Automated Diagnosis of Epilepsy Using Key-Point-Based Local Binary Pattern of EEG Signals

Tiwari, Ashwani Kumar; Pachori, Ram Bilas; Kanhangad, Vivek; Panigrahi, Bijaya Ketan

doi:10.1109/jbhi.2016.2589971

Cited by 188 publications

(74 citation statements)

References 48 publications

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“…In [15], the authors have developed a method based on the LBP of the Gabor filter-decomposed EEG signals followed by the nearest neighbor classifier and classified seizure-free and seizure EEG signals with a classification accuracy of 98.33%. In a recent study [16], key-point-based LBP has been used for the classification of seizure EEG signals. The authors obtained classification accuracy of 98.80% with a standard deviation (SD) of 0.11% in classifying seizure, seizure-free and normal groups of EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

et al. 2017

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This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high-and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

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Section: Introductionmentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

et al. 2017

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“…BNDB includes seizure, nonseizure, and normal EEG signals, and we utilized this database for the validation of our method. Other studies that have validated their algorithms using BNDB are summarized in Table . Sharma et al proposed the time‐frequency flexible wavelet transform and fractal dimension, and the performance of their method was only approximately 0.03% higher than that of our method in the classification between ZO and S classes.…”

Section: Discussionmentioning

confidence: 79%

“…However, their overall average performance was 99.34% (ZS, OS, NS, FS, ZO‐S, NF‐S, and ZONF‐S), which is slightly lower than that of our method (approximately 0.42%). Toward et al utilized key‐points based on local binary patterns (LBP) with SVM, and they validated the performance of ZO‐S, NF‐S, and ZONF. The average performance was higher than that of our method, but the difference is only 0.1%.…”

Section: Discussionmentioning

confidence: 99%

“…Automatic computer‐based methods for seizure detection have been utilized since the early 1970s . Traditionally, a number of methods based on principal component analysis (PCA) have been used, such as the wavelet transform‐based method , key points based on local binary patterns , empirical mode decomposition , and zero‐crossing . Among these, wavelet transform (WT) can be utilized in seizure detection to accurately discriminate features from subbands to use for seizure classification .…”

Section: Introductionmentioning

confidence: 99%

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Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals

Lee

Ryu²,

Kim

2020

ETRI Journal

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Long‐term electroencephalography (EEG) monitoring is time‐consuming, and requires experts to interpret EEG signals to detect seizures in patients. In this paper, we propose a novel automated method called adaptive slope of wavelet coefficient counts over various thresholds (ASCOT) to classify patient episodes as seizure waveforms. ASCOT involves extracting the feature matrix by calculating the mean slope of wavelet coefficient counts over various thresholds in each frequency subband. We validated our method using our own database and a public database to avoid overtuning. The experimental results show that the proposed method achieved a reliable and promising accuracy in both our own database (98.93%) and the public database (99.78%). Finally, we evaluated the performance of the method considering various window sizes. In conclusion, the proposed method achieved a reliable seizure detection performance with a short‐term window size. Therefore, our method can be utilized to interpret long‐term EEG results and detect momentary seizure waveforms in diagnostic systems.

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“…The histogram of the LBP image is generally used for texture classification. In the area of medical image analysis, LBP methods have been successfully used in characterizing disease patterns [16][17][18] and automated diagnosis [19]. Local binary patterns have also been used for analyzing histopathological images and detecting mitotic cells [20,21].…”

Section: Lbp Computationmentioning

confidence: 99%

Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring

Mukundan

2018

J. Imaging

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This paper presents novel feature descriptors and classification algorithms for the automated scoring of HER2 in Whole Slide Images (WSI) of breast cancer histology slides. Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level and to use simple, yet robust feature descriptors that can provide accurate classification of the slides. We propose two types of feature descriptors that encode important information about staining patterns and the percentage of staining present in ImmunoHistoChemistry (IHC)-stained slides. The first descriptor is called a characteristic curve, which is a smooth non-increasing curve that represents the variation of percentage of staining with saturation levels. The second new descriptor introduced in this paper is a local binary pattern (LBP) feature curve, which is also a non-increasing smooth curve that represents the local texture of the staining patterns. Both descriptors show excellent interclass variance and intraclass correlation and are suitable for the design of automatic HER2 classification algorithms. This paper gives the detailed theoretical aspects of the feature descriptors and also provides experimental results and a comparative analysis.

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Automated Diagnosis of Epilepsy Using Key-Point-Based Local Binary Pattern of EEG Signals

Cited by 188 publications

References 48 publications

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals

Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring

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