ArticleScientific reports2016
Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Correlation of ultrasound bladder vibrometry assessment of bladder compliance with urodynamic study results.PloS one · 2017Trial
- Topological-spectral fusion of secondary acoustic echoes for identity recognition in non-line-of-sight environments.Scientific reports · 2026Article
- Distinction of Chaos from Randomness Is Not Possible from the Degree Distribution of the Visibility and Phase Space Reconstruction Graphs.Entropy (Basel, Switzerland) · 2024Article
- Gershgorin circle theorem-based feature extraction for biomedical signal analysis.Frontiers in neuroinformatics · 2024Article
- Global nonlinear approach for mapping parameters of neural mass models.PLoS computational biology · 2023Article
- Visibility graph analysis for brain: scoping review.Frontiers in neuroscience · 2023Article
- Complex networks and deep learning for EEG signal analysis.Cognitive neurodynamics · 2021Review
- A Novel Measure Inspired by Lyapunov Exponents for the Characterization of Dynamics in State-Transition Networks.Entropy (Basel, Switzerland) · 2021Article
- Misc-GAN: A Multi-scale Generative Model for Graphs.Frontiers in big data · 2019Article
- Visibility Graph Feature Model of Vibration Signals: A Novel Bearing Fault Diagnosis Approach.Materials (Basel, Switzerland) · 2018Article
- Decoding cortical brain states from widefield calcium imaging data using visibility graph.Biomedical optics express · 2018Article
- Exact results of the limited penetrable horizontal visibility graph associated to random time series and its application.Scientific reports · 2018Article
- Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering.PloS one · 2018Article
- Forecasting the onset and course of mental illness with Twitter data.Scientific reports · 2017Article
- Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series.Scientific reports · 2017Article
- The Effects of Long-term Abacus Training on Topological Properties of Brain Functional Networks.Scientific reports · 2017Article
- A new mathematical model and experimental validation on foamy-oil flow in developing heavy oil reservoirs.Scientific reports · 2017Article
- On strongly connected networks with excitable-refractory dynamics and delayed coupling.Royal Society open science · 2017Article
- Utilization of a combined EEG/NIRS system to predict driver drowsiness.Scientific reports · 2017Article
- Dynamic properties of calcium-activated chloride currents in Xenopus laevis oocytes.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Visibility graph has established itself as a powerful tool for analyzing time series. We in this paper develop a novel multiscale limited penetrable horizontal visibility graph (MLPHVG). We use nonlinear time series from two typical complex systems, i.e., EEG signals and two-phase flow signals, to demonstrate the effectiveness of our method. Combining MLPHVG and support vector machine, we detect epileptic seizures from the EEG signals recorded from healthy subjects and epilepsy patients and the classification accuracy is 100%. In addition, we derive MLPHVGs from oil-water two-phase flow signals and find that the average clustering coefficient at different scales allows faithfully identifying and characterizing three typical oil-water flow patterns. These findings render our MLPHVG method particularly useful for analyzing nonlinear time series from the perspective of multiscale network analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.