Mishra Shruti, Kumar Satapathy Sandeep, Mohanty Sachi Nandan, Pattnaik Chinmaya Ranjan
Department of Computer Science & Engineering, Vellore Institute of Technology, Chennai, india.
School of Computer Science &Engineering, VIT-AP University, Amaravati, India.
Commun Integr Biol. 2022 Dec 15;16(1):2153648. doi: 10.1080/19420889.2022.2153648. eCollection 2023.
Epilepsy is one of the dreaded conditions that had taken billions of people under its cloud worldwide. Detecting the seizure at the correct time in an individual is something that medical practitioners focus in order to help people save their lives. Analysis of the Electroencephalogram (EEG) signal from the scalp area of the human brain can help in detecting the seizure beforehand. This paper presents a novel classification technique to classify EEG brain signals for epilepsy identification based on Discrete Wavelet Transform and Moth Flame Optimization-based Extreme Learning Machine (DM-ELM). ELM is a very popular machine learning method based on Neural Networks (NN) where the model is trained rigorously to get the minimized error rate and maximized accuracy. Here we have used several experimental evaluations to compare the performance of basic ELM and DM-ELM and it has been experimentally proved that DM-ELM outperforms basic ELM but with few time constraints.
癫痫是一种可怕的疾病，在全球范围内已使数十亿人笼罩在其阴影之下。在个体中及时检测到癫痫发作是医学从业者关注的重点，以便帮助人们挽救生命。对来自人类大脑头皮区域的脑电图（EEG）信号进行分析有助于提前检测到癫痫发作。本文提出了一种基于离散小波变换和基于蛾火焰优化的极限学习机（DM-ELM）的新颖分类技术，用于对脑电图脑信号进行分类以识别癫痫。极限学习机是一种基于神经网络（NN）的非常流行的机器学习方法，在该方法中，模型经过严格训练以获得最小化的错误率和最大化的准确率。在这里，我们使用了多种实验评估来比较基本极限学习机和DM-ELM的性能，并且实验证明DM-ELM优于基本极限学习机，但存在一些时间限制。
