EEG-Based Prediction of the Recovery of Carotid Blood Flow during Cardiopulmonary Resuscitation in a Swine Model

The recovery of cerebral circulation during cardiopulmonary resuscitation (CPR) is important to improve the neurologic outcomes of cardiac arrest patients. To evaluate the feasibility of an electroencephalogram (EEG)-based prediction model as a CPR feedback indicator of high- or low-CBF carotid blood flow (CBF), the frontal EEG and hemodynamic data including CBF were measured during animal experiments with a ventricular fibrillation (VF) swine model. The most significant 10 EEG parameters in the time, frequency and entropy domains were determined by neighborhood component analysis and Student’s t-test for discriminating high- or low-CBF recovery with a division criterion of 30%. As a binary CBF classifier, the performances of logistic regression, support vector machine (SVM), k-nearest neighbor, random forest and multilayer perceptron algorithms were compared with eight-fold cross-validation. The three-order polynomial kernel-based SVM model showed the best accuracy of 0.853. The sensitivity, specificity, F1 score and area under the curve of the SVM model were 0.807, 0.906, 0.853 and 0.909, respectively. An automated CBF classifier derived from non-invasive EEG is feasible as a potential indicator of the CBF recovery during CPR in a VF swine model.

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An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

Sensors ◽

10.3390/s20216365 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6365

Author(s):

Jung Hwan Kim ◽

Chul Min Kim ◽

Man-Sung Yim

Keyword(s):

Nearest Neighbor ◽

Nuclear Industry ◽

Classification Model ◽

Support Vector ◽

Insider Threat ◽

K Nearest Neighbor ◽

Eeg Signals ◽

Nuclear Facilities ◽

Time Frequency ◽

Threat Mitigation

This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using subject-wise classification. Based on electroencephalography (EEG) signals, a classification model was developed to identify whether a subject has a malicious intention under scenarios of being forced to become an insider threat. The model also distinguishes insider threat scenarios from everyday conflict scenarios. To support model development, 21-channel EEG signals were measured on 25 healthy subjects, and sets of features were extracted from the time, time–frequency, frequency and nonlinear domains. To select the best use of the available features, automatic selection was performed by random-forest-based algorithms. The k-nearest neighbor, support vector machine with radial kernel, naïve Bayes, and multilayer perceptron algorithms were applied for the classification. By using EEG signals obtained while contemplating becoming an insider threat, the subject-wise model identified malicious intentions with 78.57% accuracy. The model also distinguished insider threat scenarios from everyday conflict scenarios with 93.47% accuracy. These findings could be utilized to support the development of insider threat mitigation systems along with existing trustworthiness assessments in the nuclear industry.

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Fatigue Detection on Face Image Using FaceNet Algorithm and K-Nearest Neighbor Classifier

Journal of Information Systems Engineering and Business Intelligence ◽

10.20473/jisebi.7.1.22-30 ◽

2021 ◽

Vol 7 (1) ◽

pp. 22

Author(s):

Faisal Dharma Adhinata ◽

Diovianto Putra Rakhmadani ◽

Danur Wijayanto

Keyword(s):

Nearest Neighbor ◽

Detection System ◽

Well Being ◽

Extraction Process ◽

Polynomial Kernel ◽

Support Vector ◽

K Nearest Neighbor ◽

Time Data ◽

Multiclass Svm ◽

Fatigue Detection

Background: The COVID-19 pandemic has made people spend more time on online meetings more than ever. The prolonged time looking at the monitor may cause fatigue, which can subsequently impact the mental and physical health. A fatigue detection system is needed to monitor the Internet users well-being. Previous research related to the fatigue detection system used a fuzzy system, but the accuracy was below 85%. In this research, machine learning is used to improve accuracy.Objective: This research examines the combination of the FaceNet algorithm with either k-nearest neighbor (K-NN) or multiclass support vector machine (SVM) to improve the accuracy.Methods: In this study, we used the UTA-RLDD dataset. The features used for fatigue detection come from the face, so the dataset is segmented using the Haar Cascades method, which is then resized. The feature extraction process uses FaceNet's pre-trained algorithm. The extracted features are classified into three classes—focused, unfocused, and fatigue—using the K-NN or multiclass SVM method.Results: The combination between the FaceNet algorithm and K-NN, with a value of resulted in a better accuracy than the FaceNet algorithm with multiclass SVM with the polynomial kernel (at 94.68% and 89.87% respectively). The processing speed of both combinations of methods has allowed for real-time data processing.Conclusion: This research provides an overview of methods for early fatigue detection while working at the computer so that we can limit staring at the computer screen too long and switch places to maintain the health of our eyes.

