_a-novel-signal-modeling-for-classification-of-seizure-and-seizure-free-eeg-signals-myprojectbazaar.jpg "A Novel Signal Modeling for Classification of Seizure and Seizure-free EEG Signals - MyProjectBazaar from inter arma Video Screenshot Preview")
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⏲ Duration: 4 min 37 sec
✓ Published: 18-Jun-2019
✓ Published: 18-Jun-2019
Description:
This paper presents a signal modeling based new methodology of automatic seizure detection in EEG signals. The proposed method consists of three stages. First, a multirate filterbank structure is proposed that is constructed using the basis vectors of discrete cosine transform (DCT). The proposed filterbank decomposes EEG signals into its respective brain rhythms: delta, theta, alpha, beta, and gamma. Second, these brain rhythms are statistically modeled with the class of self similar Gaussian r
This paper presents a signal modeling based new methodology of automatic seizure detection in EEG signals. The proposed method consists of three stages. First, a multirate filterbank structure is proposed that is constructed using the basis vectors of discrete cosine transform (DCT). The proposed filterbank decomposes EEG signals into its respective brain rhythms: delta, theta, alpha, beta, and gamma. Second, these brain rhythms are statistically modeled with the class of self similar Gaussian r
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