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Frontiers in cardiovascular medicine · 2026

Enhanced ECG signal based on heart rate variability: validation of the direct TR correction sequence in diabetes.

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Yang Shanglin, Zhou Hongbin, Liao Xuwei, Lin Yuyang, Liou Juin J

Аннотация

BACKGROUND: Heart rate variability (HRV) is a critical biomarker for assessing autonomic dysfunction, particularly in elderly and type 2 diabetes mellitus (T2DM) populations. However, traditional HRV indices, derived from R-R intervals (RRIs), often fail to detect subtle autonomic dysfunction in complex clinical scenarios. This study proposes a novel Direct TRI Correction (DTRC) sequence to optimize HRV assessment by directly extracting TR intervals (DTRI) and applying a single-step correction. The aim is to enhance accuracy in evaluating HRV, particularly in elderly and T2DM cohorts, where early detection is critical for preventing cardiovascular complications.

METHODS: The study enrolled 124 participants (60 T2DM patients and 64 healthy controls) stratified into three groups based on glycemic control. HRV indices were derived from traditional RRI sequences, two-step corrected TRC sequences, and the novel single-step corrected DTRC sequences, respectively. Statistical analyses were performed on the HRV indices of three sequences to evaluate the comparative performance of DTRC sequences.

RESULTS: DTRC-based HRV indices demonstrated superior sensitivity and discriminative capability compared to both RRI-based and TRC-based HRV indices. Significant improvements were observed in RMSSD (p = 0.007), pNN50 (p = 0.043), SDSD (p = 0.007), LHR (p = 0.016), SSR (p = 0.010), BEI (p = 0.002), and MSELS (p = 0.009). Visualization analysis and ROC curves confirmed clearer inter-group separation, particularly for T2DM patients with poor glycemic control.

CONCLUSIONS: The DTRC sequence significantly enhances HRV assessment accuracy, offering a reliable tool for early detection of autonomic dysfunction in high-risk populations. Its simplified correction process and improved sensitivity hold promise for clinical diagnostics and wearable health monitoring applications.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

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