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Sheikh S, Jehi L. Predictive models of epilepsy outcomes. Curr Opin Neurol 2024; 37:115-120. [PMID: 38224138 DOI: 10.1097/wco.0000000000001241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
PURPOSE OF REVIEW Multiple complex medical decisions are necessary in the course of a chronic disease like epilepsy. Predictive tools to assist physicians and patients in navigating this complexity have emerged as a necessity and are summarized in this review. RECENT FINDINGS Nomograms and online risk calculators are user-friendly and offer individualized predictions for outcomes ranging from safety of antiseizure medication withdrawal (accuracy 65-73%) to seizure-freedom, naming, mood, and language outcomes of resective epilepsy surgery (accuracy 72-81%). Improving their predictive performance is limited by the nomograms' inability to ingest complex data inputs. Conversely, machine learning offers the potential of multimodal and expansive model inputs achieving human-expert level accuracy in automated scalp electroencephalogram (EEG) interpretation but lagging in predictive performance or requiring validation for other applications. SUMMARY Good to excellent predictive models are now available to guide medical and surgical epilepsy decision-making with nomograms offering individualized predictions and user-friendly tools, and machine learning approaches offering the potential of improved performance. Future research is necessary to bridge the two approaches for optimal translation to clinical care.
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Affiliation(s)
| | - Lara Jehi
- Epilepsy Center, Neurological Institute
- Center for Computational Life Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
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Kapoor B, Nagpal B, Jain PK, Abraham A, Gabralla LA. Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals. SENSORS (BASEL, SWITZERLAND) 2022; 23:423. [PMID: 36617019 PMCID: PMC9824897 DOI: 10.3390/s23010423] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/15/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
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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Affiliation(s)
- Bhaskar Kapoor
- Ambedkar Institute of Advanced Communication Technologies & Research (AIACT&R), Guru Gobind Singh Indraprastha University, New Delhi 110078, India
| | - Bharti Nagpal
- NSUT (East Campus) (Formerly AIACT&R), Delhi 110031, India
| | - Praphula Kumar Jain
- Department of Computer Engineering & Applications, GLA University, Mathura 281406, India
| | - Ajith Abraham
- Machine Intelligence Research Labs (MIR Labs), Auburn, WA 98071, USA
| | - Lubna Abdelkareim Gabralla
- Department of Computer Science and Information Technology, College of Applied, Princess Nourah bint Abdulrahman University, Riyadh 11564, Saudi Arabia
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Seizure Prediction Based on Transformer Using Scalp Electroencephalogram. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094158] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Epilepsy is a chronic and recurrent brain dysfunction disease. An acute epileptic attack will interfere with a patient’s normal behavior and consciousness, having a great impact on their life. The purpose of this study was to design a seizure prediction model to improve the quality of patients’ lives and assist doctors in making diagnostic decisions. This paper presents a transformer-based seizure prediction model. Firstly, the time-frequency characteristics of electroencephalogram (EEG) signals were extracted by short-time Fourier transform (STFT). Secondly, a three transformer tower model was used to fuse and classify the features of the EEG signals. Finally, when combined with the attention mechanism of transformer networks, the EEG signal was processed as a whole, which solves the problem of length limitations in deep learning models. Experiments were conducted with a Children’s Hospital Boston and the Massachusetts Institute of Technology database to evaluate the performance of the model. The experimental results show that, compared with previous EEG classification models, our model can enhance the ability to use time, frequency, and channel information from EEG signals to improve the accuracy of seizure prediction.
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Gao T, Chen D, Tang Y, Du B, Ranjan R, Zomaya AY, Dustdar S. Adaptive density peaks clustering: Towards exploratory EEG analysis. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Detection of preictal state in epileptic seizures using ensemble classifier. Epilepsy Res 2021; 178:106818. [PMID: 34847427 DOI: 10.1016/j.eplepsyres.2021.106818] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 10/10/2021] [Accepted: 11/12/2021] [Indexed: 12/30/2022]
Abstract
OBJECTIVE Epilepsy affected patient experiences more than one frequency seizures which can not be treated with medication or surgical procedures in 30% of the cases. Therefore, an early prediction of these seizures is inevitable for these cases to control them with therapeutic interventions. METHODS In recent years, researchers have proposed multiple deep learning based methods for detection of preictal state in electroencephalogram (EEG) signals, however, accurate detection of start of preictal state remains a challenge. We propose a novel ensemble classifier based method that gets the comprehensive feature set as input and combines three different classifiers to detect the preictal state. RESULTS We have applied the proposed method on the publicly available scalp EEG dataset CHBMIT of 22 subjects. An average accuracy of 94.31% with sensitivity and specificity of 94.73% and 93.72% respectively has been achieved with the method proposed in this study. CONCLUSIONS Proposed study utilizes the preprocessing techniques for noise removal, combines deep learning based and handcrafted features and an ensemble classifier for detection of start of preictal state. Proposed method gives better results in terms of accuracy, sensitivity, and specificity.
