ECG Recordings and QTc-RR Differential Biomarker for Type 2 Diabetes Assessment

88 patients with type 2 diabetes and 93 healthy controls provided 30-minute Lead II ECG recordings for this study. Xuwei Liao developed a novel automated algorithm to detect Q and T points across five T-wave morphological types, validated by two electrophysiologists. The resulting data, last updated in April 2026, introduces a new RQT_diff parameter that improved HRV analysis AUCs by up to 27%.

Use Cases

• Validating automated Q- and T-point detection algorithms based on the described method for diverse T-wave morphologies.
• Comparing QTc interval values between patient groups for early assessment of autonomic dysfunction.
• Evaluating the novel RQT_diff parameter against traditional RR intervals for heart rate variability analysis.
• Training logistic regression models for predictive performance using metrics like the Baroreflex Entropy Index.

Strengths

• Algorithm accuracy was verified by two electrophysiologists.
• The novel RQT_diff parameter improved AUC for SSR and LHR indices by 27% and 13%, respectively.
• Data is shared under a permissive CC-BY-4.0 license.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 29.1 KB, indicating limited scope.

Provenance

Source

figshare, author Xuwei Liao

Collection Method

Analysis of 30-minute Lead II ECG recordings from a clinical study.

Time Range

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Freshness

Last updated 2026-04-30 12:02:18; freshness should be verified.

Geography

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