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Proceedings ArticleDOI

Classification of normal, seizure and seizure-free EEG signals using EMD and EWT

01 Mar 2017-pp 360-366
TL;DR: The classification based on bandwidth features and least square support vector machine (LS-SVM) provided better categorization accuracy than earlier adopted methods.
Abstract: Objectives: Electroencephalogram (EEG) plays an important role in recording the activity of human brain. Identification of epileptic seizures can be done using EEG signals. Methods/ Statistical Analysis: In this work for classification of EEG signals a method known as Empirical mode decomposition (EMD) is used and compared with empirical wavelet transform (EWT) based method. Findings: In this paper the EMD has been considered for five classes of EEG signals. Intrinsic Mode functions obtained for these EEG signals have been shown. The amplitude modulation bandwidth B AM and frequency modulation bandwidth BFM have been calculated. Applications/ Improvements: The classification based on bandwidth features and least square support vector machine (LS-SVM) provided better categorization accuracy than earlier adopted methods. Results have been shown in this report.
Citations
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Journal ArticleDOI
TL;DR: The evaluated decomposition methods are promising approaches for seizure detection, but their use should be judiciously analysed, especially in situations that require real-time processing and computational power is an issue.

37 citations

Journal ArticleDOI
TL;DR: The proposed adaptive multi-parent crossover Genetic Algorithm (GA) for optimizing the features used in classifying epileptic seizures outperforms other approaches and achieved a high level of accuracy.

27 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used Hilbert vibration decomposition (HVD) on non-linear and non-stationary EEG signal to obtain multiple monocomponents varying in terms of amplitude and frequency.

9 citations

Posted ContentDOI
03 Jul 2019-bioRxiv
TL;DR: Three adaptive decomposition methods are evaluated for the classification of normal, ictal and inter-ictal EEG signals using a freely available database to provide a previously unavailable common methodology for comparing the performance of these methods for EEG seizure detection.
Abstract: Signal processing and machine learning methods are valuable tools in epilepsy research, potentially assisting in diagnosis, seizure detection, prediction and real-time event detection during long term monitoring. Recent approaches involve the decomposition of these signals in different modes or functions in a data-dependent and adaptive way. These approaches may provide advantages over commonly used Fourier based methods due to their ability to work with nonlinear and non-stationary data. In this work, three adaptive decomposition methods (Empirical Mode Decomposition, Empirical Wavelet Transform and Variational Mode Decomposition) are evaluated for the classification of normal, ictal and inter-ictal EEG signals using a freely available database. We provide a previously unavailable common methodology for comparing the performance of these methods for EEG seizure detection, with the use of the same classifiers, parameters and spectral and time domain features. It is shown that the outcomes using the three methods are quite similar, with maximum accuracies of 97.5% for Empirical Mode Decomposition, 96.7% for Empirical Wavelet Transform and 98.2% for Variational Mode Decomposition. Features were also extracted from the original non-decomposed signals, yielding inferior, but still fairly accurate (95.3%) results. The evaluated decomposition methods are promising approaches for seizure detection, but their use should be judiciously analysed, especially in situations that require real-time processing and computational power is an issue. An additional methodological contribution of this work is the development of two python packages, already available at the PyPI repository: One for the Empirical Wavelet Transform (ewtpy) and another for Variational Mode Decomposition (vmdpy).

5 citations

Book ChapterDOI
26 Sep 2020
TL;DR: In this article, a discriminative extension of Deep Multi-set Canonical Correlation Analysis (DMCCA) for seizure detection is proposed, where features extracted from different decomposed signals are combined by a joint optimization target of discriminant loss and multi-set canonical correlation loss.
Abstract: Due to the nonlinear and nonstationary properties in EEG signals, some seizure detection methods tried to decompose EEG signal into nonlinear and nonstationary components and use them for feature extraction. Seizure detection results showed a certain degree of improvement in these approaches. Based on this idea, more signal decomposition methods have been explored. Signal decomposition methods are designed according to different principles, which show different properties of signals. So, it can be more effective using features extracted from different signal decomposition methods. Based on this consideration, a novel method for seizure detection based on feature combination exploiting deep neural network is proposed in this paper. We introduced a discriminative extension of Deep Multi-set Canonical Correlation Analysis (DMCCA) for seizure detection. Features extracted from different decomposed signals are combined by a joint optimization target of discriminative loss and multi-set canonical correlation loss, which is both discriminative and canonical correlated. Preliminary experiments show the proposed method improves seizure detection results in terms of accuracy and AUC.

1 citations

References
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Book ChapterDOI

[...]

01 Jan 2012

139,059 citations

Journal ArticleDOI
TL;DR: In this research, discrete Daubechies and harmonic wavelets are investigated for analysis of epileptic EEG records and the capability of this mathematical microscope to analyze different scales of neural rhythms is shown to be a powerful tool for investigating small-scale oscillations of the brain signals.

1,077 citations

Journal ArticleDOI
01 Sep 2009
TL;DR: The suitability of the time-frequency ( t-f) analysis to classify EEG segments for epileptic seizures, and several methods for t- f analysis of EEGs are compared.
Abstract: The detection of recorded epileptic seizure activity in EEG segments is crucial for the localization and classification of epileptic seizures. However, since seizure evolution is typically a dynamic and nonstationary process and the signals are composed of multiple frequencies, visual and conventional frequency-based methods have limited application. In this paper, we demonstrate the suitability of the time-frequency ( t-f) analysis to classify EEG segments for epileptic seizures, and we compare several methods for t- f analysis of EEGs. Short-time Fourier transform and several t-f distributions are used to calculate the power spectrum density (PSD) of each segment. The analysis is performed in three stages: 1) t-f analysis and calculation of the PSD of each EEG segment; 2) feature extraction, measuring the signal segment fractional energy on specific t-f windows; and 3) classification of the EEG segment (existence of epileptic seizure or not), using artificial neural networks. The methods are evaluated using three classification problems obtained from a benchmark EEG dataset, and qualitative and quantitative results are presented.

658 citations

Journal ArticleDOI
TL;DR: The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system.

621 citations

Journal ArticleDOI
01 May 2007
TL;DR: ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks and it is shown that the overall accuracy values as high as 100% can be achieved by using the proposed systems.
Abstract: The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a time-consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn) as the input feature. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the ApEn drops sharply during an epileptic seizure and this fact is used in the proposed system. Two different types of neural networks, namely, Elman and probabilistic neural networks, are considered in this paper. ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks. It is shown that the overall accuracy values as high as 100% can be achieved by using the proposed system

542 citations