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Wang X, Zhang G, Wang Y, Yang L, Liang Z, Cong F. One-Dimensional Convolutional Neural Networks Combined with Channel Selection Strategy for Seizure Prediction Using Long-Term Intracranial EEG. Int J Neural Syst 2021; 32:2150048. [PMID: 34635034 DOI: 10.1142/s0129065721500489] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
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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Affiliation(s)
- Xiaoshuang Wang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, P. R. China.,Faculty of Information Technology, University of Jyväskylä, Jyväskylä 40014, Finland
| | - Guanghui Zhang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, P. R. China.,Faculty of Information Technology, University of Jyväskylä, Jyväskylä 40014, Finland
| | - Ying Wang
- Department of Neurology and Psychiatry, First Affiliated Hospital, DaLian Medical University, Dalian, P. R. China
| | - Lin Yang
- Department of Neurology and Psychiatry, First Affiliated Hospital, DaLian Medical University, Dalian, P. R. China
| | - Zhanhua Liang
- Department of Neurology and Psychiatry, First Affiliated Hospital, DaLian Medical University, Dalian, P. R. China
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, P. R. China.,Faculty of Information Technology, University of Jyväskylä, Jyväskylä 40014, Finland.,School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, P. R. China.,Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province Dalian University of Technology, Dalian, P. R. China
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El-Gindy SAE, Hamad A, El-Shafai W, Khalaf AAM, El-Dolil SM, Taha TE, El-Fishawy AS, Alotaiby TN, Alshebeili SA, El-Samie FEA. Efficient communication and EEG signal classification in wavelet domain for epilepsy patients. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021; 12:9193-9208. [DOI: 10.1007/s12652-020-02624-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 10/20/2020] [Indexed: 09/01/2023]
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Muhammad Usman S, Khalid S, Bashir S. A deep learning based ensemble learning method for epileptic seizure prediction. Comput Biol Med 2021; 136:104710. [PMID: 34364257 DOI: 10.1016/j.compbiomed.2021.104710] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/29/2021] [Accepted: 07/29/2021] [Indexed: 10/20/2022]
Abstract
In epilepsy, patients suffer from seizures which cannot be controlled with medicines or surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is extremely important so that they can be controlled with medication before they actually occur. Researchers have proposed multiple machine/deep learning based methods to predict epileptic seizures; however, accurate prediction of epileptic seizures with low false positive rate is still a challenge. In this research, we propose a deep learning based ensemble learning method to predict epileptic seizures. In the proposed method, EEG signals are preprocessed using empirical mode decomposition followed by bandpass filtering for noise removal. The class imbalance problem has been mitigated with synthetic preictal segments generated using generative adversarial networks. A three-layer customized convolutional neural network has been proposed to extract automated features from preprocessed EEG signals and combined them with handcrafted features to get a comprehensive feature set. The feature set is then used to train an ensemble classifier that combines the output of SVM, CNN and LSTM using Model agnostic meta learning. An average sensitivity of 96.28% and specificity of 95.65% with an average anticipation time of 33 min on all subjects of CHBMIT has been achieved by the proposed method, whereas, on American epilepsy society-Kaggle seizure prediction dataset, an average sensitivity of 94.2% and specificity of 95.8% has been achieved on all subjects.
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Affiliation(s)
| | - Shehzad Khalid
- Department of Computer Engineering, Bahria University, Islamabad, Pakistan.
| | - Sadaf Bashir
- Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
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Liu G, Xiao R, Xu L, Cai J. Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals. Front Syst Neurosci 2021; 15:685387. [PMID: 34093143 PMCID: PMC8173051 DOI: 10.3389/fnsys.2021.685387] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 04/20/2021] [Indexed: 12/11/2022] Open
Abstract
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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Affiliation(s)
- Guangda Liu
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
| | - Ruolan Xiao
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
| | - Lanyu Xu
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
| | - Jing Cai
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
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Usman SM, Khalid S, Bashir Z. Epileptic seizure prediction using scalp electroencephalogram signals. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.01.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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