Classification of EEG signals using neural network and logistic regression

Subaşı, Abdülhamit; Erçelebi, Ergün

doi:10.1016/j.cmpb.2004.10.009

Cited by 492 publications

(258 citation statements)

References 38 publications

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“…is a loss function and is a measure in the domain of the training data. Embedded methods use Equation ( 3.25) to minimize the ( ): 25) where [ ] measures the sparsity of the indicator , ̃ is a learner, and it could be any common classification algorithm. The function measures the performance of trained classifier ( ) on the training data ( ) for a given .…”

Section: Embedded Methodsmentioning

confidence: 99%

“…Lyapunov exponent-based features were fed into the classifier as inputs [24]. In [25], using wavelet coefficients (D3, D5 and A5) of EEG signals as an input of multilayer perceptron neural network, with the LevenbergMarquardt algorithm the sensitivity and specificity of 92.8% and 92.3% were obtained, respectively (60% of data for training and the rest 40% for testing). In this study, four channels of F7-C3, F8-C4, T5-O1, and T6-O2 were used.…”

Section: Eeg Application To Epilepsymentioning

confidence: 99%

See 1 more Smart Citation

Patient-specific epileptic seizure detection in long-term EEG recording in paediatric patients with intractable seizures

Zabihi

Kıranyaz

İnce

et al. 2013

IET Intelligent Signal Processing Conference 2013 (ISP 2013)

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Over recent years, due to the increase in the epileptic patient population, issues of diagnosing and treatment of epilepsy have become more and more prominent and much research has been done in this field in consequence. However, there are still many gaps and lack of knowledge in interpreting Electroencephalograph (EEG) signals in order to solve the problem.Particular problems in this area include difficulties in detecting the seizure events (due to the different seizure types and their variability from patient to patient or even in an individual over time), and dealing with long-term EEG recordings, which is an onerous and time consuming task for electroencephalographers.The thesis discusses the two problem areas using EEG data from four subjects with overall 21 hours of recording from the CHB-MIT scalp benchmark EEG dataset. We propose a patient specific seizure detection technique, which selects the optimal feature subsets, and train a dedicated classifier for each patient in order to maximize the classification performance. To exploit the characteristics of a patient's EEG pattern as much as possible, we used a large set of features in the proposed framework, namely time domain, frequency domain, time-frequency domain and nonlinear features, and selected the most crucial features among them by using Conditional Mutual Information Maximization (CMIM) technique. We further performed extensive comparative evaluations against 6 other feature selection methods to demonstrate the superiority of the CMIM.Support Vector Machine (SVM) with the linear kernel is used as the classifier. The experimental results show a delicate classification performance over the test set, i.e. an average of 90.62% sensitivity and 99.32% specificity are acquired when all channels and recordings are used to form a composite feature vector. In addition, an average sensitivity and specificity rates of 93.78% and 99.05% are obtained using CMIM, respectively. PREFACEThis master thesis was carried out in the MUVIS group at the signal processing department of Tampere University of Technology. It was funded by the Academy of Finland.I wish to express my sincere gratitude to Professor Serkan Kiranyaz, whose inspiring guidance and inexhaustible patience have helped me cultivate a systematic approach towards the research. I am indebted to him for the freedom he gave me in implementing my ideas. I have also acquired valuable insights through his instruction in academic approach.I am very much grateful to Professor Turker Ince for his warm support and his valuable suggestions. Besides, I would also like to thank Professor Moncef Gabbouj for providing me with the opportunity for the thesis work. He gave me a lot of trust and flexibility in the project.I have been extremely fortunate in having valuable friends who have made a homely atmosphere for me during my stay in Tampere

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Section: Embedded Methodsmentioning

confidence: 99%

Section: Eeg Application To Epilepsymentioning

confidence: 99%

Patient-specific epileptic seizure detection in long-term EEG recording in paediatric patients with intractable seizures

Zabihi

Kıranyaz

İnce

et al. 2013

IET Intelligent Signal Processing Conference 2013 (ISP 2013)

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“…For epileptic seizure electrocardiogram (ECG) classification, Subasi and Ercelebbi used lifting-based discrete wavelet transform as a preprocessing method to increase the computational speed. The results are compared with that obtained using first generation wavelets [16].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Lifting Transform for Classification of Hyperspectral Signatures

Agrawal¹,

Bawane²

2015

ARS

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Supervised classification of hyperspectral images is a challenging task because of the higher dimensionality of a pixel signature. The conventional classifiers require large training data set; however, practically limited numbers of labeled pixels are available due to complexity and cost involved in sample collection. It is essential to have a method that can reduce such higher dimensional data into lower dimensional feature space without the loss of useful information. For classification purpose, it will be useful if such a method takes into account the nature of the underlying signal when extracting lower dimensional feature vector. The lifting framework provides the required flexibility. This article proposes the adaptive lifting wavelet transform to extract the lower dimensional feature vectors for the classification of hyperspectral signatures. The proposed adaptive update step allows the decomposition filter to adapt to the input signal so as to retain the desired characteristics of the signal. A three-layer feed forward neural network is used as a supervised classifier to classify the extracted features. The effectiveness of the proposed method is tested on two hyperspectral data sets (HYDICE & ROSIS sensors). The performance of the proposed method is compared with first generation discrete wavelet transform (DWT) based feature extraction method and previous studies that use the same data. The indices used for measuring performance are overall classification accuracy and Kappa value. The experimental results show that the proposed adaptive lifting scheme (ALS) has excellent results with a small size training set.

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“…SVM [8] and LDA are two such classifiers that can be implemented to classify the EEG signal abnormalities. Stevenson N J [7] has developed a system named as Automated Grading System for EEG abnormality in neonates. Multiple linear discriminate classifiers are implemented to classify the EEG abnormality in neonates.…”

Section: Related Workmentioning

confidence: 99%