Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks

Guo, Ling; Rivero, Daniel; Dorado, Julián; Rabuñal, Juan R.; Pazos, Alejandro

doi:10.1016/j.jneumeth.2010.05.020

Cited by 377 publications

(183 citation statements)

References 34 publications

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“…The dataset has been used by Guo et al (2010) to detect cases of epilepsy using neural networks. The variables used in this research are pre-processed using DWT.…”

Section: Data and Variablesmentioning

confidence: 99%

Random Forest Classification and Support Vector Machine for Detecting Epilepsyusing Electroencephalograph Records

Kuswanto¹,

Salamah²,

Fachruddin³

2017

American Journal of Applied Sciences

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Complexity in data structure has led to the rapid development of computational statistics methods. Machine learning approaches have been introduced and applied to solve complex problems in many fields. This paper applies two common machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), in the detection of epilepsy. The diagnosis of epilepsy can usually only be made when a seizure is happening, which leads to some difficulties in the diagnostic process. The most recent way of diagnosing epilepsy is by using an Electroencephalograph (EEG) record. However, detecting epilepsy cases through EEG records takes a long time and may lead to misleading diagnostic results. The use of machine learning approaches is intended to generate fast and accurate classification results. As the EEG only generates a signal, direct analysis using RF or SVM cannot be carried out and the EEG record needs to be pre-processed. This paper uses Discrete Wavelet Transform and Line Length Features in the data preprocessing stage to decompose the signal by frequency and time. The classification results show that both RF and SVM perform very well and are able to classify cases of epilepsy accurately. The RF outperforms the SVM in the training dataset, while the SVM has a better performance in testing, with almost nom is classified cases. Several open problems relating to interpretation as well as parameter settings are described.

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“…The dataset has been used by Guo et al (2010) to detect cases of epilepsy using neural networks. The variables used in this research are pre-processed using DWT.…”

Section: Data and Variablesmentioning

confidence: 99%

Random Forest Classification and Support Vector Machine for Detecting Epilepsyusing Electroencephalograph Records

Kuswanto¹,

Salamah²,

Fachruddin³

2017

American Journal of Applied Sciences

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“…For rhythmic discharges, fast Fourier transform based (Polat & Gunes, 2007, 2008a, 2008b, frequency domain Chua, Chandran, Acharya, & Lim, 2008;Gabor, 1998;Iscan, Dokur, & Tamer, 2011;Mousavi, Niknazar, & Vahdat, 2008;Murro et al, 1991;Nigam & Graupe, 2004;Sadati, Mohseni, & Magshoudi, 2006;Srinivasan, Eswaran, & Sriraam, 2005;Ubeyli, 2010a), time-frequency based (Martinez-Vargas, Avendano-Valencia, Giraldo, & Castellanos-Dominguez, 2011;Subasi & Gursoy, 2010;Tzallas, et al, 2007aTzallas, et al, , 2007bTzallas, et al, , 2009 or wavelet based features , 2007Guo, Rivero, Dorado, Rabunal, & Pazos, 2010;Guo, Rivero, Seoane, & Pazos, 2009;Kiymik, Subasi, & Ozcalik, 2004;Lima, Coelho, & Eisencraft, 2010;H. Ocak, 2008;H.…”

Section: Automated Epileptic Seizure Analysismentioning

confidence: 99%

“…Their results showed that the most discriminative features for neonatal seizure detection 1 are morphological based features, such as amplitude, shape and duration of waveforms. In addition, time domain features such as statistical features (Adjouadi et al, 2005), Hjorth's descriptors (Hjorth, 1970), nonlinear features (Kannathal, Acharya, Lim, & Sadasivan, 2005;McSharry, et al, 2002)-correlation dimension (Elger & Lehnertz, 1998), Lyapunov exponent Ubeyli, 2006;Ubeyli, 2010b) and other features obtained from convolution kernels (Adjouadi et al, 2004), eigenvector methods (Naghsh-Nilchi & Aghashahi, 2010 ; Ubeyli, 2008aUbeyli, , 2008bUbeyli, , 2009a, principal component analysis (PCA) (Ghosh-Dastidar, Adeli, & Dadmehr, 2008;Hesse & James, 2007;James & Hesse, 2005;Polat & Gunes, 2008a;Subasi & Gursoy, 2010), ICA (Hesse & James, 2007;James & Hesse, 2005;Subasi & Gursoy, 2010), crosscorrelation function (Chandaka, Chatterjee, & Munshi, 2009;Iscan, et al, 2011), and entropy (Guo, Rivero, Dorado, et al, 2010;Kannathal, Choo, Acharya, & Sadasivan, 2005;Liang, Wang, & Chang, 2010;Naghsh-Nilchi & Aghashahi, 2010 ;H. Ocak, 2009;Srinivasan, Eswaran, & Sriraam, 2007;Wang, et al, 2011) have been proposed to characterize the EEG signal.…”

