Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG Signals
Electroencephalogram (EEG) and electrocardiogram (ECG) signals provide vital insights into brain and heart activity and are widely used in automated medical diagnostics. This study introduces a novel, multimodal fibromyalgia detection system developed by the fusion of EEG and ECG signals recorded du...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11014066/ |
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| author | Prabal Datta Barua Makiko Kobayashi Sengul Dogan Mehmet Baygin Turker Tuncer Jose Kunnel Paul Thomas Iype U. R. Acharya |
| author_facet | Prabal Datta Barua Makiko Kobayashi Sengul Dogan Mehmet Baygin Turker Tuncer Jose Kunnel Paul Thomas Iype U. R. Acharya |
| author_sort | Prabal Datta Barua |
| collection | DOAJ |
| description | Electroencephalogram (EEG) and electrocardiogram (ECG) signals provide vital insights into brain and heart activity and are widely used in automated medical diagnostics. This study introduces a novel, multimodal fibromyalgia detection system developed by the fusion of EEG and ECG signals recorded during sleep stages 2 and 3. The novelty of the model is the use of dynamic and interpretable feature engineering framework comprising of two innovations: 1) Flower Automata Pattern (FAP) for self-organized pattern-based feature extraction, and 2) Attention-Driven Wavelet Transform and Absolute Maximum Pooling (ADWTAMP) method for signal decomposition and compression. Three feature selection strategies—Neighborhood Component Analysis (NCA), Chi2, and the intersection of NCA and Chi2 (NCA^Chi2) — are employed to generate robust feature vectors, which are classified using k-nearest neighbors (kNN) and support vector machine (SVM) under the leave-one-record-out cross-validation (LORO CV) scheme. The final decision is derived through an iterative voting and greedy fusion approach. The proposed model achieved classification accuracies of 99.36% and 98.37% for sleep stages 2 and 3, respectively. Key advantages of the model include its high accuracy, low computational requirements (CPU-only execution), and explainable architecture. To the best of our knowledge, this is the first multimodal automata-based classification framework designed for fibromyalgia detection. |
| format | Article |
| id | doaj-art-25956812e5794c8ea10222e0f2a1cc4c |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-25956812e5794c8ea10222e0f2a1cc4c2025-08-20T03:20:58ZengIEEEIEEE Access2169-35362025-01-0113990329904710.1109/ACCESS.2025.357303511014066Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG SignalsPrabal Datta Barua0https://orcid.org/0000-0001-5117-8333Makiko Kobayashi1https://orcid.org/0000-0003-4711-530XSengul Dogan2https://orcid.org/0000-0001-9677-5684Mehmet Baygin3https://orcid.org/0000-0001-6449-8950Turker Tuncer4https://orcid.org/0000-0002-5126-6445Jose Kunnel Paul5https://orcid.org/0009-0006-1143-8691Thomas Iype6https://orcid.org/0000-0003-4804-9869U. R. Acharya7https://orcid.org/0000-0003-2689-8552School of Business (Information System), University of Southern Queensland, Brisbane, QLD, AustraliaGraduate School of Science and Technology, Kumamoto University, Kumamoto, JapanDepartment of Digital Forensics Engineering, Technology Faculty, Fırat University, Elâzığ, TürkiyeDepartment of Computer Engineering, Faculty of Engineering and Architecture, Erzurum Technical University, Erzurum, TürkiyeDepartment of Digital Forensics Engineering, Technology Faculty, Fırat University, Elâzığ, TürkiyeDepartment of Neurology, Government Medical College, Thiruvananthapuram, Kerala, IndiaDepartment of Neurology, Government Medical College, Thiruvananthapuram, Kerala, IndiaSchool of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, AustraliaElectroencephalogram (EEG) and electrocardiogram (ECG) signals provide vital insights into brain and heart activity and are widely used in automated medical diagnostics. This study introduces a novel, multimodal fibromyalgia detection system developed by the fusion of EEG and ECG signals recorded during sleep stages 2 and 3. The novelty of the model is the use of dynamic and interpretable feature engineering framework comprising of two innovations: 1) Flower Automata Pattern (FAP) for self-organized pattern-based feature extraction, and 2) Attention-Driven Wavelet Transform and Absolute Maximum Pooling (ADWTAMP) method for signal decomposition and compression. Three feature selection strategies—Neighborhood Component Analysis (NCA), Chi2, and the intersection of NCA and Chi2 (NCA^Chi2) — are employed to generate robust feature vectors, which are classified using k-nearest neighbors (kNN) and support vector machine (SVM) under the leave-one-record-out cross-validation (LORO CV) scheme. The final decision is derived through an iterative voting and greedy fusion approach. The proposed model achieved classification accuracies of 99.36% and 98.37% for sleep stages 2 and 3, respectively. Key advantages of the model include its high accuracy, low computational requirements (CPU-only execution), and explainable architecture. To the best of our knowledge, this is the first multimodal automata-based classification framework designed for fibromyalgia detection.https://ieeexplore.ieee.org/document/11014066/Attention maximum poolingautomata-based dynamic patternsEEG and ECG signal classificationfibromyalgia detectionflower automata patternintersection-based feature selection |
| spellingShingle | Prabal Datta Barua Makiko Kobayashi Sengul Dogan Mehmet Baygin Turker Tuncer Jose Kunnel Paul Thomas Iype U. R. Acharya Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG Signals IEEE Access Attention maximum pooling automata-based dynamic patterns EEG and ECG signal classification fibromyalgia detection flower automata pattern intersection-based feature selection |
| title | Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG Signals |
| title_full | Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG Signals |
| title_fullStr | Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG Signals |
| title_full_unstemmed | Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG Signals |
| title_short | Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG Signals |
| title_sort | flower automata pattern based discrimination of fibromyalgia from control subjects using fusion of sleep eeg and ecg signals |
| topic | Attention maximum pooling automata-based dynamic patterns EEG and ECG signal classification fibromyalgia detection flower automata pattern intersection-based feature selection |
| url | https://ieeexplore.ieee.org/document/11014066/ |
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