Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG Signals
- Publisher:
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Publication Type:
- Journal Article
- Citation:
- IEEE ACCESS, 2025, 13, (99), pp. 99032-99047
- Issue Date:
- 2025
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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: (i) Flower Automata Pattern (FAP) for self-organized pattern-based feature extraction, and (ii) 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.
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