Morphological operator based feature extraction technique along with suitable post classifiers for epilepsy risk level classification | IEEE Conference Publication | IEEE Xplore

Morphological operator based feature extraction technique along with suitable post classifiers for epilepsy risk level classification


Abstract:

Electroencephalogram (EEG) is a powerful tool for the diagnosis of neurological disorders. Since its discovery, the EEG has been used for the diagnosis of epilepsy, for t...Show More

Abstract:

Electroencephalogram (EEG) is a powerful tool for the diagnosis of neurological disorders. Since its discovery, the EEG has been used for the diagnosis of epilepsy, for trauma assessment, for sleep research, and for the analysis of higher brain functions. The EEG is highly dependent upon the availability of high quality instrumentation, and almost from the beginning, automated methods of signal processing have been applied. Recording the EEG during a seizure is particularly helpful in determining whether a patient has epilepsy or not, because seizures usually occur infrequently and unpredictably and obtaining such recording might require an EEG extending over several days. If the EEG recordings are too long, then the process is too much time consuming and hence spike detection methods which can perform automatically are needed. Morphological Filter (MF) is one such technique used for the automatic detection of spikes in epileptic EEG signals. This paper thus presents the performance analysis using morphological filtering technique along with Principal Component Analysis (PCA), Approximate Entropy (ApEn) and Sparse Representation Classifiers (SRC) as Post Classifiers for the Classification of Epilepsy Risk Levels from EEG Signals.
Date of Conference: 28-30 November 2015
Date Added to IEEE Xplore: 24 March 2016
ISBN Information:
Conference Location: Okinawa, Japan

I. Introduction

The human Electroencephalogram (EEG) is usually recorded from electrodes attached to the human scalp using high amplifiers [1]. The amplified signals are generally printed on paper using polygraph technology which contains usually 8 to 128 channels. One of the primary goals is to help the Encephalographer (EEGer) in the time consuming task of quantification of the signal that appears to the eye as a low information content background intermixed with either bursts of rhythmic activity with different frequencies (the EEG rhythms) or short transients of clinical significance (such as spikes) [2]. In spite of years of research to produce universal automated detection methods, success has been achieved only in specific areas [3]. Accomplishments include automatically sleep staging with a high degree of accuracy; counting spikes and wave complexes, and monitoring in intensive care units. However clinicians still rely on visual analysis for clinical applications.

References

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