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Classification Methods for Airborne Disease Spores from Greenhouse Crops Based on Multifeature Fusion

Applied Sciences ◽

10.3390/app10217850 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7850 ◽

Cited By ~ 1

Author(s):

Yafei Wang ◽

Xiaoxue Du ◽

Guoxin Ma ◽

Yong Liu ◽

Bin Wang ◽

...

Keyword(s):

Nearest Neighbor ◽

Fungal Spores ◽

Recall Rate ◽

Support Vector ◽

Morphological Operations ◽

K Nearest Neighbor ◽

Image Preprocessing ◽

Greenhouse Crops ◽

Svm Model ◽

Airborne Disease

Airborne fungal spores have always played an important role in the spread of fungal crop diseases, causing great concern. The traditional microscopic spore classification method mainly relies on naked eye observations and classification by professional and technical personnel in a laboratory. Due to the large number of spores captured, this method is labor-intensive, time-consuming, and inefficient, and sometimes leads to huge errors. Thus, an alternative method is required. In this study, a method was proposed to identify airborne disease spores from greenhouse crops using digital image processing. First, in an indoor simulation, images of airborne disease spores from three greenhouse crops were collected using portable volumetric spore traps. Then, a series of image preprocessing methods were used to identify the spores, including mean filtering, Gaussian filtering, OTSU (maximum between-class variance) method binarization, morphological operations, and mask operations. After image preprocessing, 90 features of the spores were extracted, including color, shape, and texture features. Based on these features, logistics regression (LR), K nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classification models were built. The test results showed that the average accuracy rates for the 3 classes of disease spores using the SVM model, LR model, KNN model, and RF model were 94.36%, 90.13%, 89.37%, and 89.23%, respectively. The harmonic average of the accuracy and the recall rate value (F value) were higher for the SVM model and its overall average value reached 91.68%, which was 2.03, 3.59, and 3.96 percentage points higher than the LR model, KNN model, and RF model, respectively. Therefore, this method can effectively identify 3 classes of diseases spores and this study can provide a reference for the identification of greenhouse disease spores.

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Diagnosis of amyotrophic lateral sclerosis (ALS) disorders based on electromyogram (EMG) signal analysis and feature selection

Polish Journal of Medical Physics and Engineering ◽

10.2478/pjmpe-2020-0018 ◽

2020 ◽

Vol 26 (3) ◽

pp. 155-160

Author(s):

Aicha Mokdad ◽

Sidi Mohammed El Amine Debbal ◽

Fadia Meziani

Keyword(s):

Nearest Neighbor ◽

Biceps Brachii ◽

Neuromuscular Disorders ◽

Machine Learning Algorithms ◽

Support Vector ◽

Svm Classifier ◽

K Nearest Neighbor ◽

Time Frequency ◽

Emg Signal ◽

Frequency Features

AbstractElectromyogram signal (EMG) provides an important source of information for the diagnosis of neuromuscular disorders. In this study, we proposed two methods of analysis which concern the bispectrum and continuous wavelet transform (CWT) of the EMG signal then a comparison is made to select which one is the most suitable to identify an abnormality in biceps brachii muscle in the main purpose is to assess the pathological severity in bifrequency and time-frequency analysis applying respectively bispectrum and CWT. Then four time and frequency features are extracted and three popular machine learning algorithms are implemented to differentiate neuropathy and healthy conditions of the selected muscle. The performance of these time and frequency features are compared using support vector machine (SVM), linear discriminate analysis (LDA) and K-Nearest Neighbor (KNN) classifier performance. The results obtained showed that the SVM classifier yielded the best performance with an accuracy of 95.8%, precision of 92.59% and specificity of 92%. followed by respectively KNN and LDA classifier that achieved respectively an accuracy of 92% and 91.5%, precision of 92% and 85.4%, and specificity of 92% and 83%.

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Depression Detection Based on Geometrical Features Extracted from SODP Shape of EEG Signals and Binary PSO

Traitement du signal ◽

10.18280/ts.380102 ◽

2021 ◽

Vol 38 (1) ◽

pp. 13-26

Author(s):

Hesam Akbari ◽

Muhammad Tariq Sadiq ◽

Malih Payan ◽

Somayeh Saraf Esmaili ◽

Hourieh Baghri ◽

...

