title: The effect of multiscale PCA de-noising in epileptic seizure detection.
abstract: In this paper we describe the effect of Multiscale Principal Component Analysis (MSPCA) de-noising method in terms of epileptic seizure detection. In addition, we developed a patient-independent seizure detection algorithm using Freiburg EEG database. Each patient contains datasets called "ictal" and "interictal". Window length of 16 s was applied to extract EEG segments from datasets of each patient. Furthermore, Power Spectral Density (PSD) of each EEG segment was estimated using different spectral analysis methods. Afterwards, these values were fed as input to different machine learning methods that were responsible for seizure detection. We also applied MSPCA de-noising method to EEG segments prior to PSD estimation to determine if MSPCA can further enhance the classifiers' performance. The MSPCA drastically improved both the sensitivity and the specificity, increasing the overall accuracy of all three classifiers up to 20%. The best overall detection accuracy (99.59%) was achieved when Eigenvector analysis was used for frequency estimation, and C4.5 as a classifier. The experiment results show that MSPCA is an effective de-noising method for improving the classification performance in epileptic seizure detection.
authors.affiliation: Department of Electrical and Electronics Engineering, International Burch University, Sarajevo, 71000, Bosnia and Herzegovina, jasmin.kevric@ibu.edu.ba.
authors.forename: Jasmin
authors.initials: J
authors.lastname: Kevric
authors.forename: Abdulhamit
authors.initials: A
authors.lastname: Subasi
mesh_terms: D001185:Artificial Intelligence; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D025341:Principal Component Analysis / Q000379:methods*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted