ArticleBMC research notes2018
Topolnogical classifier for detecting the emergence of epileptic seizures.
Article in BMC research notes, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- Article
- Altered topological structure of the brain white matter in maltreated children through topological data analysis.Network neuroscience (Cambridge, Mass.) · 2024Article
- Fibromyalgia Detection Based on EEG Connectivity Patterns.Journal of clinical medicine · 2021Article
- Evaluating State Space Discovery by Persistent Cohomology in the Spatial Representation System.Frontiers in computational neuroscience · 2021Article
- Agent-Based Learning Model for the Obesity Paradox in RCC.Frontiers in bioengineering and biotechnology · 2021Article
- Article
- Article
- A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification.Frontiers in physiology · 2021Article
- Persistent Homology-Based Topological Analysis on the Gestalt Patterns during Human Brain Cognition Process.Journal of healthcare engineering · 2021Article
- Topological Data Analysis as a New Tool for EEG Processing.Frontiers in neuroscience · 2021Review
- Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals.Entropy (Basel, Switzerland) · 2020Article
- Machine learning models predicting multidrug resistant urinary tract infections using "DsaaS".BMC bioinformatics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveAn innovative method based on topological data analysis is introduced for classifying EEG recordings of patients affected by epilepsy. We construct a topological space from a collection of EEGs signals using Persistent Homology; then, we analyse the space by Persistent entropy, a global topological feature, in order to classify healthy and epileptic signals.
resultsThe performance of the resulting one-feature-based linear topological classifier is tested by analysing the Physionet dataset. The quality of classification is evaluated in terms of the Area Under Curve (AUC) of the receiver operating characteristic curve. It is shown that the linear topological classifier has an AUC equal to [Formula: see text] while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%.
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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.