Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering

Abstract

Abstract-Dynamic state recognition and event-prediction are fundamental tasks in biomedical signal processing. We present a new, electroencephalogram (EEG)-based, brain-state identification method which could form the basis for forecasting a generalized epileptic seizure. The method relies on the existence in the EEG of a preseizure state, with extractable unique features, a priori undefined.We exposed 25 rats to hyperbaric oxygen until the appearance of a generalized EEG seizure. EEG segments from the preexposure, early exposure, and the period up to and including the seizure were processed by the fast wavelet transform. Features extracted from the wavelet coefficients were inputted to the unsupervised optimal fuzzy clustering (UOFC) algorithm.The UOFC is useful for classifying similar discontinuous temporal patterns in the semistationary EEG to a set of clusters which may represent brain-states. The unsupervised selection of the number of clusters overcomes the a priori unknown and variable number of states. The usually vague brain state transitions are naturally treated by assigning each temporal pattern to one or more fuzzy clusters.The classification succeeded in identifying several, behaviorbacked, EEG states such as sleep, resting, alert and active wakefulness, as well as the seizure. In 16 instances a preseizure state, lasting between 0.7 and 4 min was defined. Considerable individual variability in the number and characteristics of the clusters may postpone the realization of an early universal epilepsy warning. Universality may not be crucial if using a dynamic version of the UOFC which has been taught the individual's normal vocabulary of EEG states and can be expected to detect unspecified new states.

Motor Unit Action Potential Duration: Measurement and Significance

“…This technique has provided promising results in the analysis of various electrophysiological signals such as blink reflex [49], EMG and electrocardiographic recordings [50][51][52], electroencephalographic signals for analysis of epileptic activity [53], and event-related potentials [54]. By regarding transformed EMG signals at a suitable scale in the DWT domain, it is possible to evade high frequency noise and low frequency BL fluctuations.…”

Automated Epileptic Seizure Detection Methods: A Review Study

“…Additionally, these methods can alert medical staff, and allow them to perform behavioural testing to further assess which specific functions may be impaired because of an epileptic seizure and help them in localizing the source of the epileptic seizure activity. Methods used to predict epileptic seizures include time-domain analysis (Lange, Lieb, Engel, & Crandall, 1983), frequency-based methods (Schiff et al, 2000), nonlinear dynamics and chaos , methods of delays (Le Van Quyen et al, 2001), and intelligent approaches (Geva & Kerem, 1998). Advances in seizure prediction promise to give rise to implantable devices able to warn of impending seizures and to trigger therapy to prevent clinical epileptic attacks (Litt & Echauz, 2002;McSharry, Smith, & Tarassenko, 2003).…”

Classification of patient by analyzing EEG signal using DWT and least square support vector machine

“…The author has used the recurrent neural network for detecting epilepsy and the neural net-* Mohd Zuhair, RMSoEE, IIT Kharagpur, 9038273634 & md.zuhair.cs@gmail.com work was trained and tested for the epileptic EEG signal with an accuracy of 99.6%, Keshri et al, [4] has used the slope of the lines between each pair of two consecutive data points (x1, y1) and (x2, y2) and feed it into Deterministic Finite Automata and got the accuracy level as high as 95.68%. Geva et al, [5] has used wavelet analysis as both time and frequency domain view can be provided with the use of WT.…”