Keyword(s):

Early Diagnosis ◽

Classification Accuracy ◽

Nearest Neighbor ◽

Support Vector ◽

Binary Particle Swarm Optimization ◽

K Nearest Neighbor ◽

Eeg Signals ◽

Central Tendency Measure ◽

Time Frequency ◽

Geometrical Features

Late detection of depression is having detrimental consequences including suicide thus there is a serious need for an accurate computer-aided system for early diagnosis of depression. In this research, we suggested a novel strategy for the diagnosis of depression based on several geometric features derived from the Electroencephalography (EEG) signal shape of the second-order differential plot (SODP). First, various geometrical features of normal and depression EEG signals were derived from SODP including standard descriptors, a summation of the angles between consecutive vectors, a summation of distances to coordinate, a summation of the triangle area using three successive points, a summation of the shortest distance from each point relative to the 45-degree line, a summation of the centroids to centroid distance of successive triangles, central tendency measure and summation of successive vector lengths. Second, Binary Particle Swarm Optimization was utilized for the selection of suitable features. At last, the features were fed to support vector machine and k-nearest neighbor (KNN) classifiers for the identification of normal and depressed signals. The performance of the proposed framework was evaluated by the recorded bipolar EEG signals from 22 normal and 22 depressed subjects. The results provide an average classification accuracy of 98.79% with the KNN classifier using city-block distance in a ten-fold cross-validation strategy. The proposed system is accurate and can be used for the early diagnosis of depression. We showed that the proposed geometrical features are better than extracted features in the time, frequency, time-frequency domains as it helps in visual inspection and provide up to 17.56% improvement in classification accuracy in contrast to those features.

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A Comparative Analysis of Artificial Intelligence-Based Methods for Fault Diagnosis of Mechanical Systems

Mechanics and Mechanical Engineering ◽

10.2478/mme-2019-0016 ◽

2019 ◽

Vol 23 (1) ◽

pp. 113-124

Author(s):

Reihaneh Kardehi Moghaddam ◽

Navid Moshtaghi Yazdan

Keyword(s):

Artificial Intelligence ◽

Fault Diagnosis ◽

Nearest Neighbor ◽

Composite Plates ◽

Classification Systems ◽

Support Vector ◽

K Nearest Neighbor ◽

Inference System ◽

Time Frequency ◽

Number Of Iterations

Abstract The present research studied fault diagnosis of composite sheets using vibration signal processing and artificial intelligence (AI)-based methods. To this end, vibration signals were collected from sound and faulty composite plates. Using different time-frequency signal analysis and processing methods, a number of features were extracted from these signals and the most effective features containing further information on these composite plates were provided as input to different classification systems. The output of these classification systems reveals the faults in composite plates. The different types of classification systems used in this research were the support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), k-nearest neighbor (k-NN), artificial neural networks (ANNs), Extended Classifier System (XCS) algorithm, and the proposed improved XCS algorithm. The research results were reflective of the superiority of ANFIS in terms of precision, while this method had the highest process duration with an equal number of iterations. The precision of the proposed improved XCS method was lower than that of ANFIS, but the duration of the process was shorter than the ANFIS method with an equal number of iterations.

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Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

Brain Sciences ◽

10.3390/brainsci11081049 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1049

Author(s):

Katrin Trentzsch ◽

Paula Schumann ◽

Grzegorz Śliwiński ◽

Paul Bartscht ◽

Rocco Haase ◽

...

Keyword(s):

Machine Learning ◽

Multiple Sclerosis ◽

Nearest Neighbor ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Polynomial Kernel ◽

Support Vector ◽

Healthy Controls ◽

K Nearest Neighbor ◽

Gait Patterns

In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (k = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (k = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.

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The Enhancement on Stress Levels Based on Physiological Signal and Self-Stress Assessment

Traitement du signal ◽

10.18280/ts.380519 ◽

2021 ◽

Vol 38 (5) ◽

pp. 1439-1447

Author(s):

Zarith Liyana Zahari ◽

Mahfuzah Mustafa ◽

Zaridah Mat Zain ◽

Rafiuddin Abdubrani ◽

Faradila Naim

Keyword(s):

