Inspection of EEG signals for efficient seizure prediction

Alshebeili, Saleh A.; Sedik, Ahmed; El-Rahiem, Basma Abd; Alotaiby, Turky N.; Banby, Ghada M. El; El-Khobby, Heba A.; Ali, Mahmoud A. A.; Khalaf, Ashraf A. M.; El‐Samie, Fathi E. Abd

doi:10.1016/j.apacoust.2020.107327

Cited by 25 publications

(10 citation statements)

References 13 publications

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“…Most of the seizure prediction strategies are user-specific due to the variation in the type and location of the seizure with the EEG signals of patients. The conventional technique of seizure prediction consists of processes such as pre-processing of signals, selection of features, and classification [ 13 , 14 , 15 , 16 ]. The pre-processing step is executed to remove unwanted noise, enhance signal quality, and so on.…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals

Kapoor

Nagpal

Jain

et al. 2022

Sensors

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Visual analysis of an electroencephalogram (EEG) by medical professionals is highly time-consuming and the information is difficult to process. To overcome these limitations, several automated seizure detection strategies have been introduced by combining signal processing and machine learning. This paper proposes a hybrid optimization-controlled ensemble classifier comprising the AdaBoost classifier, random forest (RF) classifier, and the decision tree (DT) classifier for the automatic analysis of an EEG signal dataset to predict an epileptic seizure. The EEG signal is pre-processed initially to make it suitable for feature selection. The feature selection process receives the alpha, beta, delta, theta, and gamma wave data from the EEG, where the significant features, such as statistical features, wavelet features, and entropy-based features, are extracted by the proposed hybrid seek optimization algorithm. These extracted features are fed forward to the proposed ensemble classifier that produces the predicted output. By the combination of corvid and gregarious search agent characteristics, the proposed hybrid seek optimization technique has been developed, and is used to evaluate the fusion parameters of the ensemble classifier. The suggested technique’s accuracy, sensitivity, and specificity are determined to be 96.6120%, 94.6736%, and 91.3684%, respectively, for the CHB-MIT database. This demonstrates the effectiveness of the suggested technique for early seizure prediction. The accuracy, sensitivity, and specificity of the proposed technique are 95.3090%, 93.1766%, and 90.0654%, respectively, for the Siena Scalp database, again demonstrating its efficacy in the early seizure prediction process.

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Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals

Kapoor

Nagpal

Jain

et al. 2022

Sensors

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“…CNNs have been used for the prediction of seizures in [36], [38] and [56], while the studies used all channel iEEG signals, ignoring the channel selection. Although the channel selection strategies have been applied in [57][58][59][60] for seizure prediction, these studies mainly focused on the conventional machine learning methods. [57][58][59][60] Hence, in this work, we proposed a method of 1D-CNN combined with channel selection strategy for seizure prediction.…”

Section: Discussionmentioning

confidence: 99%

“…Although the channel selection strategies have been applied in [57][58][59][60] for seizure prediction, these studies mainly focused on the conventional machine learning methods. [57][58][59][60] Hence, in this work, we proposed a method of 1D-CNN combined with channel selection strategy for seizure prediction. From the perspective of incremental learning, the iEEG signals with a channel increase strategy (from single channel to multiple channels, and then to all channels) were used as the inputs of 1D-CNNs with the same structure.…”

Section: Discussionmentioning

confidence: 99%

One-Dimensional Convolutional Neural Networks Combined with Channel Selection Strategy for Seizure Prediction Using Long-Term Intracranial EEG

Wang

Zhang

Wang

et al. 2021

Int. J. Neur. Syst.

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Seizure prediction using intracranial electroencephalogram (iEEG) has attracted an increasing attention during recent years. iEEG signals are commonly recorded in the form of multiple channels. Many previous studies generally used the iEEG signals of all channels to predict seizures, ignoring the consideration of channel selection. In this study, a method of one-dimensional convolutional neural networks (1D-CNN) combined with channel selection strategy was proposed for seizure prediction. First, we used 30-s sliding windows to segment the raw iEEG signals. Then, the 30-s iEEG segments, which were in three channel forms (single channel, channels only from seizure onset or free zone and all channels from seizure onset and free zones), were used as the inputs of 1D-CNN for classification, and the patient-specific model was trained. Finally, the channel form with the best classification was selected for each patient. The proposed method was evaluated on the Freiburg Hospital iEEG dataset. In the situation of seizure occurrence period (SOP) of 30[Formula: see text]min and seizure prediction horizon (SPH) of 5[Formula: see text]min, 98.60[Formula: see text] accuracy, 98.85[Formula: see text] sensitivity and 0.01/h false prediction rate (FPR) were achieved. In the situation of SOP of 60[Formula: see text]min and SPH of 5[Formula: see text]min, 98.32[Formula: see text] accuracy, 98.48[Formula: see text] sensitivity and 0.01/h FPR were attained. Compared with the many existing methods using the same iEEG dataset, our method showed a better performance.

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“…Identifying epileptogenic zones prior to surgery is an indispensable step for patient before surgery. Alshebeili et al (2020) proposed a framework that uses DWT and SVM to solve the problem of focus positioning. The framework used the best frequency band characteristics and wavelet coefficient characteristics, and its positioning accuracy could reach 88.0%.…”

Section: Seizure Prediction and Localizationmentioning

confidence: 99%

Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

Liu

Xiao

et al. 2021

Front. Syst. Neurosci.

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Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.

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Inspection of EEG signals for efficient seizure prediction

Cited by 25 publications

References 13 publications

Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals

Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals

One-Dimensional Convolutional Neural Networks Combined with Channel Selection Strategy for Seizure Prediction Using Long-Term Intracranial EEG

Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

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