Abstract
Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets are adopted to decompose the EEG data into five frequencies sub-bands, such as gamma, beta, alpha, theta, and delta, whose statistical features were computed and used as classification features. The grid search optimizer is used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a conventional support vector machine and a random forest classifier constructed according to previous descriptions. Multiple performance indices were used to evaluate the proposed classification scheme, which provided better classification accuracy and detection effectiveness than has been recently reported in other studies on three-class classification of EEG data.
Highlights
Epilepsy is one of the most common neurological disorders, with one person in every100 worldwide suffering from this disease [1]
This paper proposes an improved grid search optimizer (GSO) to optimize the parameters of the gradient boosting machine (GBM)
random forest (RF) and GBM are decision tree models based on integration ideas, and they are better suited for solving multi-class classification problems
Summary
Epilepsy is one of the most common neurological disorders, with one person in every. 100 worldwide suffering from this disease [1]. Several studies have used combined time and frequency features for the automatic recognition of non-stationary EEG at the onset of epilepsy. The STFT treated non-stationary EEG as stationary signals and superimposed a series of short signals In another approach, Boashash et al extracted statistical and image features according to their time-frequency distribution to handle multichannel EEG from neonates [22]. Brabanter et al proposed a least squares support vector machine (LS-SVM) to classify two-class seizure and non-seizure EEG signals from the small seizure dataset of Bonn University They obtained 98.0–99.5% accuracy using a radial basis function (RBF) kernel, and 99.5–100% accuracy using a Morlet kernel [33].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
