Multimodal neural models using ECG-derived RR intervals and HRV features during an oral glucose tolerance test accurately identified metabolic syndrome, achieving accuracies ranging from 0.92 to 0.95.
Observational (n=40)
Can multimodal neural models using ECG-derived RR intervals and HRV features accurately classify metabolic syndrome during an oral glucose tolerance test?
Multimodal neural models using ECG-derived HRV features during an oral glucose tolerance test can accurately identify metabolic syndrome, offering a potential non-invasive screening tool.
Effect estimate: Accuracy 0.92-0.95
Background: Metabolic syndrome (MetS) leads to alterations in cardiac autonomic control that can be detected from electrocardiogram (ECG)-derived markers, particularly when the cardiovascular system is challenged during an oral glucose tolerance test (OGTT). Methods: In this paper, we present an automated framework for MetS identification using RR intervals and heart rate variability (HRV) features extracted from 12-lead ECG recordings acquired during the five OGTT stages in 40 male participants (15 with MetS, 10 controls, and 15 endurance-trained marathon runners). RR intervals were first derived using a multilead Pan-Tompkins approach with fusion-based validation. From these RR series, HRV descriptors were computed from time-domain statistics (RR mean, SDNN, rMSSD, pNN50), spectral indices (VLF, LF, HF, LF/HF), and nonlinear measures (SD1, SD2, SampEn, DFA-α1). Conventional HRV analysis revealed pronounced physiological differences between groups: MetS subjects exhibited reduced parasympathetic activity, reflected by lower rMSSD and SD1, lower HF power, and higher LF/HF ratios, whereas marathoners showed greater vagal modulation, higher HF power, and increased signal complexity. Healthy controls showed an intermediate autonomic profile. Using RR sequences and HRV descriptors (256 samples per stage), we trained three multimodal classifiers: a CNN-MLP model with a softmax output, a CNN-MLP model with an SVM head, and a CNN + LSTM-MLP + SVM architecture. Results: All models achieved strong discriminative performance, with accuracies ranging from 0.92 to 0.95, F1-macro values from 0.92 to 0.95, and macro-AUC values from 0.96 to 0.97. The CNN-MLP model achieved the best overall performance, whereas the CNN + LSTM-MLP + SVM model showed strong class discrimination, particularly for endurance athletes, while maintaining competitive recall for MetS. Conclusions: These findings support the feasibility of ECG-based autonomic assessment as a complementary non-invasive approach for early metabolic risk detection in clinical and preventive cardiometabolic screening settings.
Mejia et al. (Tue,) conducted a observational in Metabolic syndrome (n=40). Multimodal neural models using RR intervals and HRV features vs. Healthy controls and marathon runners was evaluated on Metabolic syndrome classification performance (Accuracy 0.92-0.95). Multimodal neural models using ECG-derived RR intervals and HRV features during an oral glucose tolerance test accurately identified metabolic syndrome, achieving accuracies ranging from 0.92 to 0.95.