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Wavelet analysis for early identification of HRV changes in offspring with genetic predisposition to hypertension in

A Hossen1, L Khriji1, B Al Ghunaimi2

  • 1Department of Electrical & Computer Engineering, Sultan Qaboos University, Muscat, Oman.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|January 11, 2021
PubMed

Insights

A new wavelet-based system effectively detects early hypertension risk in offspring by analyzing heart rate variability (HRV). This method accurately classifies individuals based on genetic predisposition, aiding early cardiovascular intervention.

Area of Science:

  • Cardiovascular Research
  • Biomedical Signal Processing
  • Genetics and Hypertension

Background:

  • Offspring with a genetic predisposition to hypertension may exhibit elevated resting blood pressure (BP) and sympathetic nervous system activity.
  • Heart rate variability (HRV) analysis, particularly its sympathetic component, can be computed using signal processing algorithms.
  • Early detection of cardiovascular changes in at-risk youth is crucial for timely intervention and treatment.

Purpose of the Study:

  • To design a wavelet-based system for estimating HRV to detect early cardiovascular changes in offspring genetically predisposed to hypertension.
  • To investigate the relationship between hypertension and alterations in HRV patterns.
  • To enable early identification and treatment of young individuals at risk for hypertension.

Main Methods:

  • Frequency and time domain analyses of HRV were employed to understand autonomic nervous system function in offspring with varying genetic hypertension risk.
  • A wavelet-based soft-decision algorithm was utilized for spectral analysis of HRV signals.
  • Classification models were developed to differentiate between three groups: normotensive offspring (ONT), those with one hypertensive parent (OHT1), and those with two hypertensive parents (OHT2).

Main Results:

  • The summation of power in HRV wavelet-based spectrum bands B4 and B5 (0.046875 Hz-0.078125 Hz) served as an effective classification factor.
  • Classification accuracy between normotensive offspring (ONT) and offspring with two hypertensive parents (OHT2) reached 85.10%.
  • Classification accuracy between offspring with one hypertensive parent (OHT1) and OHT2 was 81.81%, and between a combined ONT/OHT1 group and OHT2 was 85.50%.

Conclusions:

  • The wavelet-based spectral analysis technique is a successful tool for classifying subjects based on their susceptibility to hypertension development.
  • This method demonstrates potential for early identification of individuals at higher risk for hypertension.
  • The findings support the use of advanced signal processing for non-invasive cardiovascular risk assessment in young populations.
Abstract

Keywords:
Heart rate variabilityclassificationhypertensionhypertensive parentsnormotensive parentsoffspringoman family studywavelet analysis

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