Application of Entropy for Automated Detection of Neurological Disorders With Electroencephalogram Signals: A Review of the Last Decade (2012-2022)
Article
Jui, S. Janifer Jabin, Deo, Ravinesh C. Deo, Barua, Prabal Datta, Devi, Aruna, Soar, Jeffrey and Acharya, U. Rajendra. 2023. "Application of Entropy for Automated Detection of Neurological Disorders With Electroencephalogram Signals: A Review of the Last Decade (2012-2022)." IEEE Access. 11, pp. 71905-71924. https://doi.org/10.1109/ACCESS.2023.3294473
| Article Title | Application of Entropy for Automated Detection of Neurological Disorders With Electroencephalogram Signals: A Review of the Last Decade (2012-2022) |
|---|---|
| ERA Journal ID | 210567 |
| Article Category | Article |
| Authors | Jui, S. Janifer Jabin, Deo, Ravinesh C. Deo, Barua, Prabal Datta, Devi, Aruna, Soar, Jeffrey and Acharya, U. Rajendra |
| Journal Title | IEEE Access |
| Journal Citation | 11, pp. 71905-71924 |
| Number of Pages | 20 |
| Year | 2023 |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Place of Publication | United States |
| ISSN | 2169-3536 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2023.3294473 |
| Web Address (URL) | https://ieeexplore.ieee.org/document/10179861/authors#authors |
| Abstract | An automated Neurological Disorder detection system can be considered as a cost-effective and resource efficient tool for medical and healthcare applications. In automated Neurological Disorder detection, electroencephalograms are commonly used, but their low signal intensity and nonlinear features are difficult to analyze visually. A promising approach for processing of electroencephalogram signals is the concept of entropy, a nonlinear signal processing method to measure the chaos in the signal. The aim of this study was to find out the effective entropy measures and the machine learning approaches that produced promising output. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines as our method, we have identified 84 studies published between 2012 and 2022 that has investigated epilepsy, Parkinson’s disease, autism, Attention Deficit Hyperactive disorder, schizophrenia, Alzheimer’s disease, depression, and alcohol use disorder with machine learning approaches considering entropy measures. We show that Support Vector Machines was the most commonly used machine learning model, with consistent performance in most of the studies whereas sample entropy was the most commonly used entropy measure, followed by the approximate entropy. For epilepsy detection, the most used entropy feature was the log energy entropy, whereas the multi-scale entropy was commonly used for Alzheimer’s Disease, approximate and sample entropy used for Parkinson’s Disease, multi scale and Shannon entropy applied for autism, approximate and Shannon entropy used for attention deficit hyperactive disorder, sample entropy used for depression, approximate and spectral entropy adopted for schizophrenia, and the approximate and sample entropy employed for alcohol use disorder. According to the majority of the studies, there is growing concern about the increase in neuro patients and the heavy resource burden that is associated with their prevalence and diagnosis.... |
| Keywords | artificial intelligence; Neurological disorder; entropy; automated detection; EEG; machine learning |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Byline Affiliations | School of Mathematics, Physics and Computing |
| School of Business | |
| University of Technology Sydney | |
| Australian International Institute of Higher Education, Australia | |
| University of Sydney | |
| University of New England | |
| Taylor’s University, Malaysia | |
| SRM Institute of Science and Technology, India | |
| Kumamoto University, Japan | |
| University of the Sunshine Coast |
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| Application of Entropy for Automated Detection.pdf | ||
| License: CC BY-NC-ND 4.0 | ||
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