arXiv cs.AI· Md Basit Azam, Sarangthem Ibotombi Singh·· 1 天前AI 评分24
重新评估用于血糖感知的心率变异性生物标志物：年龄归一化与受试者独立验证的影响
Re-evaluating Heart Rate Variability Biomarkers for Glucose Sensing: The Impact of Age Normalisation and Subject-Independent Validation
AI 导读
一项针对 43 名男性 2 型糖尿病患者的研究重新评估了 ECG 心率变异性（HRV）对血糖状态的指示能力，采用留一受试者交叉验证并在训练折内完成特征选择与标准化。
正文 · 原文
Abstract: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.
| Subjects: | Signal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2508.11682 [eess.SP] |
| (or arXiv:2508.11682v3 [eess.SP] for this version) | |
| https://doi.org/10.48550/arXiv.2508.11682 arXiv-issued DOI via DataCite |
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| Journal reference: | BMC Med Inform Decis Mak 26, 166 (2026) |
| Related DOI: | https://doi.org/10.1186/s12911-026-03455-8
DOI(s) linking to related resources |
Submission history
From: Md Basit Azam [view email]
[v1]
Sat, 9 Aug 2025 19:24:31 UTC (1,785 KB)
[v2]
Tue, 19 Aug 2025 13:01:13 UTC (1,325 KB)
[v3]
Tue, 10 Nov 2026 20:53:09 UTC (2,558 KB)
来源：arXiv cs.AI · arxiv.org