Frequency Domain ◽

Nearest Neighbor ◽

Support Vector ◽

K Nearest Neighbor ◽

Time Frequency ◽

Physiological Signal ◽

Signal Features ◽

Stress Levels ◽

Matthew Correlation Coefficient ◽

Electrophysiological Signal

The prolonged stress needs to be determined and controlled before it harms the physical and mental conditions. This research used questionnaire and physiological approaches in determine stress. EEG signal is an electrophysiological signal to analyze the signal features. The standard features used are peak-to-peak values, mean, standard deviation and root means square (RMS). The unique features in this research are Matthew Correlation Coefficient Advanced (MCCA) and multimodal capabilities in the area of frequency and time-frequency analysis are proposed. In the frequency domain, Power Spectral Density (PSD) techniques were applied while Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were utilized to extract seven features based on time-frequency domain. Various methods applied from previous works are still limited by the stress indices. The merged works between quantities score and physiological measurements were enhanced the stress level from three-levels to six stress levels based on music application will be the second contribution. To validate the proposed method and enhance performance between electroencephalogram (EEG) signals and stress score, support vector machine (SVM), random forest (RF), K- nearest neighbor (KNN) classifier is needed. From the finding, RF gained the best performance average accuracy 85% ±10% in Ten-fold and K-fold techniques compared with SVM and KNN.

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Classification-Based Fuel Injection Fault Detection of a Trainset Diesel Engine Using Vibration Signature Analysis

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4046270 ◽

2020 ◽

Vol 142 (5) ◽

Cited By ~ 1

Author(s):

Moosa Ayati ◽

Farzad A. Shirazi ◽

Saeed Ansari-Rad ◽

Alireza Zabihihesari

Keyword(s):

Diesel Engine ◽

Fault Detection ◽

Diesel Engines ◽

Nearest Neighbor ◽

Fuel Injection ◽

Support Vector ◽

K Nearest Neighbor ◽

Vibration Signals ◽

Feature Extraction Method ◽

Time Frequency

Abstract Diesel engines are crucial components of trainsets. Automated fault detection of diesel engines can play an important role for increasing reliability of passenger trains. In this research, vibration-based fuel injection fault detection of a high-power 12-cylinder trainset diesel engine is studied. Vibration signals are analyzed in frequency and time-frequency domains to obtain possible patterns of faults. Fast Fourier transform (FFT) and wavelet packet transform (WPT) of vibration signals are used to extract several uncorrelated features. These features are chosen to increase the ability of classifiers to separate healthy and faulty engine sides, automatically. Different classification methods including multilayer perception (MLP), support vector machines (SVM), K-nearest neighbor (KNN), and local linear model tree (LOLIMOT) are used to process captured features; these methods are utilized in both “Single-sensor condition monitoring” and “Classification and fault detection” sections. It is shown that KNN networks are practical tools in the proposed fault detection procedure. The main novelty of this work comes from introducing a rich feature-extraction method based on a combination of FFT and db4 features. In addition, the complexity of computations and average running-time decrease while classification accuracy in the fuel injection fault detection procedure increases.

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Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor

Sensors ◽

10.3390/s20236897 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6897

Author(s):

Lilia Aljihmani ◽

Oussama Kerdjidj ◽

Yibo Zhu ◽

Ranjana K. Mehta ◽

Madhav Erraguntla ◽

...

Keyword(s):

Nearest Neighbor ◽

Ensemble Classifier ◽

Machine Learning Techniques ◽

Support Vector ◽

Dominant Hand ◽

Ensemble Classifiers ◽

Window Length ◽

K Nearest Neighbor ◽

Hand Tremor ◽

Time Frequency

Fatigue is defined as “a loss of force-generating capacity” in a muscle that can intensify tremor. Tremor quantification can facilitate early detection of fatigue onset so that preventative or corrective controls can be taken to minimize work-related injuries and improve the performance of tasks that require high-levels of accuracy. We focused on developing a system that recognizes and classifies voluntary effort and detects phases of fatigue. The experiment was designed to extract and evaluate hand-tremor data during the performance of both rest and effort tasks. The data were collected from the wrist and finger of the participant’s dominant hand. To investigate tremor, time, frequency domain features were extracted from the accelerometer signal for segments of 45 and 90 samples/window. Analysis using advanced signal processing and machine-learning techniques such as decision tree, k-nearest neighbor, support vector machine, and ensemble classifiers were applied to discover models to classify rest and effort tasks and the phases of fatigue. Evaluation of the classifier’s performance was assessed based on various metrics using 5-fold cross-validation. The recognition of rest and effort tasks using an ensemble classifier based on the random subspace and window length of 45 samples was deemed to be the most accurate (96.1%). The highest accuracy (~98%) that distinguished between early and late fatigue phases was achieved using the same classifier and window length.

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