Empirical Fourier decomposition-based alcoholism detection using biomedical signals: A neuro-scientific approach.
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| Title: | Empirical Fourier decomposition-based alcoholism detection using biomedical signals: A neuro-scientific approach. |
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| Authors: | Bhuvaneshwari, M1 (AUTHOR) bhvnshwari@gmail.com, Grace Mary Kanaga, E1 (AUTHOR) grace@karunya.edu |
| Source: | Sādhanā: Academy Proceedings in Engineering Sciences. Jun2026, Vol. 51 Issue 2, p1-13. 13p. |
| Subjects: | Biomedical signal processing, Alcoholism, Feature extraction, Neurophysiology, Optimization algorithms, Electrophysiology, Machine learning |
| Abstract: | Early detection of alcoholism allows for early intervention and treatment that increases the chances of successful treatment and recovery. The longer the alcoholism goes undetected, the greater the risk of developing numerous health risks, including liver disease, cardiovascular problems, gastrointestinal issues, neurological damage, and mental health disorders. Detecting alcoholism enables healthcare professionals to address these risks promptly and provide appropriate medical interventions and support. This article proposes a novel method combining the empirical wavelet transform and the Fourier decomposition method for automated detection of alcoholism using electroencephalogram (EEG) signals. The new method, called the empirical Fourier decomposition (EFD) method, which combines the improved Fourier segmentation technique and zero phase filter bank concepts. The EFD method is applied to decompose the EEG signals into sub-band wave components. Features such as mean, standard deviation, kurtosis, line length, Hjorth parameters, log energy entropy, and norm energy entropy are extracted from each component. To select the optimal features, the horse-herd optimization algorithm (HHOA) is employed to identify the best feature subset. The experiments have been carried out using classifiers, such as least squares support vector machine (LS-SVM), random forest (RF), and k-nearest neighbor (k-NN). The proposed approach provides average accuracies of 99.01% with RF, 98.36% with LS-SVM, and 98.14% with the k-NN classifiers. The experimental results show that the proposed method outperforms the existing methods and can be adopted for real-time alcoholism detection. The decomposition algorithm presented demonstrates enhanced performance across both standard and acquired datasets, indicating its robustness and generalizability in alcoholism detection. [ABSTRACT FROM AUTHOR] |
| Copyright of Sādhanā: Academy Proceedings in Engineering Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193197862 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Empirical Fourier decomposition-based alcoholism detection using biomedical signals: A neuro-scientific approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bhuvaneshwari%2C+M%22">Bhuvaneshwari, M</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bhvnshwari@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Grace+Mary+Kanaga%2C+E%22">Grace Mary Kanaga, E</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> grace@karunya.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Sādhanā%3A+Academy+Proceedings+in+Engineering+Sciences%22">Sādhanā: Academy Proceedings in Engineering Sciences</searchLink>. Jun2026, Vol. 51 Issue 2, p1-13. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Biomedical+signal+processing%22">Biomedical signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Alcoholism%22">Alcoholism</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Neurophysiology%22">Neurophysiology</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Electrophysiology%22">Electrophysiology</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Early detection of alcoholism allows for early intervention and treatment that increases the chances of successful treatment and recovery. The longer the alcoholism goes undetected, the greater the risk of developing numerous health risks, including liver disease, cardiovascular problems, gastrointestinal issues, neurological damage, and mental health disorders. Detecting alcoholism enables healthcare professionals to address these risks promptly and provide appropriate medical interventions and support. This article proposes a novel method combining the empirical wavelet transform and the Fourier decomposition method for automated detection of alcoholism using electroencephalogram (EEG) signals. The new method, called the empirical Fourier decomposition (EFD) method, which combines the improved Fourier segmentation technique and zero phase filter bank concepts. The EFD method is applied to decompose the EEG signals into sub-band wave components. Features such as mean, standard deviation, kurtosis, line length, Hjorth parameters, log energy entropy, and norm energy entropy are extracted from each component. To select the optimal features, the horse-herd optimization algorithm (HHOA) is employed to identify the best feature subset. The experiments have been carried out using classifiers, such as least squares support vector machine (LS-SVM), random forest (RF), and k-nearest neighbor (k-NN). The proposed approach provides average accuracies of 99.01% with RF, 98.36% with LS-SVM, and 98.14% with the k-NN classifiers. The experimental results show that the proposed method outperforms the existing methods and can be adopted for real-time alcoholism detection. The decomposition algorithm presented demonstrates enhanced performance across both standard and acquired datasets, indicating its robustness and generalizability in alcoholism detection. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Sādhanā: Academy Proceedings in Engineering Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12046-026-03074-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Biomedical signal processing Type: general – SubjectFull: Alcoholism Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Neurophysiology Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Electrophysiology Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Empirical Fourier decomposition-based alcoholism detection using biomedical signals: A neuro-scientific approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bhuvaneshwari, M – PersonEntity: Name: NameFull: Grace Mary Kanaga, E IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02562499 Numbering: – Type: volume Value: 51 – Type: issue Value: 2 Titles: – TitleFull: Sādhanā: Academy Proceedings in Engineering Sciences Type: main |
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