LTPat-based SOXFE: A self-organized explainable feature engineering model for automated fibromyalgia detection

BackgroundFibromyalgia is a chronic pain disorder associated with sleep disturbance and substantial functional burden.ObjectiveThis study evaluated a self-organized explainable feature engineering (SOXFE) model for EEG-based fibromyalgia classification.MethodsA previously reported sleep EEG dataset from a 32-participant source cohort was analyzed separately for NREM Stage 2 and Stage 3. The analysis-ready version contained 136 labeled stage-record files: 74 for Stage 2 and 62 for Stage 3. The SOXFE model comprised LTPat feature extraction, training-only CWNCA feature selection, tkNN classification, IMV information fusion, and DLob-based explanation. Record-wise LORO was primary; segment-level 10-fold cross-validation was complementary.ResultsRecord-wise LORO used 74 outer folds for Stage 2 and 62 for Stage 3. Descriptive pooled held-out-epoch accuracy was 92.77% (95% CI: 91.76-93.67) for Stage 2 and 97.00% (95% CI: 96.22-97.63) for Stage 3. Every epoch from the held-out recording was excluded from training and selection. Complementary segment-level 10-fold cross-validation yielded 100.00% accuracy for both stages but was not used as evidence of record-wise generalization.ConclusionsThe model provided record-disjoint classification and traceable model-derived explanations in this single-source dataset. LORO prevented within-record epoch leakage. However, participant linkage was unavailable, so records from the same person may have occurred across outer partitions and the reported values may overestimate person-independent performance. Verified subject-wise validation is required before clinical use.