Classification of epileptiform events in raw EEG signals using neural classifier

Scolaro, Geovani Rodrigo; Azevedo, Fernando Mendes de

doi:10.1109/iccsit.2010.5563949

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…The focus of our work is to address this gap. Here, we analysed the statistical behaviour of raw electrical signals from plants similar to previous studies on raw non-stationary biological signals which exhibit random fluctuations such as EMG/EEG, adopting a similar approach to develop a classification system [19][20][21][22]. The present paper reports the first exploration of its kind, aiming at finding meaningful statistical feature(s) from segmented plant electrical signals which may contain some signature of the stimulus hidden in them, in different extents.…”

Section: Introductionmentioning

confidence: 99%

Exploring strategies for classification of external stimuli using statistical features of the plant electrical response

Chatterjee

Das

Maharatna

et al. 2015

J. R. Soc. Interface.

104

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Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli-sodium chloride (NaCl), sulfuric acid (H 2 SO 4 ) and ozone (O 3 ). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.

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Section: Introductionmentioning

confidence: 99%

Exploring strategies for classification of external stimuli using statistical features of the plant electrical response

Chatterjee

Das

Maharatna

et al. 2015

J. R. Soc. Interface.

104

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show abstract

“…Wavelet Transform can be used for the parameterization, filtering and/or feature extraction of the EEG signals and also be very involved in the construction of hybrid intelligent systems for the automatic detection of epileptiform events, providing relatively good results when applied as a preprocessor for Artificial Neural Networks (Kalayci & Özdamar, 1995;Hoffmann et al, 1996;Oweiss & Anderson, 2001;Quiroga et al . 2001;Adeli et al, 2003;Khan & Gotman, 2003;Argoud et al, 2006, Pang et al, 2003Argoud et al, 2006;Mohamed et al, 2006;Subasi, 2007;Indiradevi et al, 2008;Abibullaev et al, 2009th, 2009bOcak, 2009;Scolaro & Azevedo, 2010).…”

Section: Approaches Based On Wavelet Transformmentioning

confidence: 99%

Biomedical Engineering, Trends in Electronics, Communications and Software

Laskovski¹

2011

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Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

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“…Wavelet Transform can be used for the parameterization, filtering and/or feature extraction of the EEG signals and also be very involved in the construction of hybrid intelligent systems for the automatic detection of epileptiform events, providing relatively good results when applied as a preprocessor for Artificial Neural Networks (Kalayci & Özdamar, 1995;Hoffmann et al, 1996;Oweiss & Anderson, 2001;Quiroga et al . 2001;Adeli et al, 2003;Khan & Gotman, 2003;Argoud et al, 2006, Pang et al, 2003, Liu et al, 2006Argoud et al, 2006;Mohamed et al, 2006;Subasi, 2007;Indiradevi et al, 2008;Abibullaev et al, 2009th, 2009bOcak, 2009;Scolaro & Azevedo, 2010).…”

Section: Approaches Based On Wavelet Transformmentioning

confidence: 99%

Automatic Detection of Paroxysms in EEG Signals Using Morphological Descriptors and Artificial Neural Networks

Boos¹,

Scolaro²,

Pereira³

2011

Biomedical Engineering, Trends in Electronics, Communications and Software

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Classification of epileptiform events in raw EEG signals using neural classifier

Cited by 7 publications

References 7 publications

Exploring strategies for classification of external stimuli using statistical features of the plant electrical response

Exploring strategies for classification of external stimuli using statistical features of the plant electrical response

Biomedical Engineering, Trends in Electronics, Communications and Software

Automatic Detection of Paroxysms in EEG Signals Using Morphological Descriptors and Artificial Neural Networks

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