•Epilepsy can be detected using EEG signals.•The entropy indicates the complexity of the EEG signal.•Various entropies are used to diagnose epilepsy.•Unique ranges for various entropies are proposed.
...Epilepsy is the neurological disorder of the brain which is difficult to diagnose visually using Electroencephalogram (EEG) signals. Hence, an automated detection of epilepsy using EEG signals will be a useful tool in medical field. The automation of epilepsy detection using signal processing techniques such as wavelet transform and entropies may optimise the performance of the system. Many algorithms have been developed to diagnose the presence of seizure in the EEG signals. The entropy is a nonlinear parameter that reflects the complexity of the EEG signal. Many entropies have been used to differentiate normal, interictal and ictal EEG signals. This paper discusses various entropies used for an automated diagnosis of epilepsy using EEG signals. We have presented unique ranges for various entropies used to differentiate normal, interictal, and ictal EEG signals and also ranked them depending on the ability to discrimination ability of three classes. These entropies can be used to classify the different stages of epilepsy and can also be used for other biomedical applications.
Virtual dimensionality (VD) has received considerable interest in its use of specifying the number of spectrally distinct signatures present in hyperspectral data. Unfortunately, it never defines ...what such a signature is. For example, various targets of interest, such as anomalies and endmembers, should be considered as different types of spectrally distinct signatures and have their own different values of VD. Specifically, these targets are insignificant in terms of signal energies due to their relatively small populations. Accordingly, their contributions to second-order statistics (2OS) are rather limited. In this case, 2OS-based methods such as eigen-approaches to determine VD may not be effective in determining how many such type of signal sources as spectrally distinct signatures are. This paper develops a new theory that expands 2OS-VD theory to a high-order statistics (HOS)-based VD, called HOS-VD theory. Since there is no counterpart of the characteristic polynomial equation used to find eigenvalues in 2OS available for HOS, a direct extension is inapplicable. This paper re-invents a wheel by finding actual targets directly from the data rather than eigenvectors/singular vectors used in 2OS-VD theory which do not represent any real targets in the data. Consequently, comparing to 2OS-VD theory which can only be used to estimate the value of VD without finding real targets, the developed HOS-VD theory can accomplish both of tasks at the same time, i.e., determining the value of VD as well as finding actual targets directly from the data.
The paper provides the addenda to A. Kompa, Gnesioi filoi: the search for George Syncellus’ and Theophanes the Confessor’s own words, and the authorship of their oeuvre, Studia Ceranea 5, 2015, p. ...155–230. All the expressions crucial to the stylistic and stylometric argument on the authorship of the Chronography of Theophanes have been updated after 7 years and verified in the expanded TLG database. The updated results are presented below. The conclusions confirm the previous opinions on the individual, singular authorship of the chronicle of Theophanes with differences in style from the first part of the universal history, written by George Syncellus. At the same time, both works should be treated as a single project, and the prooimion to Theophanes’ part as a sound base faor the reconstruction of the writing process. The clauses ὡς προέφην, καθὼς καὶ προέφην, ὡς προέφημεν, and καθὼς προέφημεν are specific to the Chronography of Theophanes in their frequency and diversity, but they seem to be known and used by the circles from which Theophanes acquired his literary skills.
Herein we report the host-guest interactions of pagoda4arene with various α,ω-dibromoalkanes. The guest encapsulation properties were probed by 1H NMR titration and isothermal titration calorimetry ...(ITC) experiments. Unlike pillararenes, the deeper cavity of pagoda4arene shows higher affinity toward longer straight-chain molecules with 1:1 host-to-guest stochiometric ratio of complexation. In addition, the formation of the self-assembled supramolecular polymer driven by guest halogen-halogen interactions in solution was demonstrated by diffusion-ordered spectroscopy (DOSY), ITC, and dynamic light scattering (DLS) measurements. From the ITC dilution experiments, we found that the supramolecular polymer dissociation process is mainly entropically driven (TΔS = 64.00 kJ mol−1) and enthalpically disfavored (ΔH = 43.91 kJ mol−1).
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•Wavelet Packet Decomposition was superior to other signal decomposition methods.•The inclusion of higher frequency ranges improves the classification in BCI systems.•Shortcomings of wavelets have ...been compensated by higher order statistics features.•The proposed model may be used to enhance the current rehabilitation therapies.
In this study, three popular signal processing techniques (Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the decomposition of Electroencephalography (EEG) Signals in Brain Computer Interface (BCI) system for a classification task. Publicly available BCI competition III dataset IVa, a multichannel 2-class motor-imagery dataset, was used for this purpose. Multiscale Principal Component Analysis method was applied for the purpose of noise removal. In addition, different sets of features were formed to examine the effect of a particular group of features. The parameter selection process for signal decomposition methods was thoroughly explained as well. Our results show that the combination of Multiscale Principal Component Analysis de-noising and higher order statistics features extracted from wavelet packet decomposition sub-bands resulted in highest average classification accuracy of 92.8%. Our study is one among very few that provides a comprehensive comparison between signal decomposition methods in combination with higher order statistics in classification of BCI signals. In addition, we stressed the importance of higher frequency ranges in improving the classification task for EEG signals in Brain Computer Interface Systems. Obtained results indicate that the proposed model has the potential to obtain a reliable classification of motor imagery EEG signals, and can thus be used as a practical system for controlling a wheelchair. It can also further enhance the current rehabilitation therapies where appropriate feedback is delivered once the individual executes the correct movement. In that way, motor rehabilitation outcomes may improve over time.
Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. ...However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.
•In this study, new features were developed to improve the classification of PD.•The newly developed features (BH1-BH5), were based on higher order statistics.•Ensemble learning had better ...performance compared to other classification methods.•The alpha rhythm seems to be the most appropriate rhythm for classification of PD.
Parkinson’s disease (PD) is one of the most common neurodegenerative diseases and is generally associated with its signature symptoms of rest tremor, muscle rigidity and bradykinesia. Currently, PD is diagnosed by neurologists who focus on consider multiple factors to make their decision. Biomarkers such as electroencephalography (EEG) signals can be used for the classification of PD from healthy control (HC). These methods offer an objective approach and can act as an aid for neurologists in the PD diagnosis process. In this study, we introduce new higher order statistical (HOS) features of EEG signals derived from the alpha and beta rhythms and use them for classification of PD from HC using ensemble learning. This machine learning approach helps to improve the result of classification by combining multiple models and produces a better predictive performance compared to a single classification model. Our approach is able to achieve an average sensitivity of 99.28% with 99.10% specificity using the Bagged trees ensemble classifier. These results compared to previous studies conducted in this field demonstrate the importance of HOS and different rhythm features in background EEG analysis along with the superiority of ensemble classifiers for these types of applications compared to other machine learning and deep learning methods.
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