Nydia Avila-Vanzzini

Dr. Nydia Avila-Vanzzini


Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 March 2025 in Sensors
Verified
Journal Article
Open access
PDF file
ICItayetzin Beurini Cruz-Vega
Itayetzin Beurini Cruz-Vega
NANydia Avila-Vanzzini
Nydia Avila-Vanzzini
GGG HORTENSIA González
G HORTENSIA González
RSRASHIDI SPRINGALL
RASHIDI SPRINGALL
JEJC Echeverría
JC Echeverría
Claudia LermaClaudia Lerma
Claudia Lerma
Introduction: Aortic valve disease (AVD) is an inflammatory, lipid infiltration and calcification disease that has been associated with changes in the conventional linear heart rate variability (HRV) indices showing a marked shift towards sympathetic predominance and a deterioration of the autonomic control. Objective: To explore the HRV dynamics in AVD patients through nonlinear methods by recurrence quantification analysis (RQA). Methods: In total, 127 subjects participated in a cross-sectional study categorized into three groups: healthy valve (HV), aortic valve sclerosis (AVSc), and aortic valve stenosis (AVS), as determined by echocardiographic assessment. HRV data were collected from five-minute ECG recordings at both a supine position and active standing. RQA indices were calculated using the Cross Recurrence Plot Toolbox. Results: In the supine position, patients with AVS exhibited larger determinism and trapping time than those with AVSc and HV. The analysis of these differences revealed that determinism and laminarity increased progressively from HV to AVS. In the same way, the magnitude of change (Δ) between positions decreased and presented the lowest values in AVS in most of the nonlinear indices. Conclusion: RQA indices of HRV in AVD patients indicate a rigidizing dynamic characterized by larger determinism and extended trapping times in fewer system states in relation to the severity of AVD. These findings establish a precedent for future perspective assessments for the implementation of these methods in medical software or devices.