Neural network based seizure detection system using statistical package analysis

Rajendran, Priyanka; Ganapathy, Kirupa

doi:10.11591/eei.v11i5.3771

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…With TUH data, FFT and SVM classifiers achieved 96% accuracy. In [14], the authors used ANN classifiers and obtained the same performance when they analyzed the outcomes using the SPSS tools. Because entropy measures provide unique features that are intrinsic and physiologically meaningful, another study [15] used fuzzy and distributional entropy.…”

Section: Traditional Approachesmentioning

confidence: 99%

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

Alshayeji

2023

Mach. Learn.: Sci. Technol.

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Epilepsy is one of the most debilitating neurological diseases that abruptly alters a person's way of life. Manual diagnosis is a laborious and time-consuming task prone to human error; therefore, automating this task by developing an intelligent system is necessary. Existing deep learning (DL) models require high training time, large datasets, and machines with more memory and processing power. In addition, owing to the black-box nature of DL models, no one can determine the features that the network prefers for classification decisions. To overcome these challenges, this paper proposes an accurate, automatic, and fast intelligent system for epilepsy detection using a computer-aided diagnosis (CAD)-2D machine learning (ML) framework. Existing ML models struggle to produce reliable and acceptable diagnostic results owing to the low amplitude and nonstationary nature of electroencephalograms (EEG), particularly in clinical situations where environmental influences are almost impossible to eliminate. The proposed model was built on the CHB-MIT dataset, and it represents the first study that employs the speeded-up robust feature (SURF) bag of features (BOF) technique for this application, which generates local features from the spectrogram images of the respective 1D EEG signal inputs. In addition, DL features were extracted from the spectrogram images for model performance comparisons. Both features were used separately to train the ML classifiers. Implementing SURF offers fast computation and makes the model invariant to distortions, noise, scaling, and so on. Therefore, the proposed model is more suitable for real-time applications, proposed ML framework provides an enhanced accuracy of 99.78% from the SVM-RBF classifier, along with 99.56% sensitivity, 100% specificity, and an error rate of 0.22%. The higher detection accuracy demonstrated the effectiveness of the proposed framework for medical disease diagnosis applications.

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Section: Traditional Approachesmentioning

confidence: 99%

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

Alshayeji

2023

Mach. Learn.: Sci. Technol.

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“…In the first method, features were extracted and then classified into binary classes labeled as Epileptic and Non-Epileptic seizures. In the second method, the performance of the feature extraction method was improved by using the Principal Component Analysis (PCA) method, and a 96% accuracy was achieved [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method

et al. 2023

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The Human Activity Recognition (HAR) system is the hottest research area in clinical research. The HAR plays a vital role in learning about a patient’s abnormal activities; based upon this information, the patient’s psychological state can be estimated. An epileptic seizure is a neurological disorder of the human brain and affects millions of people worldwide. If epilepsy is diagnosed correctly and in an early stage, then up to 70% of people can be seizure-free. There is a need for intelligent automatic HAR systems that help clinicians diagnose neurological disorders accurately. In this research, we proposed a Deep Learning (DL) model that enables the detection of epileptic seizures in an automated way, addressing a need in clinical research. To recognize epileptic seizures from brain activities, EEG is a raw but good source of information. In previous studies, many techniques used raw data from EEG to help recognize epileptic patient activities; however, the applied method of extracting features required much intensive expertise from clinical aspects such as radiology and clinical methods. The image data are also used to diagnose epileptic seizures, but applying Machine Learning (ML) methods could address the overfitting problem. In this research, we mainly focused on classifying epilepsy through physical epileptic activities instead of feature engineering and performed the detection of epileptic seizures in three steps. In the first step, we used the open-source numerical dataset of epilepsy of Bonn university from the UCI Machine Learning repository. In the second step, data were fed to the proposed ELM model for training in different training and testing ratios with a little bit of rescaling because the dataset was already pre-processed, normalized, and restructured. In the third step, epileptic and non-epileptic activity was recognized, and in this step, EEG signal feature extraction was automatically performed by a DL model named ELM; features were selected by a Feature Selection (FS) algorithm based on ELM and the final classification was performed using the ELM classifier. In our presented research, seven different ML algorithms were applied for the binary classification of epileptic activities, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), Stochastic Gradient Boosting Classifier (SGDC), Gradient Boosting Classifier (GB), Decision Trees (DT), and three deep learning models named Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). After deep analysis, it is observed that the best results were obtained by our proposed DL model, Extreme Learning Machine (ELM), with an accuracy of 100% accuracy and a 0.99 AUC. Such high performance has not attained in previous research. The proposed model’s performance was checked with other models in terms of performance parameters, namely confusion matrix, accuracy, precision, recall, F1-score, specificity, sensitivity, and the ROC curve.

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“…Nevertheless, both PCA and SPCA are linear methods and cannot be handled effectively with non-linear variables. With data processing technology, deep neural networks [7] can effectively solve the problem of insufficient feature extraction methods [8].…”

Section: Introductionmentioning

confidence: 99%

Multimode system condition monitoring using sparsity reconstruction for quality control

Bougheloum

Bekaik

Gherbi

2023

IJECE

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<p>In this paper, we introduce an improved multivariate statistical monitoring method based on the stacked sparse autoencoder (SSAE). Our contribution focuses on the choice of the SSAE model based on neural networks to solve diagnostic problems of complex systems. In order to monitor the process performance, the squared prediction error (SPE) chart is linked with nonparametric adaptive confidence bounds which arise from the kernel density estimation to minimize erroneous alerts. Then, faults are localized using two methods: contribution plots and sensor validity index (SVI). The results are obtained from experiments and real data from a drinkable water processing plant, demonstrating how the applied technique is performed. The simulation results of the SSAE model show a better ability to detect and identify sensor failures.</p>

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Neural network based seizure detection system using statistical package analysis

Cited by 7 publications

References 28 publications

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method

Multimode system condition monitoring using sparsity reconstruction for quality control

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