Identification of patients with preeclampsia from normal subjects using wavelet-based spectral analysis of heart rate variability

A. Hossen, A. Barhoum, D. Jaju, V. Gowri, K. Al-Hashmi, M. O. Hassan, L. Al-Kharusi

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Background: The spectral analysis of the heart rate variability (HRV) shows a decrease in the power of the high frequency (HF) component in preeclamptic pregnancy compared with normal pregnancy; such a decrease is associated with an increase in the low frequency (LF) and the very low frequency (VLF) power. The physiological interpretation is that preeclamptic pregnancy is associated with a facilitation of sympathetic regulation and an attenuation of parasympathetic influence of HR compared with non-pregnancy and normal pregnancy. Objective: To use an efficient nased on spectral analysis non-invasive technique to identify preeclamptic pregnant subjects from normal pregnant in Oman. Methods: The soft-decision wavelet-based technique is implemented to find the power of the HRV bands in high resolution manner compared to the classical fast Fourier Transform method. Data was obtained from 20 preeclamptic pregnant subjects and 20 normal pregnant controls of the same pregnancy duration, obtained from Nizwa and Sultan Qaboos University hospitals in Oman. Results: The soft-decision wavelet method succeeds to identify patients from normal pregnant with specificity, sensitivity and accuracy of 90%, 80% and 85%, respectively, compared to the FFT which results in 75% specificity, sensitivity and accuracy. Conclusion: The LF power obtained by Soft-decision wavelet decomposition is shown to be a successful feature for identification of preeclampsia.

Original languageEnglish
Pages (from-to)641-649
Number of pages9
JournalTechnology and Health Care
Volume25
Issue number4
DOIs
Publication statusPublished - 2017

Fingerprint

Pre-Eclampsia
Fast Fourier transforms
Spectrum analysis
Heart Rate
Pregnancy
Wavelet decomposition
Oman
Sensitivity and Specificity
Fourier Analysis
Sultan

Keywords

  • HRV
  • identification
  • normal pregnant
  • Preeclampsia
  • soft-decision wavelet spectral analysis

ASJC Scopus subject areas

  • Bioengineering
  • Biophysics
  • Information Systems
  • Biomaterials
  • Biomedical Engineering
  • Health Informatics

Cite this

@article{2ece58d99983494991c412b390da2ebc,
title = "Identification of patients with preeclampsia from normal subjects using wavelet-based spectral analysis of heart rate variability",
abstract = "Background: The spectral analysis of the heart rate variability (HRV) shows a decrease in the power of the high frequency (HF) component in preeclamptic pregnancy compared with normal pregnancy; such a decrease is associated with an increase in the low frequency (LF) and the very low frequency (VLF) power. The physiological interpretation is that preeclamptic pregnancy is associated with a facilitation of sympathetic regulation and an attenuation of parasympathetic influence of HR compared with non-pregnancy and normal pregnancy. Objective: To use an efficient nased on spectral analysis non-invasive technique to identify preeclamptic pregnant subjects from normal pregnant in Oman. Methods: The soft-decision wavelet-based technique is implemented to find the power of the HRV bands in high resolution manner compared to the classical fast Fourier Transform method. Data was obtained from 20 preeclamptic pregnant subjects and 20 normal pregnant controls of the same pregnancy duration, obtained from Nizwa and Sultan Qaboos University hospitals in Oman. Results: The soft-decision wavelet method succeeds to identify patients from normal pregnant with specificity, sensitivity and accuracy of 90{\%}, 80{\%} and 85{\%}, respectively, compared to the FFT which results in 75{\%} specificity, sensitivity and accuracy. Conclusion: The LF power obtained by Soft-decision wavelet decomposition is shown to be a successful feature for identification of preeclampsia.",
keywords = "HRV, identification, normal pregnant, Preeclampsia, soft-decision wavelet spectral analysis",
author = "A. Hossen and A. Barhoum and D. Jaju and V. Gowri and K. Al-Hashmi and Hassan, {M. O.} and L. Al-Kharusi",
year = "2017",
doi = "10.3233/THC-160681",
language = "English",
volume = "25",
pages = "641--649",
journal = "Technology and Health Care",
issn = "0928-7329",
publisher = "IOS Press",
number = "4",

}

TY - JOUR

T1 - Identification of patients with preeclampsia from normal subjects using wavelet-based spectral analysis of heart rate variability

AU - Hossen, A.

AU - Barhoum, A.

AU - Jaju, D.

AU - Gowri, V.

AU - Al-Hashmi, K.

AU - Hassan, M. O.

AU - Al-Kharusi, L.

PY - 2017

Y1 - 2017

N2 - Background: The spectral analysis of the heart rate variability (HRV) shows a decrease in the power of the high frequency (HF) component in preeclamptic pregnancy compared with normal pregnancy; such a decrease is associated with an increase in the low frequency (LF) and the very low frequency (VLF) power. The physiological interpretation is that preeclamptic pregnancy is associated with a facilitation of sympathetic regulation and an attenuation of parasympathetic influence of HR compared with non-pregnancy and normal pregnancy. Objective: To use an efficient nased on spectral analysis non-invasive technique to identify preeclamptic pregnant subjects from normal pregnant in Oman. Methods: The soft-decision wavelet-based technique is implemented to find the power of the HRV bands in high resolution manner compared to the classical fast Fourier Transform method. Data was obtained from 20 preeclamptic pregnant subjects and 20 normal pregnant controls of the same pregnancy duration, obtained from Nizwa and Sultan Qaboos University hospitals in Oman. Results: The soft-decision wavelet method succeeds to identify patients from normal pregnant with specificity, sensitivity and accuracy of 90%, 80% and 85%, respectively, compared to the FFT which results in 75% specificity, sensitivity and accuracy. Conclusion: The LF power obtained by Soft-decision wavelet decomposition is shown to be a successful feature for identification of preeclampsia.

AB - Background: The spectral analysis of the heart rate variability (HRV) shows a decrease in the power of the high frequency (HF) component in preeclamptic pregnancy compared with normal pregnancy; such a decrease is associated with an increase in the low frequency (LF) and the very low frequency (VLF) power. The physiological interpretation is that preeclamptic pregnancy is associated with a facilitation of sympathetic regulation and an attenuation of parasympathetic influence of HR compared with non-pregnancy and normal pregnancy. Objective: To use an efficient nased on spectral analysis non-invasive technique to identify preeclamptic pregnant subjects from normal pregnant in Oman. Methods: The soft-decision wavelet-based technique is implemented to find the power of the HRV bands in high resolution manner compared to the classical fast Fourier Transform method. Data was obtained from 20 preeclamptic pregnant subjects and 20 normal pregnant controls of the same pregnancy duration, obtained from Nizwa and Sultan Qaboos University hospitals in Oman. Results: The soft-decision wavelet method succeeds to identify patients from normal pregnant with specificity, sensitivity and accuracy of 90%, 80% and 85%, respectively, compared to the FFT which results in 75% specificity, sensitivity and accuracy. Conclusion: The LF power obtained by Soft-decision wavelet decomposition is shown to be a successful feature for identification of preeclampsia.

KW - HRV

KW - identification

KW - normal pregnant

KW - Preeclampsia

KW - soft-decision wavelet spectral analysis

UR - http://www.scopus.com/inward/record.url?scp=85027517410&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85027517410&partnerID=8YFLogxK

U2 - 10.3233/THC-160681

DO - 10.3233/THC-160681

M3 - Article

VL - 25

SP - 641

EP - 649

JO - Technology and Health Care

JF - Technology and Health Care

SN - 0928-7329

IS - 4

ER -