A multistage, multimethod approach for automatic detection and classification of epileptiform EEG

Abstract

Abstract-An efficient system for detection of epileptic activity in ambulatory electroencephalogram (EEG) must be sensitive to abnormalities while keeping the false-detection rate to a low level. Such requirements could be fulfilled neither by single stage nor by simple method strategy, due to the extreme variety of EEG morphologies and frequency of artifacts. The present study proposes a robust system that combines multiple signal-processing methods in a multistage scheme, integrating adaptive filtering, wavelet transform, artificial neural network, and expert system. The system consists of two main stages: a preliminary screening stage in which data are reduced significantly, followed by an analytical stage. Unlike most systems that merely focus on sharp transients, our system also takes into account slow waves. A nonlinear filter for separation of nonstationary and stationary EEG components is also developed in this paper. The system was evaluated on testing data from 81 patients, totaling more than 800 hours of recordings. 90.0% of the epileptic events were correctly detected. The detection rate of sharp transients was 98.0% and overall false-detection rate was 6.1%. We conclude that our system has good performance in detecting epileptiform activities and the multistage multimethod approach is an appropriate way of solving this problem.Index Terms-Adaptive filtering, artificial neural network, electroencephalogram (EEG), epilepsy, wavelet transform.

Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

“…These algorithms can be categorized into four main approaches based on time domain [7–15], frequency domain [16], time-frequency domain [10, 17], and nonlinear [18]. In time domain approach, the peaks are analyzed in time.…”

“…Various feature extraction techniques have been proposed which are model-based [21], wavelet analysis [22], template matching [23], and power spectra analysis [24]. Several classifiers have been used, which are rule-based [8, 24], artificial neural network (ANN) [10, 11, 25, 26], support vector machine (SVM) [7, 27], and expert system [10]. The highlighted purposes in designing the framework are to achieve the highest performance and to reduce the computational time.…”

“…In time domain approach, fourteen different peak features are recognized from different peak models [7–10]. The peak model is a set of peak features that represents a peak by its amplitude, width, and slope.…”

“…In time domain analysis, fourteen different peak features are recognized from different peak models [8–10]. The earliest peak model was introduced by Dumpala et al in 1982 [8].…”

“…In addition, Liu et al [10] have introduced eleven peak features. The proposed peak model consists of four amplitudes: (1) the amplitude between the magnitude of peak point and the magnitude of valley point at the first half wave; (2) the amplitude between the magnitude of peak point and the magnitude of valley point of the second half wave; (3) the amplitude between the magnitude of peak and the magnitude of turning point at the first half wave, and (4) the amplitude between the magnitude of peak and the magnitude of turning point at the second half wave.…”