scholarly journals Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 74
Author(s):  
Shahab Abdulla ◽  
Mohammed Diykh ◽  
Sarmad K. D. Alkhafaji ◽  
Jonathan H. Greena ◽  
Hanan Al-Hadeethi ◽  
...  
Keyword(s):  
Covariance Matrix ◽  
Back Propagation ◽  
Epileptic Seizures ◽  
Back Propagation Neural Network ◽  
Machine Learning Techniques ◽  
Eeg Signals ◽  
Average Accuracy ◽  
Effective Selection ◽  
Kolmogorov Smirnov ◽  
Eeg Recordings

Experts usually inspect electroencephalogram (EEG) recordings page-by-page in order to identify epileptic seizures, which leads to heavy workloads and is time consuming. However, the efficient extraction and effective selection of informative EEG features is crucial in assisting clinicians to diagnose epilepsy accurately. In this paper, a determinant of covariance matrix (Cov–Det) model is suggested for reducing EEG dimensionality. First, EEG signals are segmented into intervals using a sliding window technique. Then, Cov–Det is applied to each interval. To construct a features vector, a set of statistical features are extracted from each interval. To eliminate redundant features, the Kolmogorov–Smirnov (KST) and Mann–Whitney U (MWUT) tests are integrated, the extracted features ranked based on KST and MWUT metrics, and arithmetic operators are adopted to construe the most pertinent classified features for each pair in the EEG signal group. The selected features are then fed into the proposed AdaBoost Back-Propagation neural network (AB_BP_NN) to effectively classify EEG signals into seizure and free seizure segments. Finally, the AB_BP_NN is compared with several classical machine learning techniques; the results demonstrate that the proposed mode of AB_BP_NN provides insignificant false positive rates, simpler design, and robustness in classifying epileptic signals. Two datasets, the Bern–Barcelona and Bonn datasets, are used for performance evaluation. The proposed technique achieved an average accuracy of 100% and 98.86%, respectively, for the Bern–Barcelona and Bonn datasets, which is considered a noteworthy improvement compared to the current state-of-the-art methods.

scholarly journals Statistical Law and Predictive Analysis of Compressive Strength of Cemented Sand and Gravel

2020 ◽  
Vol 27 (1) ◽  
pp. 291-298
Author(s):  
Shoukai Chen ◽  
Yongqiwen Fu ◽  
Lei Guo ◽  
Shifeng Yang ◽  
Yajing Bie
Keyword(s):  
Compressive Strength ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Predictive Analysis ◽  
Distribution Law ◽  
Data Set ◽  
Cemented Sand ◽  
Mix Proportion ◽  
Kolmogorov Smirnov ◽  
Sand And Gravel

AbstractA data set of cemented sand and gravel (CSG) mix proportion and 28-day compressive strength was established, with outliers determined and removed based on the Boxplot. Then, the distribution law of compressive strength of CSG was analyzed using the skewness kurtosis and single-sample Kolmogorov-Smirnov tests. And with the help of Python software, a model based on Back Propagation neural network was built to predict the compressive strength of CSG according to its mix proportion. The results showed that the compressive strength follows the normal distribution law, the expected value and variance were 5.471 MPa and 3.962 MPa respectively, and the average relative error was 7.16%, indicating the predictability of compressive strength of CSG and its correlation with the mix proportion.

scholarly journals Computational method for predicting Self-Interactions Protein using Recurrent Neural Network from Protein Evolutionary Information

2020 ◽  
Author(s):  
Ji-Yong An
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Biological Activities ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Computational Method ◽  
Evolutionary Information ◽  
Support Vector ◽  
Scale Invariant ◽  
Average Accuracy

Abstract Self-interactions Protein (SIPs) play crucial roles in biological activities of organisms. Many high-throughput methods can be used to identify SIPs. However, these methods are both time-consuming and expensive. How to develop effective computational approaches for identifying SIPs is a challenging task. In the paper, we presented a novelty computational method called RRN-SIFT, which combines the Recurrent Neural Network (RNN) with Scale Invariant Feature Transform (SIFT) to predict SIPs based on protein evolutionary information. The main advantage of the proposed RNN-SIFT model is that it used SIFT for extracting key feature by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM) and employed RNN classifier to carry out classification based on extracted features. Extensive experiments show that the RRN-SIFT obtained average accuracy of 94.34% and 97.12% on yeast and human dataset. We also compared our performance with the Back Propagation Neural Network (BPNN), the state-of-the-art support vector machine (SVM) and other exiting methods. By comparing with experimental results, the performance of RNN-SIFT is significantly better than those of the BPNN, SVM and other previous methods in the domain. Therefore, we can come to the conclusion that the proposed RNN-SIFT model is useful tools and can execute incredibly well for predicting SIPs, as well as other bioinformatics tasks. In order to facilitate widely studies and encourage future proteomics research, a freely available web server called RNN-SIFT-SIPs was developed, and is available at http://219.219.62.123:8888/RNNSIFT/ and includes source code and SIPs datasets.

