scholarly journals Epilepsy and Seizure Detection Using JLTM Based ICFFA and Multiclass SVM Classifier

2021 ◽  
Vol 38 (3) ◽  
pp. 883-893
Author(s):  
Vijaykumar Janga ◽  
Srinivasa Reddy Edara
Keyword(s):  
Seizure Detection ◽  
Svm Classifier ◽  
Multiclass Svm

scholarly journals A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images

2021 ◽  
Vol 18 (22) ◽  
pp. 12191
Author(s):  
Prabhjot Kaur ◽  
Shilpi Harnal ◽  
Rajeev Tiwari ◽  
Fahd S. Alharithi ◽  
Ahmed H. Almulihi ◽  
...  
Keyword(s):  
Health Care Systems ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Short Supply ◽  
X Ray ◽  
Proposed Model ◽  
Long Time ◽  
Care Systems ◽  
Chest X Ray ◽  
Multiclass Svm

COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country’s economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named “C19D-Net”, to detect “COVID-19” infection from “Chest X-Ray” (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model (“C19D-Net”) and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24% for 4-classes, 95.51% for three-classes, and 98.1% for two-classes. The proposed method has outperformed well in expressions of “precision”, “accuracy”, “F1-score” and “recall” in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed “C19D-Net” can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images.

scholarly journals Seizure Detection using SVM Classifier on EEG Signal

2014 ◽  
Vol 14 (14) ◽  
pp. 1658-1661 ◽  
Author(s):  
B. Suguna Nanthini ◽  
B. Santhi
Keyword(s):  
Seizure Detection ◽  
Svm Classifier ◽  
Eeg Signal

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.

scholarly journals Effect of noise on support vector machine based fault diagnosis of IM using vibration and current signatures

2018 ◽  
Vol 211 ◽  
pp. 03009
Author(s):  
Purushottam Gangsar ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Severity Levels ◽  
Electrical Faults ◽  
Using Data ◽  
Multiclass Svm ◽  
The One ◽  
Analysis Of Performance

This paper analyzes the effect of noise on support vector machine (SVM) based fault diagnosis of IM (IM). For this, a number of mechanical (bearing fault, unbalanced rotor, bowed rotor and misaligned rotor) and electrical faults (broken rotor bar, stator winding fault with two severity levels and phase unbalance with two severity levels) of IM are considered here. The vibration and current signals are used here for the diagnosis. Different experiments were performed in order to generate these signals at various operating condition of IM (Speed and Load). Time domain feature are then extracted from the raw vibration and current signals obtained from the experiments. Then, the noise are added in the raw signals and the same features are extracted from this corrupted signals. The features from the original and corrupted signals are used to feed the classifier. The one-versus-one multiclass SVM are used here to perform multi-fault diagnosis. The comparative analysis of performance of the SVM classifier using data with and without noise is presented.

scholarly journals A Smart Integrated Brain-Computer Interaction for Epileptic Seizure Detection

2021 ◽  
Vol 2128 (1) ◽  
pp. 012010
Author(s):  
A. M. El-Khamisy ◽  
N. M. Abd El-Raoof ◽  
S. M. Youssef
Keyword(s):  
Epileptic Seizure ◽  
Seizure Detection ◽  
True Positive Rate ◽  
Permutation Entropy ◽  
Svm Classifier ◽  
Epileptic Seizure Detection ◽  
Domain Mapping ◽  
Patient Will ◽  
Positive Rate ◽  
Extraction Pattern

Abstract Epilepsy is brain resulted activities which are affected by suddenly seizures which have unpredictable changes affects brain electrical functionalities. Epilepsy has a significant impact on society on the healthcare treatment, cost, responds, and patients behavior. The study has main objectives to propose accurate integrated framework for epileptic seizure detection from the pre-ictal phase of the EEG signal. Locate the connected channel lobe in region where epileptic is expected to occur. Provide automated and real-time monitoring and send warning messages to patient and epileptologist to take accurate actions before ictal occur. Enable future contribution for different Seizure features and impact. Also reduce cost, time and effort. Based on the hypothesis of entropy of EEG signals during seizure has low value if (n) of channels are detected to have seizure, then they are considered as connected neighbors in brain domain mapping, which is clear alert that patient will have a seizure ictal. This end to end framework has modules of data acquisition, pre-processing, feature extraction, pattern-matching, supports vector machines (SVM) classifier for extracted feature, in addition to monitoring and notification. The extracted features includes lower threshold, homogeneity, weighted permutation entropy, power and energy. Also identify the physiological field located inside the brain which the seizure will expected to occur. The final output results have 92% for True positive rate in addition to 95% of F1 and 98.9% of accuracy. This system has proved consistency during all its phases of seizure detection with valuable and effective support to the society.

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