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A new method based on Singular Spectrum Analysis and principal component analysis was introduced to improve accuracy levels for classifying EEG recordings compared to discrete wavelet transform.
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A hybrid Singular Spectrum Analysis and principal component analysis approach is proposed to improve the accuracy of classifying brain activity from EEG signals in epilepsy.
Classifying brain activities based on electroencephalogram (EEG) signals is one of the important applications of time series discriminant analysis for diagnosing brain disorders. In this paper, we introduce a new method based on the Singular Spectrum Analysis (SSA) technique for classifying brain activity based on EEG signals via an application into a benchmark dataset for epileptic study with five categories, consisting of 100 EEG recordings per category. The results from the SSA based approach are compared with those from discrete wavelet transform before proposing a hybrid SSA and principal component analysis based approach for improving accuracy levels further.
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Hassani et al. (2018) studied Epilepsy (n=500). Singular Spectrum Analysis (SSA) and hybrid SSA-PCA approach vs. Discrete wavelet transform was evaluated on Classification accuracy of brain activity. A new method based on Singular Spectrum Analysis and principal component analysis was introduced to improve accuracy levels for classifying EEG recordings compared to discrete wavelet transform.
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