Chaos-Based Analysis of Heart Rate Variability Time Series in Obstructive Sleep Apnea Subjects
عنوان مقاله: Chaos-Based Analysis of Heart Rate Variability Time Series in Obstructive Sleep Apnea Subjects
شناسه ملی مقاله: JR_JMSI-10-1_007
منتشر شده در در سال 1399
شناسه ملی مقاله: JR_JMSI-10-1_007
منتشر شده در در سال 1399
مشخصات نویسندگان مقاله:
Shiva Naghsh - Departments of Electrical Engineering
Mohammad Ataei - Departments of Electrical Engineering
Mohammadreza Yazdchi - Biomedical Engineering, Faculty of Engineering, University of Isfahan
Mohammad Hashemi - Department of Cardiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
خلاصه مقاله:
Shiva Naghsh - Departments of Electrical Engineering
Mohammad Ataei - Departments of Electrical Engineering
Mohammadreza Yazdchi - Biomedical Engineering, Faculty of Engineering, University of Isfahan
Mohammad Hashemi - Department of Cardiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Obstructive sleep apnea (OSA) is a common disorder which can cause periodic fluctuations in heart
rate. To diagnose sleep apnea, some studies analyze electrocardiogram (ECG) signals by adopting
chaos‑based analysis. This research is going to specifically focus on whether it is possible to use
chaos‑based analysis of heart rate variability (HRV) signals rather than using chaotic analysis of
ECG signals to diagnose OSA. While conventional studies mostly use chaos‑based analysis of ECG
signals to detect OSA, here, we apply correlation dimension (CD) as a chaotic index to analyze HRV
data in OSA patients. For this purpose, ۱۷ patients with OSA and ۹ healthy individuals referred to
a sleep clinic in Isfahan/Iran are studied, and their HRV time series were extracted from ۱‑h ECG
signals recorded overnight. The preliminary step to calculate CD is phase‑space reconstruction of
the system based on HRV time series. Corresponding parameters, including embedding dimension
and lag time, are estimated optimally using enhanced related methods, and then CD is calculated
using Grassberger–Procaccia algorithm. Moreover, to evaluate our results, detrended fluctuation
analysis (DFA), one of the well‑known nonlinear methods in HRV analysis to detect OSA, is also
applied to our data and the result is compared with those obtained from CD analysis of HRV. CD
index with P < ۰.۰۰۵ indicates a significant difference in nonlinear dynamics of HRV signals detected
from OSA patients and healthy individuals.
کلمات کلیدی: Chaotic indexes, correlation dimension, detrended fluctuation analysis, heart rate variability, obstructive sleep apnea
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1700088/