Epileptic seizure detection using dynamic wavelet network

Subaşı, Abdülhamit

doi:10.1016/j.eswa.2005.04.007

Cited by 167 publications

(76 citation statements)

References 49 publications

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“…Song and Liò [29] developed an EEG epilepsy detection scheme based on the entropy-based feature extraction and extreme learning machine. Subasi [30] applied a novel method of analysis of EEG signals using discrete wavelet transform and classification using ANN. Gular et al [31] proposed an idea of a study for the assessment of accuracy of recurrent neural networks (RNN) employing Lyapunov exponents in detection seizure in the EEG signals.…”

Section: Epilepsy and Epileptic Seizure Diagnosismentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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Section: Epilepsy and Epileptic Seizure Diagnosismentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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“…The decomposition levels number is selected based on the dominant frequency components of the signal. According to Subasi [4] , the levels are selected such that those parts of the signal that correlate well with the frequencies required for the signal classification are retained in the wavelet coefficients. Therefore in the present study, we choose level 4 wavelet decomposition.…”

Section: Discrete Wavelet Transform-feature Extractionmentioning

confidence: 99%

“…A wide range of AI techniques [1][2][3][4][5][6][7][8][9][10][11] have been proposed in the literature to solve the problem of seizure detection in EEG signals. Alkan et al [1] used EEG power spectra extracted by Multiple Signal Classification (MUSIC), Autoregressive (AR) and periodogram methods as inputs to Logistic Regression (LR) and back propagation neural networks (BPNNs) classifiers.…”

Section: Introductionmentioning

confidence: 99%

“…Further extended by Acir [3] , two discrete perceptron were used to filter the data for modified Radial Basis Function Network (RBFN) classifier. Subasi [4] decomposed EEG signals using WT into the frequency sub-bands which then used as an input to feedforward error backpropagation ANN (FEBANN) and Dynamic Wavelet Neural Network (DWN). The experiments had been approved that the DWN was more accurate than the FEBANN.…”

Section: Introductionmentioning

confidence: 99%

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Swarm Negative Selection Algorithm for Electroencephalogram Signals Classification

Ba-Karait

Shamsuddin²,

Sudirman³

2009

J. of Computer Science

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Problem statement:The process of epilepsy diagnosis from EEG signals by a human scorer is a very time consuming and costly task considering the large number of epileptic patients admitted to the hospitals and the large amount of data needs to be scored. Therefore, there is a strong need to automate this process. Such automated systems must rely on robust and effective algorithms for detection and prediction. Approach: The proposed detection system of epileptic seizure in EEG signals is based on Discrete Wavelet Transform (DWT) and Swarm Negative Selection (SNS) algorithm. DWT was used to analyze EEG signals at different frequency bands and statistics over the set of the wavelet coefficients were calculated to introduce the feature vector for SNS classifier. The SNS classification model use negative selection and PSO algorithms to form a set of memory Artificial Lymphocytes (ALCs) that have the ability to distinguish between normal and epileptic EEG patterns. Thus, adapted negative selection is employed to create a set of self-tolerant ALCs. Whereas, PSO is used to evolve these ALCs away from self patterns towards non-self space and to maintain diversity and generality among the ALCs. Results: The experimental results proved that the proposed method reveals very promising performance in classifying EEG signals. A comparison with many previous studies showed that the presented algorithm has better results outperforming those reported by earlier methods. Conclusion: The technique was approved to be robust and effective in detecting and localizing epileptic seizure in EEG recording. Hence, the proposed system can be very helpful to make faster and accurate diagnosis decision.

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“…These classifiers are based on wavelet transform [2][3][4][5][6][7][8], artificial neural networks [9][10][11], fu zzy logic [12] and non-conventional tools like finite automata [13,14]. These methods are conventionally sequential in nature and hence, processing of massive data of recorded EEG becomes difficult.…”

Section: Introductionmentioning

confidence: 99%

WEBspike: A New Proposition of Deterministic Finite Automata and Parallel Algorithm Based Web Application for EEG Spike Recognition

Keshri¹

2013

IJITCS

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Abstract-The brain signal or Electroencephalogram (EEG) has been proved as one of the most important bio-signal that deals with a nu mber of problems and disorders related to the hu man being. Ep ilepsy is one of the most commonly known disorders found in human s. The application o f EEG in epilepsy related research and treatment is now a very co mmon pract ice. Variety of smart tools and algorith ms exist to assist the experts in taking decision related to the treatment to be provided to an epileptic patient. However, web based applications or tools are still needed that can assist those doctors and experts, who are not having such existing smart tools for EEG analysis with them. In the current work, a web based system named WEBspike has been proposed that breaks the geographical boundary in assisting doctors in taking proper and fast decision regarding the treat ment of epileptic patient. The proposed system receives the EEG data fro m various users through internet and processes it for Epileptic Sp ike (ES) patterns present in it. It sends back a report to the user regarding the appearance of ES pattern present in the submitted EEG data. The average spike recognition rate obtained by the system with the test files, was 99.09% on an average.

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Epileptic seizure detection using dynamic wavelet network

Cited by 167 publications

References 49 publications

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

Swarm Negative Selection Algorithm for Electroencephalogram Signals Classification

WEBspike: A New Proposition of Deterministic Finite Automata and Parallel Algorithm Based Web Application for EEG Spike Recognition

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