HRV features as potential biomarkers for auxiliary diagnosis in epilepsy

Figures

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  Fig. 1. Schematic diagram of ECG patch.

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  Fig. 2. Performance of single-paradigm logistic regression models for epilepsy diagnosis. Receiver Operating Characteristic (ROC) curves for five logistic regression models, each based on a single autonomic testing paradigm. Panels (A, C, E, G, I) show model performance on the training set (n = 280), while panels (B, D, F, H, J) show performance on the independent test set (n = 120). The paradigms are: (A, B) Supine Resting, (C, D) Active Standing, (E, F) Valsalva, (G, H) Sustained Handgrip, and (I, J) Whole Experiment. The Area Under the Curve (AUC), accuracy (ACC), sensitivity, and specificity, calculated at the optimal cutoff point determined by the Youden index, are displayed with their 95% confidence intervals.

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  Fig. 3. Importance ranking of features in the epilepsy diagnostic models across five autonomic paradigms. This figure illustrates the contribution of each feature to the logistic regression models for five different paradigms. Feature importance was quantified by the magnitude of the standardized coefficients and ranked accordingly. The paradigms shown are: (A) Supine Resting Test, (B) Active Standing Test, (C) Valsalva Test, (D) Sustained Handgrip Test, and (E) Whole Experiment Test.

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  Fig. 4. Performance of combined-paradigm models for epilepsy diagnosis. Receiver Operating Characteristic (ROC) curves evaluating the diagnostic performance of three combined-paradigm models. Panels (A, C, E) show performance on the training set (n = 280), while panels (B, D, F) show performance on the independent test set (n = 120). The combined paradigms are: (A, B) Supine Resting + Active Standing, (C, D) Supine Resting + Valsalva, and (E, F) Supine Resting + Sustained Handgrip. Performance metrics, including Area Under the Curve (AUC), accuracy (ACC), sensitivity, and specificity, were calculated at the optimal cutoff determined by the Youden index.

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  Fig. 5. Calibration curves for the combined-paradigm epilepsy diagnostic models. These plots assess the agreement between predicted and observed probabilities for three combined-paradigm models. The diagonal dashed line represents perfect calibration; curves closer to this line indicate better diagnostic accuracy. Calibration curves for both the training set (Trainset) and test set (Testset) are shown. The three models combine supine resting data with: (A) Active Standing, (B) Valsalva, and (C) Sustained Handgrip.

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  Fig. 6. Decision curve analysis evaluating the clinical utility of the combined diagnostic models. Decision curve analysis of the three combined models on the training set (A, C, E) and test set (B, D, F). The dashed line (pre) represents the diagnostic model, which provides a greater net benefit than either the “treat all” (solid gray line, All) or “treat none” (horizontal black line, None) strategies when it lies above them. The models combine supine resting data with: (A, B) Active Standing, (C, D) Valsalva, and (E, F) Sustained Handgrip.