Implementation of a Morphological Filter for Removing Spikes from the Epileptic Brain Signals to Improve Identification Ripples

Abstract

Epilepsy is a very common disease affecting at least 1% of the population, comprising a number of over 50 million people. As many patients suffer from the drug-resistant version, the number of potential treatment methods is very small. However, since not only the treatment of epilepsy, but also its proper diagnosis or observation of brain signals from recordings are important research areas, in this paper, we address this very problem by developing a reliable technique for removing spikes and sharp transients from the baseline of the brain signal using a morphological filter. This allows much more precise identification of the so-called epileptic zone, which can then be resected, which is one of the methods of epilepsy treatment. We used eight patients with 5 KHz data set and depended upon the Staba 2002 algorithm as a reference to detect the ripples. We found that the average sensitivity and false detection rate of our technique are significant, and they are ∼94% and ∼14%, respectively.

Editorial: Biomedical Data in Human–Machine Interaction

“…Al-Bakri et al in [ 11 ] titled “Implementation of a Morphological Filter for Removing Spikes from the Brain Signals to Improve Identification Ripples” focused on using a morphological filter in the analysis of invasively recorded brain signals—intracranial EEG (iEEG)—from patients affected with epilepsy in order to improve the identification of epilepsy-related ripples. Their method’s average sensitivity and false detection rate is significant, as it is and , respectively.…”