Time–frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification

Bhati, Dinesh; Sharma, Manish; Pachori, Ram Bilas; Gadre, Vikram M.

doi:10.1016/j.dsp.2016.12.004

Cited by 121 publications

(43 citation statements)

References 59 publications

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“…The advantage of the proposed method is that it can discriminate the EEG classes by optimal selection of the Q and R parameters. It should be noted that intracranial seizure (dataset S) and seizure-free (datasets F and N) EEG recordings are clean signals due to their way of recording, thus having a high signal to noise ratio [63]. The seizure and seizure-free EEG signals from scalp EEG recordings may be more susceptible to noise, thus requiring an additional noise reduction process [64] before the feature extraction step to improve the classification performance.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

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

et al. 2017

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“…We have also computed p-values using the Kruskal-Wallis test [50] to check the statistical significance of the features in order to discriminate normal and CHF HRV signals. This test recently has been used to check the discrimination ability of the features for analyzing the epileptic Electroencephalogram (EEG) signals [51][52][53]. We can observe in Table 4 that p-values are significantly low (p-value < 0.05) for all of the features.…”

Section: Resultsmentioning

confidence: 94%

Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals

Kumar

Pachori

Acharya

2017

Entropy

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Abstract:In the present work, an automated method to diagnose Congestive Heart Failure (CHF) using Heart Rate Variability (HRV) signals is proposed. This method is based on Flexible Analytic Wavelet Transform (FAWT), which decomposes the HRV signals into different sub-band signals. Further, Accumulated Fuzzy Entropy (AFEnt) and Accumulated Permutation Entropy (APEnt) are computed over cumulative sums of these sub-band signals. This provides complexity analysis using fuzzy and permutation entropies at different frequency scales. We have extracted 20 features from these signals obtained at different frequency scales of HRV signals. The Bhattacharyya ranking method is used to rank the extracted features from the HRV signals of three different lengths (500, 1000 and 2000 samples). These ranked features are fed to the Least Squares Support Vector Machine (LS-SVM) classifier. Our proposed system has obtained a sensitivity of 98.07%, specificity of 98.33% and accuracy of 98.21% for the 500-sample length of HRV signals. Our system yielded a sensitivity of 97.95%, specificity of 98.07% and accuracy of 98.01% for HRV signals of a length of 1000 samples and a sensitivity of 97.76%, specificity of 97.67% and accuracy of 97.71% for signals corresponding to the 2000-sample length of HRV signals. Our automated system can aid clinicians in the accurate detection of CHF using HRV signals. It can be installed in hospitals, polyclinics and remote villages where there is no access to cardiologists.

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“…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%

“…Kumar et al proposed a scheme based on a discrete wavelet transform (DWT) analysis and the approximate entropy using an artificial neural network. Sharma et al proposed a new approach based on the analytic time–frequency flexible wavelet transform and fractal dimension, and Bhati et al designed a localized time–frequency three‐band biorthogonal linear phase wavelet filter bank. Furthermore, Ocak proposed a new scheme based on the approximate entropy and a discrete wavelet transform analysis.…”

Section: Introductionmentioning

confidence: 99%

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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Time–frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification

Cited by 121 publications

References 59 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

Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV 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

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