Section: Automated Epileptic Seizure Analysismentioning

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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“…Medical data classification tasks are executed using different varieties of data types including text, signal, image, DNA, voice, etc. [1][2][3][4][5][6][7][8][9][10]. Some of the available literature [1][2][3][4][5][6] focus on medical data classification tasks for ailments such as diabetes, heart disease, hepatitis, Parkinson, liver, and cancer.…”

Section: Introductionmentioning

confidence: 99%

“…Some of the available literature [1][2][3][4][5][6] focus on medical data classification tasks for ailments such as diabetes, heart disease, hepatitis, Parkinson, liver, and cancer. Similarly, EEG and ECG signals are usually used in diagnosing other diseases such as epileptic seizure, schizophrenia, Alzheimer, asthma, and arrhythmia [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Evidence Accumulation Clustering with Possibilitic Fuzzy C-Means base clustering approach to disease diagnosis

Iliyasu

Fatichah

2016

Automatika

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Original scientific paperTraditionally, supervised machine learning methods are the first choice for tasks involving classification of data. This study provides a non-conventional hybrid alternative technique (pEAC) that blends the Possibilistic Fuzzy CMeans (PFCM) as base cluster generating algorithm into the 'standard' Evidence Accumulation Clustering (EAC) clustering method. The PFCM coalesces the separate properties of the Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) algorithms into a sophisticated clustering algorithm. Notwithstanding the tremendous capabilities offered by this hybrid technique, in terms of structure, it resembles the hEAC and fEAC ensemble clustering techniques that are realised by integrating the K-Means and FCM clustering algorithms into the EAC technique. To validate the new technique's effectiveness, its performance on both synthetic and real medical datasets was evaluated alongside individual runs of well-known clustering methods, other unsupervised ensemble clustering techniques and some supervised machine learning methods. Our results show that the proposed pEAC technique outperformed the individual runs of the clustering methods and other unsupervised ensemble techniques in terms accuracy for the diagnosis of hepatitis, cardiovascular, breast cancer, and diabetes ailments that were used in the experiments. Remarkably, compared alongside selected supervised machine learning classification models, our proposed pEAC ensemble technique exhibits better diagnosing accuracy for the two breast cancer datasets that were used, which suggests that even at the cost of none labelling of data, the proposed technique offers efficient medical data classification.Key words: Evidence accumulation clustering, K-means, fuzzy C-means, possibilitic fuzzy C-means, hybrid intelligent systems, health informatics, medical data classification, disease diagnosis.Grupiranje zasnovano na skupljanju dokaza s vjerojatnosno-neizrazitim C-means pristupom za dijagnozu bolesti. Tradicionalno, metode nadziranog strojnog učenja predstavljaju prvi izbor za zadatke koji uključuju klasifikaciju podataka. Ovo istraživanje prikazuje nekonvencionalnu hibridnu alternativnu (pEAC) tehniku koja kombinira vjerojatnosno-neizraziti C-Means (PFCM) kao osnovni algoritam grupiranja u standardno grupiranje korištenjem grupiranja zasnovanog na skupljanju dokaza (EAC). PFCM objedinjuje zasebna svojstva vjerojatnosnog C-Means (PCM) i neizrazitog C-Means (FCM) algoritama u sofisticirani algoritam grupiranja. Usprkos ogromnim mogućnostima koje nudi ova tehnika, u smislu strukture, ona je nalik cjelovitim hEAC i fEAC tehnikama grupiranja realiziranim integracijom K-Means i FCM algoritama grupiranja u EAC tehniku. Kako bi se validirala učinkovi-tost, njeno ponašanje je ispitano na sintetičkim i stvarnim medicinskim podacima te su provedene usporedbe s pojedinačnim široko rasprostranjenim metodama, drugim nenadziranim tehnikama grupiranja i nekim nadziranim metodama učenja. Rezultat prikazuje kako predložena pEAC tehnika nadmašuje poje...

show abstract

Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks

Cited by 377 publications

References 34 publications

Random Forest Classification and Support Vector Machine for Detecting Epilepsyusing Electroencephalograph Records

Random Forest Classification and Support Vector Machine for Detecting Epilepsyusing Electroencephalograph Records

Automated Epileptic Seizure Detection Methods: A Review Study

Evidence Accumulation Clustering with Possibilitic Fuzzy C-Means base clustering approach to disease diagnosis

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