Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier

Raghu, S.; Sriraam, N.; Temel, Yasin; Rao, Shyam Vasudeva; Hegde, Alangar S.; Kubben, Pieter L.

doi:10.1016/j.compbiomed.2019.05.016

Cited by 86 publications

(31 citation statements)

References 63 publications

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“…Based on the above discussions, our RR-DTDWT based detector is more suitable for realistic application scenarios compared with those listed in Table 6. Raghu et al [57] also proposed an automated epileptic seizure detection method based on DWT and complexity measure via sigmoid entropy, achieving a sensitivity of 94.21% and an accuracy of 94.38%, which is less effective than our method. Previous studies such as [35] evaluated the performance of their method using both Bonn and CHB-MIT databases.…”

Section: Comparison With Previous Studies Based On Bonn Databasementioning

confidence: 76%

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Jiang

Zhang

et al. 2019

Applied Sciences

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With the development of pervasive sensing and machine learning technologies, automated epileptic seizure detection based on electroencephalogram (EEG) signals has provided tremendous support for the lives of epileptic patients. Discrete wavelet transform (DWT) is an effective method for time-frequency analysis of EEG and has been used for seizure detection in daily healthcare monitoring systems. However, the shift variance, the lack of directionality and the substantial aliasing, limit the effects of DWT in some applications. Dual-tree discrete wavelet transform (DTDWT) can overcome those drawbacks but may increase information redundancy. For classification tasks with small dataset sizes, such redundancy can greatly reduce learning efficiency and model performance. In this work, we proposed a novel redundancy removed DTDWT (RR-DTDWT) framework for automated seizure detection. Energy and modified multi-scale entropy (MMSE) features in a dual tree wavelet domain were extracted to construct a complete picture of mental states. To the best of our knowledge, this is the first study to employ MMSE as an indicator of epileptic seizures. Moreover, a compact EEG representation can be obtained after removing useless information redundancy (redundancy between wavelet trees, adjacent channels and entropy scales) by a general auto-weighted feature selection framework via global redundancy minimization (AGRM). Through validation on Bonn and CHB-MIT databases, the proposed RR-DTDWT method can achieve better performance than previous studies.

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Section: Comparison With Previous Studies Based On Bonn Databasementioning

confidence: 76%

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Jiang

Zhang

et al. 2019

Applied Sciences

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“…Weighted multiscale Renyi permutation entropy (WMRPE), weighted permutation entropy (WPE), fuzzy entropy (FuzzyEn), a sigmoid entropy, approximate entropy (ApEn) based methods have also been frequently applied to this problem [13][14][15]. Additionally, nonlinear parameters such as fractal dimension, scaling exponent obtained with detrended fluctuation analysis (DFA), Hurst's exponent have been utilized in many studies and successful results have been obtained for the detection and classification of seizure and seizure-free epileptic EEG signals [16,17].…”

Section: Related Studiesmentioning

confidence: 99%

“…In the EMD-and EEMD-based approaches a total of 320 × 6 size, and DWT -based approach a total of 320 × 8 size feature sets were obtained. Applying the same (15) procedure to the EEG signal itself, a total of 320 × 2 size feature set for pre-seizure and seizure EEG data was obtained.…”

Section: Feature Extractionmentioning

confidence: 99%

Epileptic seizure classifications using empirical mode decomposition and its derivative

et al. 2020

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Background: Epilepsy is one of the most common neurological disorders associated with disruption of brain activity. In the classification and detection of epileptic seizures, electroencephalography (EEG) measurements, which record the electrical activities of the brain, are frequently used. Empirical mode decomposition (EMD) and its derivative, ensemble EMD (EEMD) are recently developed methods used to decompose nonstationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). Our main objective in this study is to present a hybrid IMF selection method combining four different approaches (energy, correlation, power spectral distance, and statistical significance measures), and investigate the effect of selected IMFs extracted by EMD and EEMD on the classification. We have applied the proposed IMF selection approach on the classification of EEG signals recorded from epilepsy patients who are under treatment at our collaborator hospital. Multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then IMF selection was performed. Finally, time-and spectral-domain, and nonlinear features are extracted and feature sets are created for the classification. Results: The maximum classification accuracies obtained using various combinations of IMFs were 94.56%, 95.63%, 96.8%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression classifiers, respectively, by using EMD analysis; whereas, the EEMD approach has provided maximum classification accuracies of 96.06%, 97%, 97%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression, respectively. Classification performance with the same features obtained using direct EEG signals instead of the decomposed IMFs was worse than the aforementioned 2 approaches for every combination. Conclusion: Simulation results demonstrate that the proposed IMF selection approach affects the classification results. Also, EEMD provides a robust method for feature extraction from EEG signals in order to classify pre-seizure and seizure segments.

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“…Also, entropy-based features such as fuzzy entropy (FuzzyEn), and sample entropy (SampEn) [10], sigmoid entropy [11], approximate entropy (ApEn) [12], weighted Permutation Entropy (WPE) [13], have also been commonly utilized to detect and predict epileptic seizures.…”

Section: Introductionmentioning

confidence: 99%

Epileptic EEG Classification by Using Advanced Signal Decomposition Methods

Cura¹,

Akan²

2021

Epilepsy - Update on Classification, Etiologies, Instrumental Diagnosis and Treatment

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Electroencephalography (EEG) signals are frequently used for the detection of epileptic seizures. In this chapter, advanced signal analysis methods such as Empirical Mode Decomposition (EMD), Ensembe (EMD), Dynamic mode decomposition (DMD), and Synchrosqueezing Transform (SST) are utilized to classify epileptic EEG signals. EMD and its derivative, EEMD are recently developed methods used to decompose nonstationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). In this study multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then essential IMFs are selected. Finally, time-and spectral-domain, and nonlinear features are extracted from selected IMFs and classified. DMD is a new matrix decomposition method proposed as an iterative solution to problems in fluid flow analysis. We present single-channel, and multi-channel EEG based DMD approaches for the analysis of epileptic EEG signals. As a third method, we use the SST representations of seizure and pre-seizure EEG data. Various features are calculated and classified by Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naive Bayes (NB), Logistic Regression (LR), Boosted Trees (BT), and Subspace kNN (S-kNN) to detect pre-seizure and seizure signals. Simulation results demonstrate that the proposed approaches achieve outstanding validation accuracy rates.

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Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier

Cited by 86 publications

References 63 publications

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Epileptic seizure classifications using empirical mode decomposition and its derivative

Epileptic EEG Classification by Using Advanced Signal Decomposition Methods

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