A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform

Bhattacharyya, Abhijit; Pachori, Ram Bilas

doi:10.1109/tbme.2017.2650259

Cited by 348 publications

(175 citation statements)

References 51 publications

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“…The classification performance measurement metrics [51], namely accuracy (Acc), sensitivity (Sens) and specificity (Spec), are obtained using the ten-fold cross-validation method [51,52]. The ten-fold cross-validation method has been used widely to get unbiased performance of the classifier in the area of bio-medical signal processing [50,51].…”

Section: Classification Of Eeg Recordsmentioning

confidence: 99%

“…The ten-fold cross-validation method has been used widely to get unbiased performance of the classifier in the area of bio-medical signal processing [50,51]. All of the classification tasks along with wrapper-based feature selections have been performed using the WEKA machine learning toolbox (Weka 3.6.13, University of Waikato, Hamilton, New Zealand) [53].…”

Section: Classification Of Eeg Recordsmentioning

confidence: 99%

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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: Classification Of Eeg Recordsmentioning

confidence: 99%

Section: Classification Of Eeg Recordsmentioning

confidence: 99%

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

et al. 2017

Self Cite

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“…The classification accuracy with the linear kernel is highest for 20 features. The classifier is trained and tested using the ten-fold cross-validation method [54], which was recently used in several studies for the training and testing of the classifier [55][56][57]. The best classification performance of LS-SVM in terms of specificity, sensitivity and accuracy for different kernels is provided in Table 6.…”

Section: Resultsmentioning

confidence: 99%

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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“…More recently, as a specialised technique for WT, a multivariate empirical wavelet transform was proposed in [13] that builds signal adaptive wavelet based filters. The subband signals, called modes, have a tightly packed frequency support centered around a specific frequency.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Generating accurate results is of utmost importance in a medical diagnosis and it mustn't be compromised at any cost. Further, the WPD can be augmented by using empirical transforms as suggested in [13]. Combining the AR+WPD model with the feature ranking mechanism proposed in [12] would lead to a more relevant set of features for seizure prediction.…”

Section: Another Variable Is Defined As Followsmentioning

confidence: 99%

A Proposal to Automate Seizure Detection based on a Comparative Study of EEG Signal Analysis

Telang¹,

More²,

Modi³

et al. 2017

IJCA

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Epilepsy is a chronic neurological disorder which is characterized by recurrent and sudden seizures. People with epilepsy suffer from multiple types of seizures and Electroencephalography is an important clinical tool for diagnosing, monitoring and managing neurological disorders related to epilepsy. EEG signals are most often used to diagnose epilepsy, as seizures cause anomalies in EEG readings. In today's world where adult life expectancy is rising and humans are living longer than ever before, the healthcare system generates vast amounts of data, including EEG signals. This paper examines the prospects and challenges faced in utilizing this data in order to optimize seizure detection in order to improve the patients' quality of life. This paper also explores how Machine Learning can be applied to extract features and analyze the EEG signals and propose methods to achieve high classification accuracy.

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A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform

Abstract: The proposed method develops time-frequency plane for multivariate signals and builds patient-specific models for EEG seizure detection.

Cited by 348 publications

References 51 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

A Proposal to Automate Seizure Detection based on a Comparative Study of EEG Signal Analysis

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