iSeizdiag: toward the framework development of epileptic seizure detection for healthcare.

IF 2.1 4区医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Frontiers in Computational Neuroscience Pub Date : 2025-05-27 eCollection Date: 2025-01-01 DOI:10.3389/fncom.2025.1545425

Ashish Sharma, Akshat Saxena, Mradul Agrawal, Kunal Kishor, Deepti Kaushik, Prateek Jain, Arvind R Yadav, Manob Jyoti Saikia

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Abstract

Introduction: The seizure episodes result from abnormal and excessive electrical discharges by a group of brain cells. EEG framework-based signal acquisition is the real-time module that records the electrical discharges produced by the brain cells. The electrical discharges are amplified and appear as a graph on electroencephalogram systems. Different neurological disorders are represented as different waves on EEG records.

Method: This paper involves the detection of Epilepsy which appears as rapid spiking on electroencephalogram signals, using feature extraction and machine learning techniques. Various models, such as the Support Vector Machine, K Nearest Neighbor, and random forest, have been trained, and accuracy has been analyzed to predict the seizure.

Result: An average accuracy of 95% has been claimed using the optimized model for epileptic seizure detection during training and validation. During the analysis of multiple models, the 97% accuracy is claimed after testing. Some statistical parameters are calculated to justify the optimized framework.

Discussion: The proposed approach represents a satisfactory contribution in precise detection for smart healthcare.

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面向医疗保健的癫痫发作检测框架发展。

癫痫发作是由一组脑细胞异常和过度放电引起的。基于脑电图框架的信号采集是记录脑细胞放电的实时模块。放电被放大并在脑电图系统上显示为图形。不同的神经系统疾病在脑电图记录上表现为不同的波。方法：采用特征提取和机器学习技术对脑电图信号中表现为快速尖峰的癫痫进行检测。各种模型，如支持向量机、K近邻和随机森林，已经被训练，并分析了预测癫痫发作的准确性。结果：优化后的模型在训练和验证过程中用于癫痫发作检测的平均准确率达到95%。在对多个模型的分析中，经过测试，准确率达到97%。计算了一些统计参数来验证优化后的框架。讨论：提出的方法在智能医疗的精确检测方面做出了令人满意的贡献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES

CiteScore

5.30

自引率

3.10%

发文量

166

审稿时长

6-12 weeks

期刊介绍： Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro