Abstract:
Seizure forecasting systems have been studied in recent years for improving the quality of life for patients with epilepsy and gain further understanding about seizures. ...Show MoreMetadata
Abstract:
Seizure forecasting systems have been studied in recent years for improving the quality of life for patients with epilepsy and gain further understanding about seizures. A common approach for this is the study of electroencephalography (EEG) recordings, using signal processing techniques and, more recently, machine learning algorithms. A four-stage system is developed for patient-specific seizure prediction; consisting of pre-processing, dimensionality reduction, feature extraction and classification between interictal and preictal EEG signals. A hybrid method using principal component analysis (PCA) and independent component analysis (ICA) is applied for dimensionality reduction. Nonlinear features are selected for the analysis and characterization of the signals. A Hidden Markov Model (HMM) with Gaussian mixture emissions is trained for each type of signal and evaluated as a classifier. A sensitivity of 0.95 and a specificity of 0.86 were achieved.
Published in: 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)
Date of Conference: 05-07 September 2018
Date Added to IEEE Xplore: 15 November 2018
ISBN Information: