Innovative Fibromyalgia Detection Approach Based on Quantum-Inspired 3LBP Feature Extractor Using ECG Signal
Fibromyalgia is a chronic pain syndrome associated with sleep disturbances, which may manifest as altered electroencephalography and electrocardiography (ECG) signal alterations during sleep. We aimed to develop a lightweight machine learning model for diagnosing fibromyalgia using single-lead ECG s...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10253679/ |
| _version_ | 1797663925263663104 |
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| author | Prabal Datta Barua Makiko Kobayashi Masayuki Tanabe Mehmet Baygin Jose Kunnel Paul Thomas Iype Sengul Dogan Turker Tuncer Ru-San Tan U. Rajendra Acharya |
| author_facet | Prabal Datta Barua Makiko Kobayashi Masayuki Tanabe Mehmet Baygin Jose Kunnel Paul Thomas Iype Sengul Dogan Turker Tuncer Ru-San Tan U. Rajendra Acharya |
| author_sort | Prabal Datta Barua |
| collection | DOAJ |
| description | Fibromyalgia is a chronic pain syndrome associated with sleep disturbances, which may manifest as altered electroencephalography and electrocardiography (ECG) signal alterations during sleep. We aimed to develop a lightweight machine learning model for diagnosing fibromyalgia using single-lead ECG signals recorded during sleep. We analyzed 139 single-lead ECGs recorded during Stage 2 and Sleep Stage 3 of 16 patients with fibromyalgia and 16 age and sex matched controls. ECG records were divided into 15-second segments: 3308 and 1783 in healthy vs fibromyalgia classes, respectively. Our model comprised (1) feature extraction that combined an 8-wavelet filter and 4-level multiple filters-based multilevel discrete wavelet transform signal decomposition with a novel local binary pattern (LBP)-like function, 3LBP, that generated multiple patterns (analogous to quantum superposition) for feature map value extraction (the optimal input-specific pattern was dynamically selected using a novel forward-forward algorithm); (2) feature selection using neighborhood component analysis and Chi-square functions; (3) classification with k-nearest neighbors and support vector machine classifiers using leave-one-record-out cross-validation; and (4) mode function-based iterative majority voting to generate voted results, from which the best model result was derived. Our model attained binary classification accuracies of 93.87% and 92.02% for Sleep Stage 2 and Sleep Stage 3, respectively. The observed outcomes and empirical evidence unequivocally demonstrate the efficacy of our proposed methodology in differentiating the electrocardiographic signatures of fibromyalgia patients from control subjects. The model exhibited self-organizational properties and computational efficiency, rendering it amenable to facile clinical integration. |
| first_indexed | 2024-03-11T19:21:50Z |
| format | Article |
| id | doaj.art-283dfcc0c7b1440eb13f2155d3e1a224 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T19:21:50Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-283dfcc0c7b1440eb13f2155d3e1a2242023-10-06T23:01:25ZengIEEEIEEE Access2169-35362023-01-011110135910137210.1109/ACCESS.2023.331514910253679Innovative Fibromyalgia Detection Approach Based on Quantum-Inspired 3LBP Feature Extractor Using ECG SignalPrabal Datta Barua0https://orcid.org/0000-0001-5117-8333Makiko Kobayashi1https://orcid.org/0000-0003-4711-530XMasayuki Tanabe2Mehmet Baygin3https://orcid.org/0000-0001-6449-8950Jose Kunnel Paul4Thomas Iype5Sengul Dogan6https://orcid.org/0000-0001-9677-5684Turker Tuncer7https://orcid.org/0000-0002-5126-6445Ru-San Tan8https://orcid.org/0000-0003-2086-6517U. Rajendra Acharya9https://orcid.org/0000-0003-2689-8552Graduate School of Science and Technology, Kumamoto University, Kumamoto, JapanGraduate School of Science and Technology, Kumamoto University, Kumamoto, JapanGraduate School of Science and Technology, Kumamoto University, Kumamoto, JapanDepartment of Computer Engineering, Faculty of Engineering and Architecture, Erzurum Technical University, Erzurum, TurkeyDepartment of Neurology, Government Medical College Thiruvananthapuram, Thiruvananthapuram, Kerala, IndiaDepartment of Neurology, Government Medical College Thiruvananthapuram, Thiruvananthapuram, Kerala, IndiaDepartment of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, TurkeyDepartment of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, TurkeyDepartment of Cardiology, National Heart Centre Singapore, 5 Hospital Dr, SingaporeSchool of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD, AustraliaFibromyalgia is a chronic pain syndrome associated with sleep disturbances, which may manifest as altered electroencephalography and electrocardiography (ECG) signal alterations during sleep. We aimed to develop a lightweight machine learning model for diagnosing fibromyalgia using single-lead ECG signals recorded during sleep. We analyzed 139 single-lead ECGs recorded during Stage 2 and Sleep Stage 3 of 16 patients with fibromyalgia and 16 age and sex matched controls. ECG records were divided into 15-second segments: 3308 and 1783 in healthy vs fibromyalgia classes, respectively. Our model comprised (1) feature extraction that combined an 8-wavelet filter and 4-level multiple filters-based multilevel discrete wavelet transform signal decomposition with a novel local binary pattern (LBP)-like function, 3LBP, that generated multiple patterns (analogous to quantum superposition) for feature map value extraction (the optimal input-specific pattern was dynamically selected using a novel forward-forward algorithm); (2) feature selection using neighborhood component analysis and Chi-square functions; (3) classification with k-nearest neighbors and support vector machine classifiers using leave-one-record-out cross-validation; and (4) mode function-based iterative majority voting to generate voted results, from which the best model result was derived. Our model attained binary classification accuracies of 93.87% and 92.02% for Sleep Stage 2 and Sleep Stage 3, respectively. The observed outcomes and empirical evidence unequivocally demonstrate the efficacy of our proposed methodology in differentiating the electrocardiographic signatures of fibromyalgia patients from control subjects. The model exhibited self-organizational properties and computational efficiency, rendering it amenable to facile clinical integration.https://ieeexplore.ieee.org/document/10253679/ECG-based fibromyalgia detection3LBPmultiple filters-based multilevel discrete wavelet transformleave-one-record-out cross-validationquantum-based feature extraction |
| spellingShingle | Prabal Datta Barua Makiko Kobayashi Masayuki Tanabe Mehmet Baygin Jose Kunnel Paul Thomas Iype Sengul Dogan Turker Tuncer Ru-San Tan U. Rajendra Acharya Innovative Fibromyalgia Detection Approach Based on Quantum-Inspired 3LBP Feature Extractor Using ECG Signal IEEE Access ECG-based fibromyalgia detection 3LBP multiple filters-based multilevel discrete wavelet transform leave-one-record-out cross-validation quantum-based feature extraction |
| title | Innovative Fibromyalgia Detection Approach Based on Quantum-Inspired 3LBP Feature Extractor Using ECG Signal |
| title_full | Innovative Fibromyalgia Detection Approach Based on Quantum-Inspired 3LBP Feature Extractor Using ECG Signal |
| title_fullStr | Innovative Fibromyalgia Detection Approach Based on Quantum-Inspired 3LBP Feature Extractor Using ECG Signal |
| title_full_unstemmed | Innovative Fibromyalgia Detection Approach Based on Quantum-Inspired 3LBP Feature Extractor Using ECG Signal |
| title_short | Innovative Fibromyalgia Detection Approach Based on Quantum-Inspired 3LBP Feature Extractor Using ECG Signal |
| title_sort | innovative fibromyalgia detection approach based on quantum inspired 3lbp feature extractor using ecg signal |
| topic | ECG-based fibromyalgia detection 3LBP multiple filters-based multilevel discrete wavelet transform leave-one-record-out cross-validation quantum-based feature extraction |
| url | https://ieeexplore.ieee.org/document/10253679/ |
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