Detection of Spikes with Artificial Neural Networks Using Raw EEG

Özdamar, Özcan; Kalaycı, T.

doi:10.1006/cbmr.1998.1475

Cited by 66 publications

(43 citation statements)

References 26 publications

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“…ANNs have been trained using either raw data (Ko & Chung, 2000;Ozdamar & Kalayci, 1998;Pang, Upton, Shine, & Kamath, 2003;Webber, et al, 1994) or select features (Acir, Oztura, Kuntalp, Baklan, & Guzelis, 2005;Castellaro et al, 2002;Gabor & Seyal, 1992;Liu, Zhang, & Yang, 2002;Pang, et al, 2003;Tzallas, Karvelis, et al, 2006;Webber, et al, 1994) to detect spikes. In the first case, windows of raw EEG data are fed into an ANN.…”

Section: F Methods Based On Artificial Neural Networkmentioning

confidence: 99%

Automated Epileptic Seizure Detection Methods: A Review Study

Tzallas¹,

Tsipouras²,

Tsalikakis³

et al. 2012

Epilepsy - Histological, Electroencephalographic and Psychological Aspects

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Section: F Methods Based On Artificial Neural Networkmentioning

confidence: 99%

Automated Epileptic Seizure Detection Methods: A Review Study

Tzallas¹,

Tsipouras²,

Tsalikakis³

et al. 2012

Epilepsy - Histological, Electroencephalographic and Psychological Aspects

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“…The relevance with respect to the problem is checked by using problem dependent rules. Usually, neural networks are implemented to create such rules automatically from a training set of data [5,6].…”

Section: A Three Stages Proceduresmentioning

confidence: 99%

Adapted Wavelets for Pattern Detection

Mesa

2005

Lecture Notes in Computer Science

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Abstract. Wavelets are widely used in numerous applied fields involving for example signal analysis, image compression or function approximation. The idea of adapting wavelet to specific problems, it means to create and use problem and data dependent wavelets, has been developed for various purposes. In this paper, we are interested in to define, starting from a given pattern, an efficient design of FIR adapted wavelets based on the lifting scheme. We apply the constructed wavelet for pattern detection in the 1D case. To do so, we propose a three stages detection procedure which is finally illustrated by spike detection in EEG.

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“…Morphological Filters are employed in [11] for the task of epileptic spike detection using geometric characteristics. Some machine learning techniques such as Artificial Neural Networks (ANN) [12,13], K-means clustering [14] and Support Vector Machines [15] have been used as effective tools for spike classification and detection based on two main approaches, whether using raw EEG or features extracted from raw EEG for model training and testing.…”

Section: Introductionmentioning

confidence: 99%

A Multistage System for Automatic Detection of Epileptic Spikes

Anh-Dao¹,

Linh-Trung

Nguyen

et al. 2018

REV J. Electron. Commun.

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Abstract-A multistage automatic detection system for epileptic spikes is introduced as an assistant tool for epileptic analysis and diagnosis based on electroencephalogram (EEG). The system consists of four stages: preprocessing, feature extraction, classifier and expert system. Multiple state-of-the-art signal processing and machine learning techniques including wavelet transform, spectral filtering, artificial neural network are utilized in order to improve the ability of the overall system, stage by stage. Compared to other works, our contributions are three-fold: peaks in the EEG recording are categorized into two groups of non-epileptic spikes and possible epileptic spikes by a committee of three perceptrons; appropriate mother wavelet and wavelet scales are selected for the best system performance; and, based on the neurological fact that an epileptic spike is usually followed by a slow wave, a simple expert system is presented to eliminate pseudo-spikes which are closely analogous to true epileptic spikes. Experimental results show that the proposed system is capable of detecting epileptic spikes efficiently.

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Detection of Spikes with Artificial Neural Networks Using Raw EEG

Cited by 66 publications

References 26 publications

Automated Epileptic Seizure Detection Methods: A Review Study

Automated Epileptic Seizure Detection Methods: A Review Study

Adapted Wavelets for Pattern Detection

A Multistage System for Automatic Detection of Epileptic Spikes

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