Regression analysis for detecting epileptic seizure with different feature extracting strategies.

Regression analysis for detecting epileptic seizure with different feature extracting strategies. Biomed Tech (Berl). 2019 May 30;: Authors: Hussain L, Saeed S, Idris A, Awan IA, Shah SA, Majid A, Ahmed B, Chaudhary QA Abstract Due to the excitability of neurons in the brain, a neurological disorder is produced known as epilepsy. The brain activity of patients suffering from epilepsy is monitored through electroencephalography (EEG). The multivariate nature of features from time domain, frequency domain, complexity and wavelet entropy based, and the statistical features were extracted from healthy and epileptic subjects using the Bonn University database and seizure and non-seizure intervals using the CHB MIT database. The robust machine learning regression methods based on regression, support vector regression (SVR), regression tree (RT), ensemble regression, Gaussian process regression (GPR) were employed for detecting and predicting epileptic seizures. Performance was measured in terms of root mean square error (RMSE), squared error, mean square error (MSE) and mean absolute error (MAE). Moreover, detailed optimization was performed using a RT to predict the selected features from each feature category. A deeper analysis was conducted on features and tree regression methods where optimal RMSE and MSE results were obtained. The best optimal performance was obtained using the ensemble boosted regression tree (BRT) and exponential GP...
Source: Biomedizinische Technik/Biomedical Engineering - Category: Biomedical Engineering Tags: Biomed Tech (Berl) Source Type: research