Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review

Rasheed, Khansa; Qayyum, Adnan; Qadir, Junaid; Sivathamboo, Shobi; Kwan, Patrick; Kuhlmann, Levin; O’Brien, Terence J.; Razi, Adeel

doi:10.1109/rbme.2020.3008792

Cited by 206 publications

(143 citation statements)

References 121 publications

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“…This chapter describes about the results obtained by the novel algorithm VSPO-SVM against FCM-MPSO (3), EDMLC (6) and K-MODE (11). In terms of optimizing EEG signals, early stage seizure estimation, feature extraction, and prediction accuracy rating, VSPO-SVM outperforms the other schemes.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 1 compares the sensitivity and specificity performance analysis of VSPO-SVM against FCM-MPSO (3), EDMLC (6) and K-MODE (11). It is noted that VSPO-SVM gives the remarkable performance with enhanced results.…”

Section: Sensitivity and Specificity Performance Analysismentioning

confidence: 99%

“…The following performance measures are used in this analysis to compare the performance of the proposed algorithm VSPO-SVM to the existing schemes FCM-MPSO (3), EDMLC (6) and K-MODE (11) which were chosen as the baseline schemes in the previous chapter.…”

Section: Performance Analysis Measuresmentioning

confidence: 99%

“…The technique may be applied to any other patient from any data collection without manual feature extraction (10). Researchers provide detailed review of emerging ML technologies in early prediction of seizures using the EEG signal in order to identify the gaps, problems and pitfalls in seizure prediction research and to suggest possible guidelines (11).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust Optimization of electroencephalograph (EEG) Signals for Epilepsy Seizure Prediction by utilizing VSPO Genetic Algorithms with SVM and Machine Learning Methods

Banupriya¹,

Devi²

2021

IJST

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Objectives: To optimize the EEG signals in order to predict the epileptic seizures at early stage and to improve the accuracy level by employing genetic algorithm and machine learning methods. Methods: Virus Swarm Particle Optimization Technique (VSPO) based Genetic algorithm is utilized for the purpose of feature selection and Machine Learning SVM technique is utilized for classification of EEG signals to determine seizure or non-seizure. The Discrete Wavelet Transform (DWT) is utilized for factor extraction to assess the recurrence range of EEG signals associated with seizures, to partition them into separate spaces using DWT of EEG symbols, and to consider the variations between seizure and normal functionality. VPSO-GA with SVM extracts the features from the Andrzejak R G dataset and then selects the relevant function to perform classification and prediction in order to optimize the EEG signals for early ES prediction and to improve the accuracy level. To demonstrate the effectiveness of the proposed algorithm, MATLAB is used for implementation. The performance results are compared to the existing baseline versions FCM-MPSO, EDMLC and K-MODE. Findings: EEG signals are optimized and early ES prediction is done with 98.13% accuracy level, 98.03% sensitivity, 98.01% specificity, 98.90% Precision, 97.96% Recall, 191 True Positive, 104 True Negative and 98.46% F-Score to predict the seizure in an optimized manner which is high compared to the existing versions. Novelty: According to the findings of the comprehensive study, the proposed algorithm VPSO-SVM outperforms FCM-MPSO, EDMLC and K-MODE in terms of accuracy level of epileptic seizure prediction at early stage by optimizing the EEG signals in a robust manner.

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Section: Resultsmentioning

confidence: 99%

Section: Sensitivity and Specificity Performance Analysismentioning

confidence: 99%

Section: Performance Analysis Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Optimization of electroencephalograph (EEG) Signals for Epilepsy Seizure Prediction by utilizing VSPO Genetic Algorithms with SVM and Machine Learning Methods

Banupriya¹,

Devi²

2021

IJST

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“…[ 92 , 93 , 94 ]. Moreover, the success of advanced AI and deep-learning algorithms in epilepsy detection has opened the way to epilepsy prediction, where interictal signals that are observed between seizures are studied with the aim of extracting reliable markers of a future seizure [ 95 ].…”

Section: Neural Recording Circuit Techniquesmentioning

confidence: 99%

A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control

Ranjandish

Schmid

2020

Sensors

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Closed-loop implantable electronics offer a new trend in therapeutic systems aimed at controlling some neurological diseases such as epilepsy. Seizures are detected and electrical stimulation applied to the brain or groups of nerves. To this aim, the signal recording chain must be very carefully designed so as to operate in low-power and low-latency, while enhancing the probability of correct event detection. This paper reviews the electrical characteristics of the target brain signals pertaining to epilepsy detection. Commercial systems are presented and discussed. Finally, the major blocks of the signal acquisition chain are presented with a focus on the circuit architecture and a careful attention to solutions to issues related to data acquisition from multi-channel arrays of cortical sensors.

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Artificial intelligence extension of the OSCAR‐IB criteria

Petzold

Albrecht

Balcer

et al. 2021

Ann Clin Transl Neurol

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Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

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Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review

Cited by 206 publications

References 121 publications

Robust Optimization of electroencephalograph (EEG) Signals for Epilepsy Seizure Prediction by utilizing VSPO Genetic Algorithms with SVM and Machine Learning Methods

Robust Optimization of electroencephalograph (EEG) Signals for Epilepsy Seizure Prediction by utilizing VSPO Genetic Algorithms with SVM and Machine Learning Methods

A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control

Artificial intelligence extension of the OSCAR‐IB criteria

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