scholarly journals Epileptic EEG signal classifications based on DT-CWT and SVM classifier

2021 ◽  
Author(s):  
Deivasigamani S ◽  
◽  
Senthilpari C ◽  
Wong Hin Yong ◽  
Rajesh P.K. ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
The Other ◽  
Support Vector ◽  
Svm Classifier ◽  
Medical Procedure ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Sensitivity Specificity ◽  
The Mind

Contamination in human cerebrum causes the mind issue which is as Epilepsy. The contaminated territory in the cerebrum area creates the unpredictable example signals as focal signs and the other sound locales in the mind produce the standard example signals as non-focal sign. Henceforth, the discovery of focal signs from the non-focal signs is a significant for epileptic medical procedure in epilepsy patients. This paper proposes a straightforward and proficient technique for EEG (Electroencephalogram) signals orders utilizing SVM (Support Vector Machine) classifier. The exhibition of the proposed EEG signals characterization framework is assessed as far as Sensitivity, Specificity, and Accuracy.

EEG Signals Classification Using Support Vector Machine

2020 ◽  
Vol 12 (2) ◽  
pp. 215-224
Author(s):  
Abdelhakim Ridouh ◽  
Daoud Boutana ◽  
Salah Bourennane
Keyword(s):  
Support Vector Machine ◽  
Wavelet Decomposition ◽  
Moving Average ◽  
Real Life ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Decomposition Level ◽  
Sensitivity Specificity

We address with this paper some real-life healthy and epileptic EEG signals classification. Our proposed method is based on the use of the discrete wavelet transform (DWT) and Support Vector Machine (SVM). For each EEG signal, five wavelet decomposition level is applied which allow obtaining five spectral sub-bands correspond to five rhythms (Delta, Theta, Alpha, Beta and gamma). After the extraction of some features on each sub-band (energy, standard deviation, and entropy) a moving average (MA) is applied to the resulting features vectors and then used as inputs to SVM to train and test. We test the method on EEG signals during two datasets: normal and epileptics, without and with using MA to compare results. Three parameters are evaluated such as sensitivity, specificity, and accuracy to test the performances of the used methods.

scholarly journals Investigation of Biomechanical Characteristics of Orthopedic Implants for Tibial Plateau Fractures by Means of Deep Learning and Support Vector Machine Classification

2020 ◽  
Vol 10 (14) ◽  
pp. 4697 ◽  
Author(s):  
Bogdan Niculescu ◽  
Cosmin Ioan Faur ◽  
Tiberiu Tataru ◽  
Bogdan Marian Diaconu ◽  
Mihai Cruceru
Keyword(s):  
Support Vector Machine ◽  
Tibial Plateau ◽  
Orthopedic Implants ◽  
Tibial Plateau Fractures ◽  
Applied Force ◽  
The Other ◽  
Support Vector ◽  
Svm Classifier ◽  
Significant Difference ◽  
Applied Forces

An experimental comparative study of the biomechanical behavior of commonly used orthopedic implants for tibial plateau fractures was carried out. An artificial bone model Synbone1110 was used and a Schatzker V type tibial plateau fracture was created in vitro, then stabilized with three different implant types, classic L plate, Locking Plate System (PLS), and Hybrid External Fixator (HEF). The stiffness of the bone—implant assembly was assessed by means of mechanical testing using an automated testing machine. It was found that the classic L plate type internal implant has a significantly higher value of deformation then the other two implant types. In case of the other implant types, PLS had a better performance than HEF at low and medium values of the applied force. At high values of the applied forces, the difference between deformation values of the two types became gradually smaller. An Artificial Neural Network model was developed to predict the implant deformation as a function of the applied force and implant device type. To establish if a clear-cut distinction exists between mechanical performance of PLS and HEF, a Support Vector Machine classifier was employed. At high values of the applied force, the Support Vector Machine (SVM) classifier predicts that no statistically significant difference exists between the performance of PLS and HEF.

