Adaptive Lévy Flight-Based Optimization for Fetal ECG Recovery and Arrhythmia Detection

Abstract

Early detection of fetal cardiac abnormality facilitates timely intervention and management. Existing noninvasive fetal electrocardiogram (fECG) extraction techniques often rely on fixed or heuristic optimization parameters and segment-level evaluation strategies, limiting robustness under nonstationary maternal–fetal signal overlap and introducing potential information leakage. This work presents an adaptive optimization framework utilizing the adaptive Lévy flight-based cuckoo search (ALCS) method for improved fECG extraction and detection of fetal arrhythmias, contributing to intelligent human–machine systems in obstetric monitoring. The framework employs ALCS as an outer loop optimizer to determine an optimal initialization of the least mean square (LMS) filter coefficients for adaptive noise cancellation (ANC), where a convergence-aware Lévy flight parameter governs the search process. The Lévy parameter $\lambda$ evolves adaptively within $[1.5, 2.0]$ enabling an effective transition from exploration to exploitation. The proposed optimizer achieves approximately 1.8–$2.0\times$ faster convergence than standard LMS, reducing the mean-squared error by $\sim$74% to 0.10 within 50 iterations. A comprehensive feature set exceeding 100 descriptors is extracted from fECG signals and evaluated using ensemble classifiers. Ensuring a leakage-safe evaluation protocol, the experiments were conducted using subjectwise grouped cross-validation with foldwise feature selection on the noninvasive fetal ECG arrhythmia database available from PhysioNet. The proposed framework achieves robust arrhythmia detection, attaining 91.2% accuracy and 95.0% area under the curve across unseen subjects. To quantify systematic bias and agreement across evaluation stages, Bland–Altman analysis is employed to provide clinical interpretability beyond aggregate scores. Overall, the results demonstrate that convergence-aware Lévy-based optimization improves ANC performance for fECG analysis, while acknowledging dataset scale and offline evaluation limitations.