scholarly journals An Improved Optimization Algorithm for Epileptic Seizure Detection in EEG Signals Using Random Forest Classifier

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 327-340
Author(s):  
A. Phraeson Gini ◽  
Dr.M.P. Flower Queen
Keyword(s):  
Random Forest ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
True Positive Rate ◽  
Intrinsic Mode Functions ◽  
Selection Algorithm ◽  
Eeg Signals ◽  
Efficient Detection ◽  
Positive Rate ◽  
Electroencephalogram Eeg

Epilepsy is a psychiatric condition that has serious consequences for the human brain. The Electroencephalogram (EEG) may reveal a pattern that tells physicians whether an epileptic seizure is likely to occur again. EEG testing may also help the physician exclude other conditions that mimic epilepsy as a reason for the seizure. Now-a-days the researchers are showing much interest in these seizure detection because of its significance in epileptic detection. This paper is addressing an efficient soft computing framework for seizure detection from the EEG signal. The proposed pipeline of work is having the state-of-art as the possibility of achieving the maximum accuracy. The spectral features extracted from the Intrinsic mode functions (IMF) of EEG samples and it is directing the proposed flow towards the efficient detection of seizure and also the random forest algorithm based a convulsion classification is reliable for because of its learning behavior from the huge number of known dataset. The feature selection algorithm in this proposed work is stimulating the overall work towards the maximum true positive rate. This work is implemented on MATLAB platform and dataset were downloaded from the universal database such as Bonn university database. The results obtained from the proposed approach is showing the truthfulness of the approach introduced here.

Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods

2020 ◽  
Vol 65 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Chahira Mahjoub ◽  
Régine Le Bouquin Jeannès ◽  
Tarek Lajnef ◽  
Abdennaceur Kachouri
Keyword(s):  
Epileptic Seizure ◽  
High Performance ◽  
Visual Analysis ◽  
Binary Classification ◽  
Seizure Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Epileptic Seizure Detection

AbstractElectroencephalography (EEG) is a common tool used for the detection of epileptic seizures. However, the visual analysis of long-term EEG recordings is characterized by its subjectivity, time-consuming procedure and its erroneous detection. Various epileptic seizure detection algorithms have been proposed to deal with such issues. In this study, a novel automatic seizure-detection approach is proposed. Three different strategies are suggested to the user whereby he/she could choose the appropriate one for a given classification problem. Indeed, the feature extraction step, including both linear and nonlinear measures, is performed either directly from the EEG signals, or from the derived sub-bands of tunable-Q wavelet transform (TQWT), or even from the intrinsic mode functions (IMFs) of multivariate empirical mode decomposition (MEMD). The classification procedure is executed using a support vector machine (SVM). The performance of the proposed method is evaluated through a publicly available database from which six binary classification cases are formulated to discriminate between healthy, seizure and non-seizure EEG signals. Our results show high performance in terms of accuracy (ACC), sensitivity (SEN) and specificity (SPE) compared to the state-of-the-art approaches. Thus, the proposed approach for automatic seizure detection can be considered as a valuable alternative to existing methods, able to alleviate the overload of visual analysis and accelerate the seizure detection.

scholarly journals EEG Classification Using TQWT and Classifiers

2020 ◽  
Vol 5 (8) ◽  
pp. 603-611
Author(s):  
Sibasankar Padhy ◽  
S Sai Suryateja
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Epileptic Seizures ◽  
Random Forest Classifier ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Maximum Accuracy ◽  
Specificity And Sensitivity ◽  
Electroencephalogram Eeg ◽  
Logistic Regression Classifier