MULTI-CLUSTER SUPPORT VECTOR MACHINE CLASSIFIER FOR THE CLASSIFICATION OF SUSPICIOUS AREAS IN DIGITAL MAMMOGRAMS

2011 ◽  
Vol 10 (04) ◽  
pp. 481-494 ◽  
Author(s):  
PETER MC LEOD ◽  
BRIJESH VERMA
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Anova Analysis ◽  
Novel Technique ◽  
Digital Database ◽  
Benchmark Database

This paper presents a novel technique for the classification of suspicious areas in digital mammograms. The proposed technique is based on clustering of input data into numerous clusters and amalgamating them with a Support Vector Machine (SVM) classifier. The technique is called multi-cluster support vector machine (MCSVM) and is designed to provide a fast converging technique with good generalization abilities leading to an improved classification as a benign or malignant class. The proposed MCSVM technique has been evaluated on data from the Digital Database of Screening Mammography (DDSM) benchmark database. The experimental results showed that the proposed MCSVM classifier achieves better results than standard SVM. A paired t-test and Anova analysis showed that the results are statistically significant.

Support Vector Machine Classifier with WHM Offset for Unbalanced Data

2008 ◽  
Vol 12 (1) ◽  
pp. 94-101 ◽  
Author(s):  
Boyang Li ◽  
◽  
Jinglu Hu ◽  
Kotaro Hirasawa
Keyword(s):  
Support Vector Machine ◽  
Real World ◽  
Support Vector Machine Classifier ◽  
Classification Problem ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Real World Data ◽  
Unbalanced Classification ◽  
Improved Support Vector Machine

We propose an improved support vector machine (SVM) classifier by introducing a new offset, for solving the real-world unbalanced classification problem. The new offset is calculated based on the unbalanced support vectors resulting from the unbalanced training data. We developed a weighted harmonic mean (WHM) algorithm to further reduce the effects of noise on offset calculation. We apply the proposed approach to classify real-world data. Results of simulation demonstrate the effectiveness of our proposed approach.

scholarly journals Modified Support Vector Machine for Detecting Stress Level Using EEG Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Richa Gupta ◽  
M. Afshar Alam ◽  
Parul Agarwal
Keyword(s):  
Stress Level ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Mental Strain ◽  
Feature Extraction And Selection ◽  
Whale Optimization ◽  
Optimal Kernel ◽  
Sensitivity Specificity ◽  
Better Than

Stress is categorized as a condition of mental strain or pressure approaches because of upsetting or requesting conditions. There are various sources of stress initiation. Researchers consider human cerebrum as the primary wellspring of stress. To study how each individual encounters stress in different forms, researchers conduct surveys and monitor it. The paper presents the fusion of 5 algorithms to enhance the accuracy for detection of mental stress using EEG signals. The Whale Optimization Algorithm has been modified to select the optimal kernel in the SVM classifier for stress detection. An integrated set of algorithms (NLM, DCT, and MBPSO) has been used for preprocessing, feature extraction, and selection. The proposed algorithm has been tested on EEG signals collected from 14 subjects to identify the stress level. The proposed approach was validated using accuracy, sensitivity, specificity, and F1 score with values of 96.36%, 96.84%, 90.8%, and 97.96% and was found to be better than the existing ones. The algorithm may be useful to psychiatrists and health consultants for diagnosing the stress level.

scholarly journals PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Yukai Yao ◽  
Hongmei Cui ◽  
Yang Liu ◽  
Longjie Li ◽  
Long Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Roc Curve ◽  
Support Vector Machine Classifier ◽  
Data Preprocessing ◽  
Parameters Optimization ◽  
Experimental Results ◽  
Support Vector ◽  
Grid Search ◽  
Search Methods ◽  
Sensitivity Specificity

We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.

scholarly journals Using Support Vector Machine Detection of Breast Cancer in Early stage

2020 ◽  
pp. 213-216
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Sensitivity Specificity

