← 研踪 PaperTrailarXiv:2508.11682 · v3
Re-evaluating Heart Rate Variability Biomarkers for Glucose Sensing: The Impact of Age Normalisation and Subject-Independent Validation
首次提交 2025-08-10 03:24
版本更新 2026-11-11 04:53
雷达观测 2026-11-13 11:12
摘要
Heart rate variability (HRV) from electrocardiogram (ECG) signals may indicate glycemic status, but prior studies often combine distinct glucose targets and use validation strategies vulnerable to data leakage. Since HRV declines with age, aging may be confounded with diabetes-related autonomic dysfunction. We tested whether age-dependent HRV normalisation improves glycemic estimation. We analysed ECG features from 43 male patients with type 2 diabetes. Outcomes were glycated hemoglobin (HbA1c; n=29) and fasting blood glucose (FBG; n=38). We used leave-one-subject-out cross-validation, with feature selection and standardisation within each training fold. We compared 20 machine-learning algorithms, six age-adjustment methods, and 20 normalisation parameter combinations. Validation included 500 permutations and bootstrap confidence intervals. Extra Trees regression performed best: R2=0.222 (r=0.476, p=0.009) for HbA1c and R2=0.086 (r=0.344, p=0.034) for FBG. Mean absolute errors were 1.18 percentage points and 2.27 mmol/L, respectively. Permutation testing supported both associations (p=0.002). Contrary to our hypothesis, no age-adjustment method or parameter combination improved performance. Tree ensembles retained positive performance under strict validation, whereas linear models had negative R2 values; neural networks performed poorly. Strict within-fold preprocessing materially changed model performance and revealed potential inflation from conventional validation. Bootstrap 95% confidence intervals were [0.13, 0.82] for HbA1c R2 and [0.10, 0.72] for FBG R2. Although findings support HRV-glycemic associations, performance remains insufficient for standalone clinical use. The study highlights the importance of separating glycemic targets, subject-independent validation, within-fold preprocessing, and comprehensive baselines in non-invasive glycemic monitoring research.
期刊信息：BMC Med Inform Decis Mak 26, 166 (2026)
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v3 · 2026-11-11 04:53 · 最新观测版本
Re-evaluating Heart Rate Variability Biomarkers for Glucose Sensing: The Impact of Age Normalisation and Subject-Independent Validation
Heart rate variability (HRV) from electrocardiogram (ECG) signals may indicate glycemic status, but prior studies often combine distinct glucose targets and use validation strategies vulnerable to data leakage. Since HRV declines with age, aging may be confounded with diabetes-related autonomic dysfunction. We tested whether age-dependent HRV normalisation improves glycemic estimation. We analysed ECG features from 43 male patients with type 2 diabetes. Outcomes were glycated hemoglobin (HbA1c; n=29) and fasting blood glucose (FBG; n=38). We used leave-one-subject-out cross-validation, with feature selection and standardisation within each training fold. We compared 20 machine-learning algorithms, six age-adjustment methods, and 20 normalisation parameter combinations. Validation included 500 permutations and bootstrap confidence intervals. Extra Trees regression performed best: R2=0.222 (r=0.476, p=0.009) for HbA1c and R2=0.086 (r=0.344, p=0.034) for FBG. Mean absolute errors were 1.18 percentage points and 2.27 mmol/L, respectively. Permutation testing supported both associations (p=0.002). Contrary to our hypothesis, no age-adjustment method or parameter combination improved performance. Tree ensembles retained positive performance under strict validation, whereas linear models had negative R2 values; neural networks performed poorly. Strict within-fold preprocessing materially changed model performance and revealed potential inflation from conventional validation. Bootstrap 95% confidence intervals were [0.13, 0.82] for HbA1c R2 and [0.10, 0.72] for FBG R2. Although findings support HRV-glycemic associations, performance remains insufficient for standalone clinical use. The study highlights the importance of separating glycemic targets, subject-independent validation, within-fold preprocessing, and comprehensive baselines in non-invasive glycemic monitoring research.
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