002 - 0.06 %, respectively. This shows that PSD features of vertical EOG are indispensable, whereas MaxCC can be neglected. Mean classification accuracies were estimated at 86.6±b 1.3 % and 92.3±b 0.2 % for GRLVQ and SVM, respec-tively. GRLVQ permits objective feature reduction by inclu-sion of all processing stages, but is not as accurate as SVM.

scholarly journals EOG feature relevance determination for microsleep detection

2017 ◽  
Vol 3 (2) ◽  
pp. 261-264
Author(s):  
Martin Golz ◽  
Sebastian Wollner ◽  
David Sommer ◽  
Sebastian Schnieder
Keyword(s):  
Cross Correlation ◽  
Feature Reduction ◽  
Support Vector ◽  
Spectral Bands ◽  
Automatic Relevance Determination ◽  
Signal Features ◽  
Power Spectral ◽  
Vector Machines ◽  
Power Spectral Densities ◽  
Feature Relevance

AbstractAutomatic relevance determination (ARD) was applied to two-channel EOG recordings for microsleep event (MSE) recognition. 10 s immediately before MSE and also before counterexamples of fatigued, but attentive driving were analysed. Two type of signal features were extracted: the maximum cross correlation (MaxCC) and logarithmic power spectral densities (PSD) averaged in spectral bands of 0.5 Hz width ranging between 0 and 8 Hz. Generalised learn-ing vector quantisation (GRLVQ) was used as ARD method to show the potential of feature reduction. This is compared to support-vector machines (SVM), in which the feature reduction plays a much smaller role. Cross validation yielded mean normalised relevancies of PSD features in the range of 1.6 – 4.9 % and 1.9 – 10.4 % for horizontal and vertical EOG, respectively. MaxCC relevancies were 0.002 – 0.006 % and 0.002 – 0.06 %, respectively. This shows that PSD features of vertical EOG are indispensable, whereas MaxCC can be neglected. Mean classification accuracies were estimated at 86.6±b 1.3 % and 92.3±b 0.2 % for GRLVQ and SVM, respectively. GRLVQ permits objective feature reduction by inclusion of all processing stages, but is not as accurate as SVM.

scholarly journals Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals

2017 ◽  
Vol 3 (2) ◽  
pp. 563-567
Author(s):  
Christian Heinze ◽  
Constantin Hütterer ◽  
Thomas Schnupp ◽  
Gustavo Lenis ◽  
Martin Golz
Keyword(s):  
Empirical Distribution ◽  
Driving Simulation ◽  
Classification Error ◽  
Support Vector ◽  
Qt Intervals ◽  
Power Spectral ◽  
Vector Machines ◽  
Power Spectral Densities ◽  
The Mean ◽  
Driver Sleepiness

AbstractWe examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (19–32 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska sleepiness scale (KSS) were polled every five minutes. ECG recordings were divided into 5-min segments, each associated with the mean of one self- and two observer-KSS ratings. Those mean KSS values were binarized to obtain two classes not drowsy and drowsy. The Q-, R- and T-waves in the recordings were detected; R-peak positions were manually reviewed; the Q- and T-detection method was tested against the manual annotations of Physio-net’s QT database. Power spectral densities of RR intervals (RR-PSD) and quantiles of the empirical distribution of heart-rate corrected QTc intervals were estimated. Support-vector machines and random-holdout cross-validation were used for the estimation of the classification error. Using either RR-PSD or QTc features yielded mean test errors of 79.3 ± 0.3 % and 82.7 ± 0.5 %, respectively. Merging RR and QTc features improved the accuracy to 88.3 ± 0.2 %. QTc intervals of the class drowsy were generally prolonged com-pared to not drowsy. Our findings indicate that the inclusion of QT intervals contribute to the discrimination of driver sleepiness.

Support Vector Machines and Affective Science

2020 ◽  
Vol 12 (4) ◽  
pp. 297-308
Author(s):  
Chris H. Miller ◽  
Matthew D. Sacchet ◽  
Ian H. Gotlib
Keyword(s):  
Support Vector Machines ◽  
Reduction Technique ◽  
Feature Reduction ◽  
Data Driven ◽  
Future Research ◽  
Support Vector ◽  
Predictive Algorithm ◽  
Affective Science ◽  
Vector Machines ◽  
Potential Applications

Support vector machines (SVMs) are being used increasingly in affective science as a data-driven classification method and feature reduction technique. Whereas traditional statistical methods typically compare group averages on selected variables, SVMs use a predictive algorithm to learn multivariate patterns that optimally discriminate between groups. In this review, we provide a framework for understanding the methods of SVM-based analyses and summarize the findings of seminal studies that use SVMs for classification or data reduction in the behavioral and neural study of emotion and affective disorders. We conclude by discussing promising directions and potential applications of SVMs in future research in affective science.

