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Chaos-Based Analysis of Heart Rate Variability Time Series in Obstructive Sleep Apnea Subjects
Shiva Naghsh1, Mohammad Ataei1, Mohammadreza Yazdchi2
1Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
Journal of Medical Signals and Sensors
|March 14, 2020
Summary
Chaos-based analysis of heart rate variability (HRV) can diagnose obstructive sleep apnea (OSA). This study used correlation dimension (CD) on HRV signals, showing significant differences between OSA patients and healthy individuals.
Area of Science:
- Cardiology
- Sleep Medicine
- Nonlinear Dynamics
Background:
- Obstructive sleep apnea (OSA) is a prevalent disorder characterized by periodic heart rate fluctuations.
- Current diagnostic methods for OSA sometimes employ chaos-based analysis of electrocardiogram (ECG) signals.
- Heart rate variability (HRV) analysis offers a potential alternative for assessing OSA-related cardiac dynamics.
Purpose of the Study:
- To investigate the efficacy of chaos-based analysis of HRV signals for diagnosing obstructive sleep apnea (OSA).
- To compare the diagnostic performance of correlation dimension (CD) applied to HRV with traditional ECG-based chaos analysis and detrended fluctuation analysis (DFA).
Main Methods:
- HRV time series were extracted from 1-hour ECG recordings of 17 OSA patients and 9 healthy individuals.
- Phase-space reconstruction of HRV data was performed to calculate the correlation dimension (CD) using the Grassberger-Procaccia algorithm.
- Detrended fluctuation analysis (DFA) was also applied to HRV data for comparative analysis.
Main Results:
- The correlation dimension (CD) index revealed a statistically significant difference (P < 0.005) in the nonlinear dynamics of HRV signals between OSA patients and healthy controls.
- Results from CD analysis of HRV showed comparable diagnostic potential to established nonlinear methods like DFA for OSA detection.
Conclusions:
- Chaos-based analysis of heart rate variability (HRV) using correlation dimension (CD) is a viable method for diagnosing obstructive sleep apnea (OSA).
- This approach provides a non-invasive and effective tool for differentiating OSA patients from healthy individuals based on cardiac autonomic function.
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