极限学习机
脑电图
癫痫
模式识别(心理学)
人工智能
奇异值分解
灵敏度(控制系统)
计算机科学
信号(编程语言)
语音识别
人工神经网络
心理学
神经科学
工程类
电子工程
程序设计语言
作者
R. Harikumar,C. Ganesh Babu,M. Gowri Shankar
标识
DOI:10.1080/03772063.2021.1987997
摘要
Epilepsy is a chronic brain condition affecting one in 100 patients. EEG (Electroencephalogram) is a signal representing the influence of combining various processes in the brain. This paper aims to identify the likelihood levels of epilepsy from the features extracted from the EEG signals and to compare them with the output of the Singular Value Decomposition and Extreme Learning Machine (ELM). The SVD approach extracts functionality from EEG signals (that is, to minimize dimensionality) while the ELM is used as a classification system. A study of twenty patients is documented in this paper. The criteria such as the Performance Index (PI), Sensitivity (Se), Specificity (Sp), Average Detection (AD), and Good Detection Ratio (GDR) are evaluated using ELM to identify epilepsy. Results show that when SVD is classified with ELM at sigmoid activation function, an average detection of 98.94% and a GDR of 97.83% are obtained.
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