New Approach to Epileptic Diagnosis Using Visibility Graph of High-Frequency Signal

Tang, Xiaoying; Xia, Li; Liao, Yezi; Liu, Weifeng; Peng, Yungui; Gao, Tianxin; Zeng, Yanjun

doi:10.1177/1550059412464449

Cited by 36 publications

(19 citation statements)

References 15 publications

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“…Such methods were applied to the high frequency sub-band from depth electrode recordings in three patients with epilepsy, enabling identification of the seizure focus based on the electrode with a significantly different level of graph-index complexity. 17 Data from this study suggests that the seizure focus experiences an unusually high degree of complexity of its oscillatory patterns. However, the exceedingly small sample size of this study limits the generalizability of these findings, and further research is required to validate these preliminary findings.…”

Section: Lateralization and Localization Of Epilepsymentioning

confidence: 73%

Clinical correlates of graph theory findings in temporal lobe epilepsy

Haneef

Chiang

2014

Seizure

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Purpose Temporal lobe epilepsy (TLE) is considered a brain network disorder, additionally representing the most common form of pharmaco-resistant epilepsy in adults. There is increasing evidence that seizures in TLE arise from abnormal epileptogenic networks, which extend beyond the clinico-radiologically determined epileptogenic zone and may contribute to the failure rate of 30–50% following epilepsy surgery. Graph theory allows for a network-based representation of TLE brain networks using several neuroimaging and electrophysiologic modalities, and has potential to provide clinicians with clinically useful biomarkers for diagnostic and prognostic purposes. Methods We performed a review of the current state of graph theory findings in TLE as they pertain to localization of the epileptogenic zone, prediction of pre- and post-surgical seizure frequency and cognitive performance, and monitoring cognitive decline in TLE. Results Although different neuroimaging and electrophysiologic modalities have yielded occasionally conflicting results, several potential biomarkers have been characterized for identifying the epileptogenic zone, pre-/post-surgical seizure prediction, and assessing cognitive performance. For localization, graph theory measures of centrality have shown the most potential, including betweenness centrality, outdegree, and graph index complexity, whereas for prediction of seizure frequency, measures of synchronizability have shown the most potential. The utility of clustering coefficient and characteristic path length for assessing cognitive performance in TLE is also discussed. Conclusions Future studies integrating data from multiple modalities and testing predictive models are needed to clarify findings and develop graph theory for its clinical utility.

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Section: Lateralization and Localization Of Epilepsymentioning

confidence: 73%

Clinical correlates of graph theory findings in temporal lobe epilepsy

Haneef

Chiang

2014

Seizure

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“…Recently, VGs, which were first proposed by Lacasa et al [18] have been employed to analyze EEG signals [7], [19], [20]. VGs have also been employed by Shao [21] to study heartbeat interval signals and applied by Xiang et al [22] to analyze ECG signals.…”

mentioning

confidence: 99%

Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal

Zhu

Wen

2014

IEEE J. Biomed. Health Inform.

337

191

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The existing sleep stages classification methods are mainly based on time or frequency features. This paper classifies the sleep stages based on graph domain features from a single-channel electroencephalogram (EEG) signal. First, each epoch (30 s) EEG signal is mapped into a visibility graph (VG) and a horizontal VG (HVG). Second, a difference VG (DVG) is obtained by subtracting the edges set of the HVG from the edges set of the VG to extract essential degree sequences and to detect the gait-related movement artifact recordings. The mean degrees (MDs) and degree distributions (DDs) P (k) on HVGs and DVGs are analyzed epoch-by-epoch from 14,963 segments of EEG signals. Then, the MDs of each DVG and HVG and seven distinguishable DD values of P (k) from each DVG are extracted. Finally, nine extracted features are forwarded to a support vector machine to classify the sleep stages into two, three, four, five, and six states. The accuracy and kappa coefficients of six-state classification are 87.5% and 0.81, respectively. It was found that the MDs of the VGs on the deep sleep stage are higher than those on the awake and light sleep stages, and the MDs of the HVGs are just the reverse.

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“…The complexity and fractality (self-similarity) of a time series can be achieved by calculating its VG complexity which is similar to fractal dimension (FD) computation, without constructing the state space which requires a large number of sampling points. In this study, graph index complexity (GIC) methods are evaluated for measuring VG complexity (Lacasa et al 2008;Tang et al 2013;Ahmadlou et al 2010).…”

Section: Complexity Of Vgmentioning

confidence: 99%

“…Within this theoretical approach, there is a new simple method that converts a time series to a graph and it is called visibility graph (VG), with a structure which has been shown to be related to fractality (self-similarity) and complexity of the time series (Lacasa et al 2008). Since the quantification of complexity and self-similarity of a graph does not need many nodes in the graph when a time series is converted to a graph, then the computation of its complexity does not need many time samples (Tang et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Analysis of heart rate signals during meditation using visibility graph complexity

2018

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In the dynamics analysis of heart rate, the complexity of visibility graphs (VGs) is seen as a sign of short term variability in signals. The present study was conducted to investigate the possible impact of meditation on heart rate signals complexity using VG method. In this study, existing heart rate signals in Physionet database were used. The dynamics of the signals were then studied both before and during meditation by examining the complexity of VGs using graph index complexity (GIC). Generally, the obtained results showed that the heart rate signals were more complex during meditation. The simple process of calculating the GIC of VG and its adaptability to the chaotic nature of the biological signals can help in estimating the heart rate complexity in meditation.

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New Approach to Epileptic Diagnosis Using Visibility Graph of High-Frequency Signal

Cited by 36 publications

References 15 publications

Clinical correlates of graph theory findings in temporal lobe epilepsy

Clinical correlates of graph theory findings in temporal lobe epilepsy

Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal

Analysis of heart rate signals during meditation using visibility graph complexity

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