Comparative analysis of multiple machine learning algorithms for epileptic seizure prediction

Lekshmy, H O; Panickar, Dhanyalaxmi; Harikumar, Sandhya

doi:10.1088/1742-6596/2161/1/012055

Cited by 12 publications

(3 citation statements)

References 23 publications

(21 reference statements)

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“…They used hybrid SVM by combining a “genetic algorithm” (GA) with “particle swarm optimization” (PSO) to determine the SVM parameters; this model achieved 99.38% accuracy. Lekshmy et al [ 84 ] provided a comparative analysis of the ML methods in epileptic seizure prediction along with their effectiveness. It was concluded that the Random Forest (RF) and long short-term memory (LSTM) algorithms achieved the highest accuracies as 97% and 98%, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy

Ahmed,

Alotaibi,

Atta-ur-Rahman

et al. 2022

SN COMPUT. SCI.

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Epilepsy is the second most common neurological disease after Alzheimer. It is a disorder of the brain which results in recurrent seizures. Though the epilepsy in general is considered as a serious disorder, its effects in children are rather dangerous. It is mainly because it reasons a slower rate of development and a failure to improve certain skills among such children. Seizures are the most common symptom of epilepsy. As a regular medical procedure, the specialists record brain activity using an electroencephalogram (EEG) to observe epileptic seizures. The detection of these seizures is performed by specialists, but the results might not be accurate and depend on the specialist’s experience; therefore, automated detection of epileptic pediatric seizures might be an optimal solution. In this regard, several techniques have been investigated in the literature. This research aims to review the approaches to pediatric epilepsy seizures’ identification especially those based on machine learning, in addition to the techniques applied on the CHB-MIT scalp EEG database of epileptic pediatric signals.

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Section: Literature Reviewmentioning

confidence: 99%

A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy

Ahmed,

Alotaibi,

Atta-ur-Rahman

et al. 2022

SN COMPUT. SCI.

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“…In addition, logistic regression, support vector machine, and k-nearest neighbor are also commonly used in classification. A review showed that random forest is the best classifier in these methods (Lekshmy et al, 2022 ). However, due to the wide variety of features that can be extracted, sometimes additional feature selection methods were needed to improve the efficiency of feature extraction (Wang and Lyu, 2014 ).…”

Section: Related Workmentioning

confidence: 99%

A multi-frame network model for predicting seizure based on sEEG and iEEG data

Zhang

et al. 2022

Front. Comput. Neurosci.

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IntroductionAnalysis and prediction of seizures by processing the EEG signals could assist doctors in accurate diagnosis and improve the quality of the patient's life with epilepsy. Nowadays, seizure prediction models based on deep learning have become one of the most popular topics in seizure studies, and many models have been presented. However, the prediction results are strongly related to the various complicated pre-processing strategies of models, and cannot be directly applied to raw data in real-time applications. Moreover, due to the inherent deficiencies in single-frame models and the non-stationary nature of EEG signals, the generalization ability of the existing model frameworks is generally poor.MethodsTherefore, we proposed an end-to-end seizure prediction model in this paper, where we designed a multi-frame network for automatic feature extraction and classification. Instance and sequence-based frames are proposed in our approach, which can help us simultaneously extract features of different modes for further classification. Moreover, complicated pre-processing steps are not included in our model, and the novel frames can be directly applied to the raw data. It should be noted that the approaches proposed in the paper can be easily used as the general model which has been validated and compared with existing model frames.ResultsThe experimental results showed that the multi-frame network proposed in this paper was superior to the existing model frame in accuracy, sensitivity, specificity, F1-score, and AUC in the classification performance of EEG signals.DiscussionOur results provided a new research idea for this field. Researchers can further integrate the idea of the multi-frame network into the state-of-the-art single-frame seizure prediction models and then achieve better results.

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“…The oldest and most prevalent neurological condition in the globe is epilepsy [4,5]. Epilepsy is the third most prevalent neurological condition in the world, affecting 50 million individuals worldwide, based on a World Health Organization (WHO) study from June 2019 [6]- [10]. An abnormality of the brain characterized by recurrent seizures is called Epilepsy.…”

Section: Introductionmentioning

confidence: 99%

Generalized Epileptic Seizure Prediction using Machine Learning Method

Altaf¹,

Unar²,

Narejo³

et al. 2023

IJACSA

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In recent years, the electroencephalography (EEG) signal identification of epileptic seizures has developed into a routine procedure to determine epilepsy. Since physically identifying epileptic seizures by expert neurologists becomes a labor-intensive, time-consuming procedure that also produces several errors. Thus, efficient, and computerized detection of epileptic seizures is required. The disordered brain function that causes epileptic seizures can have an impact on a patient's condition. Epileptic seizures can be prevented by medicine with great success if they are predicted before they start. Electroencephalogram (EEG) signals are utilized to predict epileptic seizures by using machine learning algorithms and complex computational methodologies. Furthermore, two significant challenges that affect both expectancy time and genuine positive forecast rate are feature extraction from EEG signals and noise removal from EEG signals. As a result, we suggest a model that offers trustworthy preprocessing and feature extraction techniques. To automatically identify epileptic seizures, a variety of ensemble learning-based classifiers were utilized to extract frequency-based features from the EEG signal. Our algorithm offers a higher true positive rate and diagnoses epileptic episodes with enough foresight before they begin. On the scalp EEG CHB-MIT dataset on 24 subjects, this suggested framework detects the beginning of the preictal state, the state that occurs before a few minutes of the onset of the detention, resulting in an elevated true positive rate of (91%) than conventional methods and an optimum estimation time of 33 minutes and an average time of prediction is 23 minutes and 36 seconds. Depending on the experimental findings' The maximum accuracy, sensitivity, and specificity rates in this research were 91 %, 98%, and 84%.

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Comparative analysis of multiple machine learning algorithms for epileptic seizure prediction

Cited by 12 publications

References 23 publications

A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy

A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy

A multi-frame network model for predicting seizure based on sEEG and iEEG data

Generalized Epileptic Seizure Prediction using Machine Learning Method

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