Performance Evaluation of Machine Learning Techniques for Customer Churn Prediction in Telecommunication Sector

2021 ◽  
pp. 262-274
Author(s):  
Babita Majhi ◽  
Sachin Singh Rajput ◽  
Ritanjali Majhi
Keyword(s):  
Information Gain ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Learning Techniques ◽  
Learning Machine ◽  
Nearest Neighborhood

The principle objective of this chapter is to build up a churn prediction model which helps telecom administrators to foresee clients who are no doubt liable to agitate. Many studies affirmed that AI innovation is profoundly effective to anticipate this circumstance as it is applied through training from past information. The prediction procedure is involved three primary stages: normalization of the data, then feature selection based on information gain, and finally, classification utilizing different AI methods, for example, back propagation neural network (BPNNM), naïve Bayesian, k-nearest neighborhood (KNN), support vector machine (SVM), discriminant analysis (DA), decision tree (DT), and extreme learning machine (ELM). It is shown from simulation study that out of these seven methods SVM with polynomial based kernel is coming about 91.33% of precision where ELM is at the primary situation with 92.10% of exactness and MLANN-based CCP model is at third rank with 90.4% of accuracy. Similar observation is noted for 10-fold cross validation also.

Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning

2021 ◽  
pp. 2150026
Author(s):  
Mehmet Akif Ozdemir ◽  
Ozlem Karabiber Cura ◽  
Aydin Akan
Keyword(s):  
Epileptic Seizures ◽  
Eeg Signals ◽  
Clinical Tool ◽  
Energy Distributions ◽  
Time Frequency ◽  
Synchrosqueezing Transform ◽  
Novel Method ◽  
Eeg Recordings ◽  
Accuracy Rates ◽  
Aided Diagnosis

Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fourier-based Synchrosqueezing Transform (SST), which is a high-resolution time-frequency (TF) representation, and Convolutional Neural Network (CNN) is proposed to detect and predict seizure segments. SST is based on the reassignment of signal components in the TF plane which provides highly localized TF energy distributions. Epileptic seizures cause sudden energy discharges which are well represented in the TF plane by using the SST method. The proposed SST-based CNN method is evaluated using the IKCU dataset we collected, and the publicly available CHB-MIT dataset. Experimental results demonstrate that the proposed approach yields high average segment-based seizure detection precision and accuracy rates for both datasets (IKCU: 98.99% PRE and 99.06% ACC; CHB-MIT: 99.81% PRE and 99.63% ACC). Additionally, SST-based CNN approach provides significantly higher segment-based seizure prediction performance with 98.54% PRE and 97.92% ACC than similar approaches presented in the literature using the CHB-MIT dataset.

Feature Reduction Using Genetic Algorithm for Cognitive Man-Machine Communication

2015 ◽  
Vol 7 (4) ◽  
pp. 1-17
Author(s):  
Naveen Irtiza ◽  
Humera Farooq
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Feature Space ◽  
Feature Reduction ◽  
Machine Learning Techniques ◽  
Brain State ◽  
Cognitive State ◽  
Eeg Signals ◽  
Eeg Recordings ◽  
Machine Communication

Electroencephalographic (EEG) signals are usually comprised of high-dimensional feature space. This work aims to assess the effect of reducing the number of features extracted from EEG recordings. A methodology is proposed that combines brain imaging and machine learning techniques to predict the cognitive state of the subjects whether they are feeling themselves in a safe or dangerous environment. The changes in the brain state are correlated with power modulations of oscillatory rhythms in recorded EEG signals called ERD / ERS (Event-related De-synchronization / Synchronization). In order to determine the optimized number of features, Genetic Algorithm (GA) will be used. GA has played instrumental role in solving optimization problems from diverse fields. In various studies and researches for Cognitive Man-Machine Communication, the algorithm has been used as an effective method to extract an optimal set of features.

scholarly journals Exploring Douglas-Peucker Algorithm in the Detection of Epileptic Seizure from Multicategory EEG Signals

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Roozbeh Zarei ◽  
Jing He ◽  
Siuly Siuly ◽  
Guangyan Huang ◽  
Yanchun Zhang
Keyword(s):  
Principal Component ◽  
Epileptic Seizures ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Eeg Signals ◽  
Decision Tree Classifier ◽  
Efficient Detection ◽  
Eeg Data ◽  
Tree Classifier ◽  
Representative Samples