Feature Reduction for Computationally Efficient Damage State Classification Using Binary Tree Support Vector Machines

2008 ◽  
Author(s):  
Clyde Coelho ◽  
Aditi Chattopadhyay
Keyword(s):  
Support Vector Machines ◽  
Binary Tree ◽  
Feature Reduction ◽  
Training Data ◽  
Support Vector ◽  
Computationally Efficient ◽  
Damage State ◽  
Data Set ◽  
Linear Discriminant ◽  
Vector Machines

This paper proposes a computationally efficient methodology for classifying damage in structural hotspots. Data collected from a sensor instrumented lug joint subjected to fatigue loading was preprocessed using a linear discriminant analysis (LDA) to extract features that are relevant for classification and reduce the dimensionality of the data. The data is then reduced in the feature space by analyzing the structure of the mapped clusters and removing the data points that do not affect the construction of interclass separating hyperplanes. The reduced data set is used to train a support vector machines (SVM) based classifier and the results of the classification problem are compared to those when the entire data set is used for training. To further improve the efficiency of the classification scheme, the SVM classifiers are arranged in a binary tree format to reduce the number of comparisons that are necessary. The experimental results show that the data reduction does not reduce the ability of the classifier to distinguish between classes while providing a nearly fourfold decrease in the amount of training data processed.

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.

SENSOR-FAULT TOLERANT CONDITION MONITORING OF AN INDUSTRIAL MACHINE

2013 ◽  
Vol 09 (01) ◽  
pp. 1350001 ◽  
Author(s):  
SRINIVAS RAMAN ◽  
CLARENCE W. DE SILVA
Keyword(s):  
Condition Monitoring ◽  
Fault Tolerant ◽  
Wavelet Packet ◽  
Experimental System ◽  
Feature Reduction ◽  
Support Vector ◽  
Sensor Failure ◽  
Failure Characteristics ◽  
Machine Vibration ◽  
Vector Machines

In this paper, a multi-sensor condition monitoring scheme is developed to diagnose machine faults in the presence of sensor failure. The signals from the monitored machine are decomposed using the wavelet packet transform (WPT). Two feature reduction schemes, using genetic algorithms are developed for feature selection in condition monitoring. One scheme assumes no prior knowledge about system costs or failure characteristics, and the other scheme aims to minimize the operating costs over a period of time. Two classifiers, radial basis function networks and support vector machines, are developed and compared in their ability to classify machine faults under conditions of sensor failure. The developed methodology is implemented in an experimental system, an industrial fish processing machine. The machine is instrumented with multiple accelerometers and microphones to continuously acquire signals of machine vibration and sound. The performance of the implemented fault diagnosis methodology is evaluated though experimentation.

scholarly journals An Advanced Partial Discharge Recognition Strategy of Power Cable

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaotian Bi ◽  
Ang Ren ◽  
Simeng Li ◽  
Mingming Han ◽  
Qingquan Li
Keyword(s):  
Partial Discharge ◽  
Fdtd Simulation ◽  
Support Vector ◽  
Power Cable ◽  
Signal Features ◽  
Accuracy Comparison ◽  
Training Samples ◽  
Vector Machines ◽  
Fractal Characteristics ◽  
Detection And Localization

Detection and localization of partial discharge are very important in condition monitoring of power cables, so it is necessary to build an accurate recognizer to recognize the discharge types. In this paper, firstly, a power cable model based on FDTD simulation is built to get the typical discharge signals as training samples. Secondly, because the extraction of discharge signal features is crucial, fractal characteristics of the training samples are extracted and inputted into the recognizer. To make the results more accurate, multi-SVM recognizer made up of six Support Vector Machines (SVM) is proposed in this paper. The result of the multi-SVM recognizer is determined by the vote of the six SVM. Finally, the BP neural networks and ELM are compared with multi-SVM. The accuracy comparison shows that the multi-SVM recognizer has the best accuracy and stability, and it can recognize the discharge type efficiently.

scholarly journals Inter-individual variability of EEG features during microsleep events

2019 ◽  
Vol 5 (1) ◽  
pp. 13-16 ◽  
Author(s):  
Martin Golz ◽  
Adolf Schenka ◽  
Florian Haselbeck ◽  
Martin Patrick Pauli
Keyword(s):  
Cross Validation ◽  
Individual Variability ◽  
Error Rates ◽  
Gradient Boosting ◽  
Support Vector ◽  
Unusual Behavior ◽  
Signal Features ◽  
Power Spectral ◽  
Driving Task ◽  
Eeg Features

AbstractThis paper examines the question of how strongly the spectral properties of the EEG during microsleep differ between individuals. For this purpose, 3859 microsleep examples were compared with 4044 counterexamples in which drivers were very drowsy but were able to perform the driving task. Two types of signal features were compared: logarithmic power spectral densities and entropy measures of wavelets coefficient series. Discriminant analyses were performed with the following machine learning methods: support-vector machines, gradient boosting, learning vector quantization. To the best of our knowledge, this is the first time that results of the leave-one-subject-out cross-validation (LOSO CV) for the detection of microsleep are presented. Error rates lower than 5.0 % resulted in 17 subjects and lower than 13 % in another 11 subjects. In 3 individuals, EEG features could not be explained by the pool of EEG features of all other individuals; for them, detection errors were 15.1 %, 17.1 %, and 27.0 %. In comparison, cross validation by means of repeated random subsampling, in which individuality is not considered, yielded mean error rates of 5.0 ± 0.5 %. A subsequent inspection of raw EEG data showed that in two individuals a bad signal quality due to poor electrode attachment could be the cause and in one individual a very unusual behavior, a high and long-lasting eyelid activity which interfered the recorded EEG in all channels.

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