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首页> 外文会议>2015 IEEE Student Symposium in Biomedical Engineering amp; Sciences >Performance comparison of fuzzy mutual information as dimensionality reduction techniques and SRC, SVD and approximate entropy as post classifiers for the classification of epilepsy risk levels from EEG signals
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Performance comparison of fuzzy mutual information as dimensionality reduction techniques and SRC, SVD and approximate entropy as post classifiers for the classification of epilepsy risk levels from EEG signals

机译：模糊互信息作为降维技术与SRC，SVD和近似熵作为后分类器对EEG信号进行癫痫风险水平分类的性能比较

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摘要

One of the most commonly occurring disorder in the brain is epilepsy and it is characterized by the sudden onset of recurrent seizures. When the electrical discharge in the brain bursts suddenly in an abnormal fashion it leads to epilepsy. Epilepsy is a neurological disorder of the Central Nervous System (CNS) and causes great trouble to mankind because of the recurrence of the seizures. The EEG provides a significant tool for exploring the neural activities in the network of the brain which is widely associated with the synchronous changes happening in the membrane potentials of neighbouring neurons. This paper provides a performance comparison when Fuzzy Mutual Information (FMI) acts as a dimensionality reduction technique followed by the Sparse Representation Classifier (SRC), Singular Value Decomposition (SVD), Approximate Entropy (ApEn) as Post Classifiers for the Classification of Epilepsy Risk Levels from EEG Signals. The bench mark parameters considered here are Performance Index (PI), Quality Value (QV), Specificity, Sensitivity, Time Delay and Accuracy.
机译：大脑中最常见的疾病之一是癫痫，其特征是反复发作的突然发作。当大脑中的放电突然异常爆发时，会导致癫痫病。癫痫病是中枢神经系统（CNS）的一种神经系统疾病，由于癫痫发作的复发，给人类带来了极大的麻烦。脑电图为探索大脑网络中的神经活动提供了重要工具，该活动与邻近神经元膜电位发生的同步变化广泛相关。本文提供了当模糊互信息（FMI）用作降维技术，紧随其后的是稀疏表示分类器（SRC），奇异值分解（SVD），近似熵（ApEn）作为癫痫风险分类的后分类器时的性能比较。脑电信号的水平。这里考虑的基准参数是性能指标（PI），质量值（QV），特异性，灵敏度，时间延迟和准确性。

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