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

• Published in: Technology and health care Vol. 25; no. 4; pp. 641 - 649
• Main Authors: Hossen, A., Barhoum, A., Jaju, D., Gowri, V., Al-Hashmi, K., Hassan, M.O., Al-Kharusi, L.
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 09.08.2017; Sage Publications Ltd
• Subjects: Adult; Algorithms; Fast Fourier transformations; Female; Fourier Analysis; Fourier transforms; Heart rate; Heart Rate - physiology; Humans; Identification methods; Low frequencies; Oman; Parasympathetic nervous system; Patients; Pre-eclampsia; Pre-Eclampsia - diagnosis; Pre-Eclampsia - physiopathology; Preeclampsia; Pregnancy; Sensitivity; Sensitivity and Specificity; Spectra; Spectral analysis; Variability; Very Low Frequencies; Wavelet Analysis; Wavelet transforms; Oman; identification; normal pregnant; soft-decision wavelet spectral analysis; HRV; Preeclampsia
• ISSN: 0928-7329; 1878-7401; 1878-7401
• DOI: 10.3233/THC-160681

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.