{"title":"ECn-MultiBSTM: multiclass epileptic seizure classification using electro cetacean optimized bidirectional long short-term memory model.","authors":"Pankaj Kunekar, Pankaj Dadheech, Mukesh Kumar Gupta","doi":"10.1007/s11571-025-10268-4","DOIUrl":null,"url":null,"abstract":"<p><p>Multiclass epileptic seizureclassification aims to identify and categorize different epileptic seizure types like a non-epileptic seizure, epileptic interictal seizure, and epileptic ictal seizurein individuals based on Electroencephalography (EEG) signal characteristics. Multi-class seizure classification requires recognizing various seizure forms and patterns, which can be challenging due to noise and high variability patterns in EEG signals. Existing models face limitations such as difficulty in handling the complex and dynamic nature of seizure patterns, poor generalization to unseen data, and sensitivity to noise and artifacts, all of which impact classification accuracy and reliability. To address these issues, the Electro Cetacean Optimization based Multi Bidirectional Long Short-Term Memory (ECn-MultiBSTM) model is proposed. The BiLSTM modelis utilized for feature extraction, which captures sequential data by processing data in both forward and backward directions. This bidirectional approach enables the model to identify subtle patterns that distinguish various seizure types with higher accuracy. The ECn-MultiBSTM model also incorporates advanced Electro Cetacean optimizationtechniques that enhance its ability to search efficiently for optimal solutions.Through dynamic social coordination and rapid search strategies, the model fine-tunes its hyperparameters, ensuring improved performance and adaptability.The proposed ECn-MultiBSTM model significantly enhances multiclassseizure classification performance, achieving impressive metrics of 95.84% accuracy, 95.30% precision, 95.54% F1-score,0.94% MCC, 95.79% sensitivity, and 95.88% specificity when evaluated on the CHB-MIT SCALP EEG dataset.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"83"},"PeriodicalIF":3.1000,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116414/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Neurodynamics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11571-025-10268-4","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/5/27 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Multiclass epileptic seizureclassification aims to identify and categorize different epileptic seizure types like a non-epileptic seizure, epileptic interictal seizure, and epileptic ictal seizurein individuals based on Electroencephalography (EEG) signal characteristics. Multi-class seizure classification requires recognizing various seizure forms and patterns, which can be challenging due to noise and high variability patterns in EEG signals. Existing models face limitations such as difficulty in handling the complex and dynamic nature of seizure patterns, poor generalization to unseen data, and sensitivity to noise and artifacts, all of which impact classification accuracy and reliability. To address these issues, the Electro Cetacean Optimization based Multi Bidirectional Long Short-Term Memory (ECn-MultiBSTM) model is proposed. The BiLSTM modelis utilized for feature extraction, which captures sequential data by processing data in both forward and backward directions. This bidirectional approach enables the model to identify subtle patterns that distinguish various seizure types with higher accuracy. The ECn-MultiBSTM model also incorporates advanced Electro Cetacean optimizationtechniques that enhance its ability to search efficiently for optimal solutions.Through dynamic social coordination and rapid search strategies, the model fine-tunes its hyperparameters, ensuring improved performance and adaptability.The proposed ECn-MultiBSTM model significantly enhances multiclassseizure classification performance, achieving impressive metrics of 95.84% accuracy, 95.30% precision, 95.54% F1-score,0.94% MCC, 95.79% sensitivity, and 95.88% specificity when evaluated on the CHB-MIT SCALP EEG dataset.
期刊介绍:
Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models.
The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome.
The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged.
1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics.
2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages.
3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.