Discovering the concealed patterns of Electroencephalogram (EEG) signals is a crucial part in efficient detection of epileptic seizures. This study develops a new scheme based on Douglas-Peucker algorithm (DP) and principal component analysis (PCA) for extraction of representative and discriminatory information from epileptic EEG data. As the multichannel EEG signals are highly correlated and are in large volumes, the DP algorithm is applied to extract the most representative samples from EEG data. The PCA is utilised to produce uncorrelated variables and to reduce the dimensionality of the DP samples for better recognition. To verify the robustness of the proposed method, four machine learning techniques, random forest classifier (RF), k-nearest neighbour algorithm (k-NN), support vector machine (SVM), and decision tree classifier (DT), are employed on the obtained features. Furthermore, we assess the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that the DP technique effectively extracts the representative samples from EEG signals compressing up to over 47% sample points of EEG signals. The results also indicate that the proposed feature method with the RF classifier achieves the best performance and yields 99.85% of the overall classification accuracy (OCA). The proposed method outperforms the most recently reported methods in terms of OCA in the same epileptic EEG database.

scholarly journals Recognition Method of Limb Motor Imagery EEG Signals Based on Integrated Back-propagation Neural Network

2015 ◽  
Vol 9 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Mingyang Li ◽  
Wanzhong Chen ◽  
Bingyi Cui ◽  
Yantao Tian
Keyword(s):  
Neural Network ◽  
Motor Imagery ◽  
Hand Movement ◽  
Recognition Rate ◽  
Back Propagation ◽  
Operation Time ◽  
Back Propagation Neural Network ◽  
Classification Problems ◽  
Eeg Signals ◽  
Elman Neural Network

In this paper, in order to solve the existing problems of the low recognition rate and poor real-time performance in limb motor imagery, the integrated back-propagation neural network (IBPNN) was applied to the pattern recognition research of motor imagery EEG signals (imagining left-hand movement, imagining right-hand movement and imagining no movement). According to the motor imagery EEG data categories to be recognized, the IBPNN was designed to consist of 3 single three-layer back-propagation neural networks (BPNN), and every single neural network was dedicated to recognizing one kind of motor imagery. It simplified the complicated classification problems into three mutually independent two-class classifications by the IBPNN. The parallel computing characteristic of IBPNN not only improved the generation ability for network, but also shortened the operation time. The experimental results showed that, while comparing the single BPNN and Elman neural network, IBPNN was more competent in recognizing limb motor imagery EEG signals. Also among these three networks, IBPNN had the least number of iterations, the shortest operation time and the best consistency of actual output and expected output, and had lifted the success recognition rate above 97 percent while other single network is around 93 percent.

DECOMPOSITION OF SEIZURES IN EEG SIGNALS USING DISCRETE WAVELET TRANSFORMS (DWT)

2021 ◽  
pp. 50-52
Author(s):  
N Shweta ◽  
Nagendra H
Keyword(s):  
Neurological Disorders ◽  
Wavelet Transforms ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Eeg Signals ◽  
Seizure Duration ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
The Brain

An electroencephalogram (EEG) is a test that records electrical activity in the brain. Epileptic seizures affect approximately 50 million people worldwide, making it one of the most serious neurological disorders. Seizures cause a loss of consciousness, but there are no specic signs associated with epileptic seizures. analysing the brain's activity during seizures and locating the seizure duration in EEG recordings is difcult and time consuming. A discrete wavelet transform (DWT), which is an effective tool for decomposing EEG signals into delta, theta, alpha, beta, and gamma ( and ) frequency bands. For research, the db4 is used, which has a morphological d,q,a,b g structure that is different to that of EEG.

Application of Back Propagation Neural Network and Information Entropy in Deep Detection of Anesthesia

2020 ◽  
Vol 10 (8) ◽  
pp. 1875-1879
Author(s):  
Yujuan Zhou ◽  
Lei Wang ◽  
Jintai Jia ◽  
Gema Monasterio
Keyword(s):  
Neural Network ◽  
General Anesthesia ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Sample Entropy ◽  
Depth Of Anesthesia ◽  
Eeg Signals ◽  
Entropy Index ◽  
Wavelet Entropy ◽  
Anesthesia Depth

In order to study the monitoring of anesthesia depth during general anesthesia, the EEG (electroencephalogram) signals of 30 patients with laparoscopic general anesthesia were taken as the research objects. The approximate entropy, sample entropy, ranking entropy, and wavelet entropy of EEG signals under different anesthesia conditions were compared by BP (Back Propagation) neural network. The results showed that with the deepening of anesthesia, the four kinds of information entropies of EEG signal showed a downward trend. Among them, the sample entropy algorithm, ranking entropy algorithm, and wavelet entropy algorithm had a higher accuracy in the classification of anesthesia depth. Whereas, the network model established by combining sample entropy index and wavelet entropy index had the highest accuracy in judging anesthesia depth, which was 99.98%. To sum up, the method presented to monitor the depth of anesthesia by combining the characteristics of various EEG signals provides a new reference for the monitoring of the depth of anesthesia.

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