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

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.

scholarly journals An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

Sensors ◽  
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.

scholarly journals Enhanced Performance of PCG Signal using Effective Feature Extraction Method

2020 ◽  
Vol 9 (2) ◽  
pp. 809-813
Keyword(s):  
Nearest Neighbor ◽  
Heart Sound ◽  
Biomedical Signal Processing ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Feature Extraction Method ◽  
Signal Features ◽  
Heart Auscultation ◽  
Noisy Background

Phonocardiography (PCG) is the realistic portrayal of sounds created in the heart auscultation. PCG is an improvement for ECG. Particularly in observing of patient and biomedical research, these signals need to do the diagnosis. This paper deals with the processing of heart sound signals i.e., Phonocardiography (PCG) Signals. The primary goal of analyzing these heart sound signals is to separate the signals from the noisy background and to extract some parameters which are used for patient monitoring and for other researches. Various momentum explore ventures are going on biomedical signal processing and its applications. The performance of the PCG signal will comprise of sectioning the signal into S1 and S2 and then compare, whether the PCG is normal or abnormal. In the previous framework the different change approaches are utilized to break down the PCG signal.In the primary stage, for include extraction; acquired heart sound signals were isolated to its subgroups utilizing discrete wavelet change with Level-1 to Level-10. This upgraded strategy proposes a best component for Heart Signal Features, which are removed and changed in to other area to arrange signals. This enhanced method proposes a best feature for Heart Signal Features, which are extracted and transformed in to other domain to classify signals. In the proposed strategy the Wavelet is utilized for highlight extraction and different Statistical strategies are utilized. InformationGain (IG), Mutual Information (MI) and so on. Feature selection techniques are compared using classifiers like kNN(k-Nearest Neighbor), Naïve Bayes, C4.5 and Support Vector Machines (SVMs). MATLAB & WEKA Soft wares are used for analysis Purpose. In this paper, coiffelet technique is utilized to analyze the synthetic PCG and the classifier parameters are compared with one another.

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

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.

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

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3650
Author(s):  
Heejin Kim ◽  
Ki Hong Kim ◽  
Ki Jeong Hong ◽  
Yunseo Ku ◽  
Sang Do Shin ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cardiopulmonary Resuscitation ◽  
Nearest Neighbor ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Swine Model ◽  
K Nearest Neighbor ◽  
Carotid Blood Flow ◽  
Time Frequency ◽  
Svm 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.

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

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%.

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

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.

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

2020 ◽  
Vol 142 (5) ◽  
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.

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

Sensors ◽  
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.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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