Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Scale-Dependent Lyapunov Exponent

Li, Qiong; Gao, Jianbo; Huang, Qi; Wu, Yuan; Xu, Bo

doi:10.3389/fbioe.2020.01006

Cited by 10 publications

(7 citation statements)

References 68 publications

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“…Based on the amplitude-integrated electroencephalography (aEEG) and compress spectrum array (CDSA) theory, Abend et al [ 31 ] performed the EEG identification of epileptic seizures; the sensitivity of identifying long-term epileptic discharges was 88%, whereas the sensitivity of identifying short-term epileptic discharges was 40% [ 31 ]. The researchers [ 32 ] then used the short-term index to classify EEG signals, revealing pathological brain electrical activity, and achieved 99.6% accuracy in the classification of healthy and pathological EEG. This means that EEG signatures can effectively identify disease EEG signals.…”

Section: Discussionmentioning

confidence: 99%

EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine

Liu

et al. 2022

Brain Sciences

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Epilepsy is one of the most serious nervous system diseases; it can be diagnosed accurately by video electroencephalogram. In this study, we analyzed microstate epileptic electroencephalogram (EEG) to aid in the diagnosis and identification of epilepsy. We recruited patients with focal epilepsy and healthy participants from the Third Xiangya Hospital and recorded their resting EEG data. In this study, the EEG data were analyzed by microstate analysis, and the support vector machine (SVM) classifier was used for automatic epileptic EEG classification based on features of the EEG microstate series, including microstate parameters (duration, occurrence, and coverage), linear features (median, second quartile, mean, kurtosis, and skewness) and non-linear features (Petrosian fractal dimension, approximate entropy, sample entropy, fuzzy entropy, and Lempel–Ziv complexity). In the gamma sub-band, the microstate parameters as a model were the best for interictal epilepsy recognition, with an accuracy of 87.18%, recall of 70.59%, and an area under the curve of 94.52%. There was a recognition effect of interictal epilepsy through the features extracted from the EEG microstate, which varied within the 4~45 Hz band with an accuracy of 79.55%. Based on the SVM classifier, microstate parameters and EEG features can be effectively used to classify epileptic EEG, and microstate parameters can better classify epileptic EEG compared with EEG features.

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Section: Discussionmentioning

confidence: 99%

EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine

Liu

et al. 2022

Brain Sciences

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“…SDLE was first introduced in [38,189], and has been further developed in [193,194] and applied to characterize EEG [143], HRV [195,196], financial time series [76], Earth's geodynamo [197], precipitation dynamics [198], sea clutter [199], THz imagery [200], and evaluate randomness [99]. As with the presentation of AFA, here, we will only present the key elements of the method; a concrete example of combining this method with a machine-learning approach (random forest) for distinguishing epileptiform discharges from normal electroencephalograms can be found in Li et al [201].…”

Section: Multiscale Analysis With the Scale-dependent Lyapunov Expone...mentioning

confidence: 99%

Complex Systems, Emergence, and Multiscale Analysis: A Tutorial and Brief Survey

Gao

2021

Applied Sciences

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Mankind has long been fascinated by emergence in complex systems. With the rapidly accumulating big data in almost every branch of science, engineering, and society, a golden age for the study of complex systems and emergence has arisen. Among the many values of big data are to detect changes in system dynamics and to help science to extend its reach, and most desirably, to possibly uncover new fundamental laws. Unfortunately, these goals are hard to achieve using black-box machine-learning based approaches for big data analysis. Especially, when systems are not functioning properly, their dynamics must be highly nonlinear, and as long as abnormal behaviors occur rarely, relevant data for abnormal behaviors cannot be expected to be abundant enough to be adequately tackled by machine-learning based approaches. To better cope with these situations, we advocate to synergistically use mainstream machine learning based approaches and multiscale approaches from complexity science. The latter are very useful for finding key parameters characterizing the evolution of a dynamical system, including malfunctioning of the system. One of the many uses of such parameters is to design simpler but more accurate unsupervised machine learning schemes. To illustrate the ideas, we will first provide a tutorial introduction to complex systems and emergence, then we present two multiscale approaches. One is based on adaptive filtering, which is excellent at trend analysis, noise reduction, and (multi)fractal analysis. The other originates from chaos theory and can unify the major complexity measures that have been developed in recent decades. To make the ideas and methods better accessed by a wider audience, the paper is designed as a tutorial survey, emphasizing the connections among the different concepts from complexity science. Many original discussions, arguments, and results pertinent to real-world applications are also presented so that readers can be best stimulated to apply and further develop the ideas and methods covered in the article to solve their own problems. This article is purported both as a tutorial and a survey. It can be used as course material, including summer extensive training courses. When the material is used for teaching purposes, it will be beneficial to motivate students to have hands-on experiences with the many methods discussed in the paper. Instructors as well as readers interested in the computer analysis programs are welcome to contact the corresponding author.

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“…By leveraging the sensitivity of the Recurrence Rate Gradient to subtle changes in recurrence behavior and the depth of analysis provided by the Recurrence Hurst, we enhance the diagnostic capabilities for neurodegenerative diseases, with the generated features being evaluated through a support vector machine (SVM). SVM and other machine-learning approaches have been proved to be promising in the computer-aided diagnosis of degenerative diseases [ 38 , 39 , 40 ]. This not only provides deeper insights into their neurodynamic processes but also underscores the potential of MTRRP and its metrics to be significant advancements in the field, offering a comprehensive tool for advancing medical diagnostics and research.…”

Section: Introductionmentioning

confidence: 99%

Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer’s Disease and Frontotemporal Dementia

Zheng,

Xiong,

Zhang

2024

Brain Sciences

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This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our approach, Recurrence Complexity, captures structural complexity by integrating local randomness and global structural features through the product of Recurrence Rate Gradient and Recurrence Hurst, both derived from MTRRP. We applied this technique to resting-state EEG data from patients diagnosed with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and age-matched healthy controls. The results revealed significantly higher recurrence complexity in the occipital areas of AD and FTD patients, particularly pronounced in the Alpha and Beta frequency bands. Furthermore, EEG features derived from MTRRP were evaluated using a Support Vector Machine with leave-one-out cross-validation, achieving a classification accuracy of 87.7%. These findings not only underscore the utility of MTRRP in detecting distinct neurophysiological patterns associated with neurodegenerative diseases but also highlight its broader applicability in time series analysis, providing a substantial tool for advancing medical diagnostics and research.

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Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Scale-Dependent Lyapunov Exponent

Cited by 10 publications

References 68 publications

EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine

EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine

Complex Systems, Emergence, and Multiscale Analysis: A Tutorial and Brief Survey

Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer’s Disease and Frontotemporal Dementia

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