Barua, Prabal DattaKobayashi, MakikoDogan, SengulBaygin, MehmetTuncer, TurkerPaul, Jose KunnelAcharya, U. R.2026-03-262026-03-2620252169-353610.1109/ACCESS.2025.35730352-s2.0-105006649283https://doi.org/10.1109/ACCESS.2025.3573035https://hdl.handle.net/20.500.14901/2702Tuncer, Türker/0000-0002-5126-6445; Kobayashi, Makiko/0000-0003-4711-530X; Barua, Prabal Datta/0000-0001-5117-8333; Acharya, U Rajena/0000-0003-2689-8552; Iype, Thomas/0000-0003-4804-9869; , Jose Kunnel Paul/0009-0006-1143-8691;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 (NCAChi2) - 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.eninfo:eu-repo/semantics/openAccessFeature ExtractionBrain ModelingElectroencephalographySleepElectrocardiographyAccuracyComputational ModelingBiological System ModelingAutomataSupport Vector MachinesAttention Maximum PoolingAutomata-Based Dynamic PatternsEEG and ECG Signal ClassificationFibromyalgia DetectionFlower Automata PatternIntersection-Based Feature SelectionArticle