EEGraph: An open-source Python library for modeling electroencephalograms using graphs

Maitín, Ana M.; Nogales, Alberto; Chazarra, Pedro; García‐Tejedor, Álvaro J.

doi:10.1016/j.neucom.2022.11.050

Cited by 5 publications

(9 citation statements)

References 20 publications

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“…EEGraph: EEGraph is a Python library to model electroencephalograms (EEGs) as graphs, so the connectivity between different brain areas could be analyzed 116 .…”

Section: List Of Available Toolsmentioning

confidence: 99%

Overview of methods and available tools used in complex brain disorders

Ilias,

Doukas,

Kontoulis

et al. 2023

Open Res Europe

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Complex brain disorders, including Alzheimer’s dementia, sleep disorders, and epilepsy, are chronic conditions that have high prevalence individually and in combination, increasing mortality risk, and contributing to the socioeconomic burden of patients, their families and, their communities at large. Although some literature reviews have been conducted mentioning the available methods and tools used for supporting the diagnosis of complex brain disorders and processing different files, there are still limitations. Specifically, these research works have focused primarily on one single brain disorder, i.e., sleep disorders or dementia or epilepsy. Additionally, existing research initiatives mentioning some tools, focus mainly on one single type of data, i.e., electroencephalography (EEG) signals or actigraphies or Magnetic Resonance Imaging, and so on. To tackle the aforementioned limitations, this is the first study conducting a comprehensive literature review of the available methods used for supporting the diagnosis of multiple complex brain disorders, i.e., Alzheimer's dementia, sleep disorders, epilepsy. Also, to the best of our knowledge, we present the first study conducting a comprehensive literature review of all the available tools, which can be exploited for processing multiple types of data, including EEG, actigraphies, and MRIs, and receiving valuable forms of information which can be used for differentiating people in a healthy control group and patients suffering from complex brain disorders. Additionally, the present study highlights both the benefits and limitations of the existing available tools.

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“…EEGraph: EEGraph is a Python library to model electroencephalograms (EEGs) as graphs, so the connectivity between different brain areas could be analyzed 116 .…”

Section: List Of Available Toolsmentioning

confidence: 99%

Overview of methods and available tools used in complex brain disorders

Ilias,

Doukas,

Kontoulis

et al. 2023

Open Res Europe

View full text Add to dashboard Cite

show abstract

“…In vivo and in vitro human and animal data estimations informed this criterion. The level of consistency for this thresholding procedure was determined to ensure the retention of 30% of the most robust connections [29].…”

Section: Graph Theory Thresholdmentioning

confidence: 99%

“…A high CC value indicates a higher level of local connectivity within a network. This metric is valuable when it comes to assessing how nodes form distinct clusters or communities within a more extensive network structure [29].…”

Section: Graph Theory Thresholdmentioning

confidence: 99%

EEG-Based Functional Connectivity Analysis for Cognitive Impairment Classification

Echeverri-Ocampo,

Ardila,

Molina-Mateo

et al. 2023

Electronics

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Understanding how mild cognitive impairment affects global neural networks may explain changes in brain electrophysiology. Using graph theory and the visual oddball paradigm, we evaluated the functional connectivity of neuronal networks in brain lobes. The study involved 30 participants: 14 with mild cognitive impairment (MCI) and 16 healthy control (HC) participants. We conducted an examination using the visual oddball paradigm, focusing on electroencephalography signals with targeted stimuli. Our analysis employed functional connectivity utilizing the change point detection method. Additionally, we implemented training for linear discriminant analysis, K-nearest neighbor, and decision tree techniques to classify brain activity, distinguishing between subjects with mild cognitive impairment and those in the healthy control group. Our results demonstrate the efficacy of combining functional connectivity measurements derived from electroencephalography with machine learning for cognitive impairment classification. This research opens avenues for further exploration, including the potential for real-time detection of cognitive decline in complex real-world scenarios.

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“…There are numerous examples of modified ML and deep learning algorithms that were created into independent Python modules for usage in medical datasets. [10,11]. The open-source Python library EEGraph is applied by authors [10].…”

Section: Introductionmentioning

confidence: 99%

“…[10,11]. The open-source Python library EEGraph is applied by authors [10]. Another Python library is dicom_wsi [11].…”

Section: Introductionmentioning

confidence: 99%

A fast and effective approach for classification medical data sets

Ivanov

Toleva

Hooper

2023

ijirss

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This study's objective is to offer a practical computer method for handling classification problems on large datasets. The aim of this study is to offer a practical computer approach for handling classification tasks on big datasets. We show that using Python’s built-in parameters to balance classes can improve the accuracy and the metrics of a classification task. We employ logistic regression, support vector machines, decision trees, and random forest classifier. We use the parameter “class_weight='balanced'” to run each classification model as well as stratified train/test splitting to ensure that relative class frequencies are approximately preserved in each train and set subsets. We use our methods on medical datasets because class imbalance is frequently a problem there. Our research shows that the proposed algorithms can improve the accuracy and classification metrics of the given medical datasets. We propose an effective and easy-to-apply alternative to improve the prediction ability of the presented classification models in medical datasets. We test an easily reproducible set-up where any classification model can be used to model imbalanced classes. The key tuning of the model lies in the stratified train/test split as well as the parameter “class weight='balanced'”. By combination of parameter tuning, better classification performance can be obtained in a quick and simple manner. It is simple and quick to replicate our algorithms to examine various medical datasets and determine which model best fits the data. It can be reproduced in biostatistical laboratories and by medical companies. Because it is simple to comprehend, medical researchers can swiftly review the information and determine the best course of action.

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EEGraph: An open-source Python library for modeling electroencephalograms using graphs

Cited by 5 publications

References 20 publications

Overview of methods and available tools used in complex brain disorders

Overview of methods and available tools used in complex brain disorders

EEG-Based Functional Connectivity Analysis for Cognitive Impairment Classification

A fast and effective approach for classification medical data sets

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