Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform

Gupta, Vipin; Priya, T.S.Krishna; Yadav, Abhishek; Pachori, Ram Bilas; Acharya, U. Rajendra

doi:10.1016/j.patrec.2017.03.017

Cited by 110 publications

(43 citation statements)

References 32 publications

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“…The selection procedure of J is given in Section 3. FAWT has been utilized for detecting the CAD in [31,32], in order to diagnose CHF in [33], to identify electroencephalogram (EEG) signals of focal and non-focal classes in [34], and for the faults identification in rotating machinery [21]. We have used the Matlab toolbox (İ Bayram,İstanbul Technical University,İstanbul, Turkey) available for FAWT implementation at [35].…”

Section: Computation Of Features In Fawt Frameworkmentioning

confidence: 99%

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Kumar

Pachori

Acharya

2017

Entropy

119

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Myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. It expands rapidly and, if not treated timely, continues to damage the heart muscles. An electrocardiogram (ECG) is generally used by the clinicians to diagnose the MI patients. Manual identification of the changes introduced by MI is a time-consuming and tedious task, and there is also a possibility of misinterpretation of the changes in the ECG. Therefore, a method for automatic diagnosis of MI using ECG beat with flexible analytic wavelet transform (FAWT) method is proposed in this work. First, the segmentation of ECG signals into beats is performed. Then, FAWT is applied to each ECG beat, which decomposes them into subband signals. Sample entropy (SEnt) is computed from these subband signals and fed to the random forest (RF), J48 decision tree, back propagation neural network (BPNN), and least-squares support vector machine (LS-SVM) classifiers to choose the highest performing one. We have achieved highest classification accuracy of 99.31% using LS-SVM classifier. We have also incorporated Wilcoxon and Bhattacharya ranking methods and observed no improvement in the performance. The proposed automated method can be installed in the intensive care units (ICUs) of hospitals to aid the clinicians in confirming their diagnosis.

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Section: Computation Of Features In Fawt Frameworkmentioning

confidence: 99%

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Kumar

Pachori

Acharya

2017

Entropy

119

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“…e SVM was used as a pattern classi er to achieve identifying the focal and nonfocal EEG signals. Similarly, Gupta et al also presented a framework for entropy-based pattern learning based on exible analytic wavelet transform (FAWT) to detect the focal EEG signals [29]. e EEG signals were rst represented as 15 levels of FAWT.…”

Section: Studymentioning

confidence: 99%

“…(iii) irdly, how to deal with physiological signals in the interest of e ectively extracting entropy measures from them has been becoming one of the most key factors that determine the performance on entropy-based pattern learning tasks. e existing studies have shown that using the entropy-based pattern learning for assessment of physiological signals, the feature extraction of entropy measures depends heavily on the decomposition and representation methods of physiological signals [27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals

Wang

et al. 2020

Complexity

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Measures of predictability in physiological signals based on entropy metrics have been widely used in the application domain of medical assessment and clinical diagnosis. In this paper, we propose a new entropy-based pattern learning by a combination of singular spectrum analysis (SSA) and entropy measures for assessment of physiological signals. Physiological signals are first represented as a series of SSA components, and then well-established entropy measures are extracted from the resulting SSA components that can help to facilitate the features extraction from physiological signals. The entropy measures of notable SSA components are used to form input features and fed into pattern classifier. To demonstrate its validity, applicability, and versatility, the proposed entropy-based pattern learning is used to perform medical assessments with three kinds of classical physiological signals, that is, electroencephalogram (EEG), electromyogram (EMG), and RR-interval signals. Experiments demonstrate that in all cases, the proposed entropy-based pattern learning can effectively capture specific biosignal patterns of physiological signals and achieve excellent identification performances for the assessments of EEG, EMG, and RR-interval signals. Besides, through the comparison of the identification performances for entropy-based pattern learning based on the physiological signals themselves and the SSA components, it is concluded that the discriminating power of entropy-based pattern learning based on the SSA components is much stronger than that based on the physiological signals themselves. Since it can be easily extended to any other physiological signal analysis, the proposed entropy-based pattern learning may use as an efficient approach to reveal biosignal patterns for medical assessment of physiological signals.

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“…Earlier research by Hans Berger has shown that there are frequency bands highly connected with the activity of the brain [2]. These frequency bands were named delta (< 4 Hz), theta (4-7 Hz), alpha (8)(9)(10)(11)(12)(13)(14)(15), beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31), and gamma (> 32 Hz). The original EEG signal is the compilation of all neuron's activities.…”

Section: Introductionmentioning

confidence: 99%

PSR-based research of feature extraction from one-second EEG signals: a neural network study

Dawid

2019

SN Appl. Sci.

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The speed and accuracy of signal classification are the most valuable parameters to create real-time systems for interaction between the brain and the computer system. In this work, we propose a schema of the extraction of features from one-second electroencephalographic (EEG) signals generated by facial muscle stress. We have tested here three sorts of EEG signals. The signals originate from different facial expressions. The phase-space reconstruction (PSR) method has been used to convert EEG signals from these three classes of facial muscle tension. For further processing, the data has been converted into a two-dimensional (2D) matrix and saved in the form of color images. The 2D convolutional neural network (CNN) served to determine the accuracy of the classifications of the previously unknown PSR generated images from the EEG signals. We have witnessed an improvement in the accuracy of the signal classification in the phase-space representation. We have found that the CNN network better classifies colored trajectories in the 2D phase-space graph. At the end of this work, we compared our results with the results obtained by a one-dimensional convolution neural network.

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Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform

Cited by 110 publications

References 32 publications

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals

PSR-based research of feature extraction from one-second EEG signals: a neural network study

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