Patient-Specific Epilepsy Seizure Detection Using Random Forest Classification over One-Dimension Transformed EEG Data

Pinto-Orellana, Marco Antonio; Cerqueira, Fábio Ribeiro

doi:10.1007/978-3-319-53480-0_51

Cited by 7 publications

(6 citation statements)

References 29 publications

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“…For example, Zabihi et al [ 26 ] performed a subject-specific study with an average of 93.7% sensitivity and a specificity of 99.05% in four subjects. Another study [ 27 ] proposed a patient-dependent system with 97.12% specificity and 99.29% sensitivity. Alternatively, an individual classifier can be built for each channel and seizure pattern [ 28 ], eventually reaching an average accuracy of 95.12%.…”

Section: Discussionmentioning

confidence: 99%

Automated Seizure Detection Based on State-Space Model Identification

Wang,

Sperling,

Wyeth

et al. 2024

Sensors

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In this study, we developed a machine learning model for automated seizure detection using system identification techniques on EEG recordings. System identification builds mathematical models from a time series signal and uses a small number of parameters to represent the entirety of time domain signal epochs. Such parameters were used as features for the classifiers in our study. We analyzed 69 seizure and 55 non-seizure recordings and an additional 10 continuous recordings from Thomas Jefferson University Hospital, alongside a larger dataset from the CHB-MIT database. By dividing EEGs into epochs (1 s, 2 s, 5 s, and 10 s) and employing fifth-order state-space dynamic systems for feature extraction, we tested various classifiers, with the decision tree and 1 s epochs achieving the highest performance: 96.0% accuracy, 92.7% sensitivity, and 97.6% specificity based on the Jefferson dataset. Moreover, as the epoch length increased, the accuracy dropped to 94.9%, with a decrease in sensitivity to 91.5% and specificity to 96.7%. Accuracy for the CHB-MIT dataset was 94.1%, with 87.6% sensitivity and 97.5% specificity. The subject-specific cases showed improved results, with an average of 98.3% accuracy, 97.4% sensitivity, and 98.4% specificity. The average false detection rate per hour was 0.5 ± 0.28 in the 10 continuous EEG recordings. This study suggests that using a system identification technique, specifically, state-space modeling, combined with machine learning classifiers, such as decision trees, is an effective and efficient approach to automated seizure detection.

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Section: Discussionmentioning

confidence: 99%

Automated Seizure Detection Based on State-Space Model Identification

Wang,

Sperling,

Wyeth

et al. 2024

Sensors

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“…In this task, EEG signals were recorded occasionally from healthy participants and patients with epileptic symptoms [ 20 ]. Seven ML and DL algorithms were used in SD studies: CNN [ 265 , 266 ], SVM [ 116 , 118 , 179 , 180 , 182 , 184 , 191 , 192 , 198 , 200 , 207 , 267 ], KNN [ 189 , 268 , 269 ], ANNs [ 183 , 199 ], RF [ 185 , 187 , 190 ], LDA [ 186 ], and ELM [ 181 ]. However, among the 24 studies focused on seizures, 12 applied the SVM algorithm with various kernels.…”

Section: Discussionmentioning

confidence: 99%

“…The RBF kernel is a common function used in the SVM algorithm applied to different tasks, as described by Hamed et al [ 179 ] and Jaiswal and Banka [ 180 ]. Furthermore, the most common feature extraction methods reported in the publications selected for our study were WT [ 118 , 179 , 183 , 184 , 198 , 199 , 267 ], EMD [ 116 , 182 , 192 ], PCA [ 180 , 189 , 190 , 207 ], and FFT [ 268 , 269 ]. Similarly, the statistical feature extraction method 1D-TP used widely for image processing [ 270 ] has been applied [ 187 ] to generate the lower and upper features of each signal.…”

Section: Discussionmentioning

confidence: 99%

“…The feature extraction methods DWT and WPD have been used in references [ 179 , 181 ], respectively. In studies using the CHB-MIT database [ 190 , 191 ], the classification performance has reached 97.12% sensitivity with RF with PCA as a feature extraction method and 96% sensitivity with SVM. In addition, the use of SVM and ANNs classifiers resulted in nearly identical performance in studies using the publicly available European epilepsy database [ 199 , 200 ], in which an average sensitivity value of 73.5% was achieved.…”

Section: Discussionmentioning

confidence: 99%

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Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

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Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

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“…Then, to validate the results from RF classification, we assessed seizure susceptibility following TBI using two other models: SVM and NN classifiers. The RF classifier was initially chosen for its robustness, proven effectiveness in predicting prior seizure onset (Mursalin et al 2017;Pinto-Orellana and Cerqueira 2017), and success in early detection of Alzheimer's disease (Amoroso et al 2018).…”

Section: Machine Learning Modelmentioning

confidence: 99%

A Machine Learning Model to Predict Seizure Susceptibility from Resting-state fMRI Connectivity

2019

Modeling and Simulation in Medicine (MSM 2019)

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Traumatic brain injury (TBI) is a leading cause of disability globally. Many patients develop post-traumatic epilepsy, or recurrent seizures following TBI. In recent years, significant efforts have been made to identify biomarkers of epileptogenesis that may assist in preventing seizure occurrence by identifying high-risk patients. We present a novel method of assessing seizure susceptibility using data from 49 patients enrolled in the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx). We employ a machine learning paradigm that utilizes a Random Forest classifier trained with resting-state functional magnetic resonance imaging (fMRI) data to predict seizure outcomes. Following 100 rounds of stratified crossvalidation with 70% of resting state fMRI scans as the training set and 30% as the testing set, our model was found to assess seizure outcome in the testing set with 69% accuracy. To validate the method, we compared our results with classification by Support Vector Machines and Neural Network classifiers.

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Patient-Specific Epilepsy Seizure Detection Using Random Forest Classification over One-Dimension Transformed EEG Data

Cited by 7 publications

References 29 publications

Automated Seizure Detection Based on State-Space Model Identification

Automated Seizure Detection Based on State-Space Model Identification

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

A Machine Learning Model to Predict Seizure Susceptibility from Resting-state fMRI Connectivity

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