The Breast Cancer is disease which tremendously increased in women’s nowadays. Mammography is technique of low-powered X-ray diagnosis approach for detection and diagnosis of cancer diseases at early stage. The proposed system shows the solution of two problems. First shows to detect tumors as suspicious regions with a weak contrast to their background and second shows way to extract features which categorize tumors. Hence this classification can be done with SVM, a great method of statistical learning has made significant achievement in various field. Discovered in the early 90’s, which led to an interest in machine learning? Here the different types of tumor like Benign, Malignant, or Normal image are classified using the SVM classifier. This techniques shows how easily we can detect region of tumor is present in mammogram images with more than 80% of accuracy rates for linear classification using SVM. The 10-fold cross validation to get an accurate outcome is been used by proposed system. The Wisconsin breast cancer diagnosis data set is referred from UCI machine learning repository. The considering accuracy, sensitivity, specificity, false discovery rate, false omission rate and Matthews’s correlation coefficient is appraised in the proposed system. This Provides good result for both training and testing phase. The techniques also shows accuracy of 98.57% and 97.14% by use of Support Vector Machine and K-Nearest Neighbors

scholarly journals Wavelet based Extraction of Features from EEG Signals and Classification of Novel Emotion Recognition Using SVM and HMM Classifier and to Measure its Accuracy

2021 ◽  
Vol 36 (1) ◽  
pp. 727-732
Author(s):  
M. Mohanambal ◽  
Dr.P. Vishnu Vardhan
Keyword(s):  
Support Vector Machine ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Mean Value ◽  
Support Vector ◽  
Svm Classifier ◽  
Emotion Detection ◽  
Eeg Signals

Aim: The study aims to extract features from EEG signals and classify emotion using Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifier. Materials and methods: The study was conducted using the Support Vector Machine (SVM) and Hidden Markov Model (HMM) programs to analyze and compare the recognition of emotions classified under EEG signals. The results were computed using the MATLAB algorithm. For each group, ten samples were used to compare the efficiency of SVM and HMM classifiers. Result: The study’s performance exhibits the HMM classifier’s accuracy over the SVM classifier and the emotion detection from EEG signals computed. The mean value of the HMM classifier is 52.2, and the SVM classifier is 22.4. The accuracy rate of 35% with the data features is found in HMM classifier. Conclusion: This study shows a higher accuracy level of 35% for the HMM classifier when compared with the SVM classifier. In the detection of emotions using the EEG signal. This result shows that the HMM classifier has a higher significant value of P=.001 < P=.005 than the SVM classifier.

Linear and non-linear feature extraction from rat electrocorticograms for seizure detection by support vector machine

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Haitham S. Mohammed ◽  
Hagar M. Hassan ◽  
Michael H. Zakhari ◽  
Hassan Mostafa ◽  
Ebtesam A. Mohamad
Keyword(s):  
Support Vector Machine ◽  
Seizure Detection ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Features ◽  
Linear Feature ◽  
Non Linear ◽  
Before And After ◽  
Sensitivity Specificity

Abstract Seizures, the main symptom of epilepsy, are provoked due to a neurological disorder that underlies the disease. The accurate detection of seizures is a crucial step in any procedure of treatment. In the present study, electrocorticogram (ECoG) signals were recorded from awake and freely moving animals implanted with cortical electrodes before and after pentylenetetrazol, the chemo-convulsant injection. ECoG signals were segmented into 4-s epochs and labeled. Twenty-four linear and non-linear features were extracted from the time and frequency domains of the ECoG signals. The extracted features either individually or in combinations were fed to an automatic support vector machine (SVM) classification system. SVM classifier was trained with 5 min of ictal and non-ictal labeled ECoG signals to build the hyperplane that separates two sets of training signals. Sensitivity, specificity, and accuracy were determined for the testing dataset using the different feature combinations. It has been found that some linear features either individually or in combinations outperform non-linear features in terms of the accuracy for seizure detection. The maximum accuracy achieved by the system was 95.3% and has been obtained only after linear and non-linear features were combined. ECoG signals were classified without pre-processing or removal of artifacts to reduce the required computational time to be suitable for online implementation purposes. This may prove the detection system’s robustness and supports its use in online seizure detection protocols.

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