The purpose of this study is to detect the epileptic seizures, which can be indicated by the abnormal disturbances in intracranial neurons using the electroencephalogram (EEG) signals. The EEG signals are grouped into three categories viz., Normal EEG signals (Z and O subsets), Seizure-free EEG signals (N and F subsets), and Seizure EEG signals (S subset). Whereas, for classification in this study, EEG signals are divided into three groups namely NF-S, O-FS, and ZO-NF-S. The signal length is fixed to be 4096 samples. The EEG signals will be decomposed by using Tunable-Q Wavelet Transform (TQWT), which produces intrinsic mode functions (IMFs) in decreasing order of frequency. These IMFs are analysed to gather the features of these signals, which help to classify them into various categories, and these features are fed as inputs to three classifiers viz., Random Forest (RF), Decision Table (DT), and Logistic Regression (LR). Logistic Regression classifier has showed higher accuracy, specificity and sensitivity for NF-S and O-F-S groups in comparison to RF and DT classifiers, whereas, Random Forest classifier expressed higher accuracy, specificity and sensitivity for ZO-NF-S groups in comparison to other classifiers. By utilising LR classifier, the suitable parameters of TQWT in NF-S (seizure-free vs. Seizure) are Q=6, r=3, and J=9 and showed maximum accuracy of 98%; and in O-F-S (Normal vs. Seizure-free vs. Seizure), Q=1, r=3, and J=9 attained maximum accuracy of 94.7%. Whereas, in ZONF-S (Normal vs. Seizure-free vs. Seizure), Q=4, r=3, and J=9 expressed maximum accuracy of 99.8% utilising Random Forest classifier.

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.

scholarly journals Alternative Form of Ordinary Differential Equation of Electroencephalography Signals During an Epileptic Seizure

2021 ◽  
Vol 17 (2) ◽  
pp. 109-113
Author(s):  
Ameen Omar Barja
Keyword(s):  
Differential Equation ◽  
Ordinary Differential Equation ◽  
Epileptic Seizure ◽  
Essential Role ◽  
Clinical Neurophysiology ◽  
Alternative Form ◽  
Eeg Signals ◽  
Seizure Disorders ◽  
Electroencephalogram Eeg ◽  
The Brain

One of the most important fields in clinical neurophysiology is an electroencephalogram (EEG). It is a test used to detect problems related to the brain electrical activity, and it can track and records patterns of brain waves. EEG continues to play an essential role in diagnosis and management of patients with epileptic seizure disorders. Nevertheless, the outcome of EEG as a tool for evaluating epileptic seizure is often interpreted as a noise rather than an ordered pattern. The mathematical modelling of EEG signals provides valuable data to neurologists, and is heavily utilized in the diagnosis and treatment of epilepsy. EEG signals during the seizure can be modeled as ordinary differential equation (ODE). In this study we will present an alternative form of ODE of EEG signals through the seizure.

Enhancing the Performance of a Model to Predict Driving Distraction with the Random Forest Classifier

2021 ◽  
pp. 036119812110186
Author(s):  
Samira Ahangari ◽  
Mansoureh Jeihani ◽  
Anam Ardeshiri ◽  
Md Mahmudur Rahman ◽  
Abdollah Dehzangi
Keyword(s):  
Random Forest ◽  
Driving Simulator ◽  
Situational Awareness ◽  
True Positive Rate ◽  
The United States ◽  
Driving Performance ◽  
Lane Changing ◽  
Wide Range ◽  
Positive Rate ◽  
Better Than

Distracted driving is known to be one of the main causes of crashes in the United States, accounting for about 40% of all crashes. Drivers’ situational awareness, decision-making, and driving performance are impaired as a result of temporarily diverting their attention from the primary task of driving to other unrelated tasks. Detecting driver distraction would help in adapting the most effective countermeasures. To tackle this problem, we employed a random forest (RF) classifier, one of the best classifiers that has attained promising results for a wide range of problems. Here, we trained RF using the data collected from a driving simulator, in which 92 participants drove under six different distraction scenarios of handheld calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). Various driving performance measures such as speed, acceleration, throttle, lane changing, brake, collision, and offset from the lane center were investigated. Using the RF method, we achieved 76.5% prediction accuracy on the independent test set, which is over 8.2% better than results reported in previous studies. We also obtained a 76.6% true positive rate, which is 14% better than those reported in previous studies. Such results demonstrate the preference of RF over other machine learning methods to identify driving distractions.

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