Age-related network topological difference based on the sleep ECG signal
Guohun Zhu1, Cong Wang2, Feng Liu1
1School of ITEE, The University of Queensland, St Lucia, 4072, Brisbane, Australia.
Physiological Measurement
|August 10, 2018
Summary
Age-related differences in sleep electrocardiogram (ECG) network properties were identified. These network variations show potential for sleep stage classification and disorder diagnosis.
Area of Science:
- Cardiovascular Physiology
- Network Science
- Sleep Medicine
Background:
- Age significantly impacts brain network characteristics during sleep (EEG/fMRI).
- Limited understanding exists on age-related ECG network properties across sleep stages.
Purpose of the Study:
- To explore age-related scale-free and small-world network properties of ECG signals.
- To investigate how these network properties change during different sleep stages (Wakeful, Light Sleep, Deep Sleep, REM).
Main Methods:
- Analysis of ECG signals from male subjects divided into younger (<=40) and older (>40) age groups.
- Evaluation of scale-free network parameters (mean degree, clustering coefficient, path length).
- Assessment of small-world network properties across distinct sleep stages.
Main Results:
- Distinct scale-free network topologies were observed between age groups.
- Small-world properties were present in all sleep stages for both groups.
- The small-world index significantly decreased with age during Light Sleep and REM stages.
Conclusions:
- ECG network topology variations exhibit age-relevant differences.
- These age-related network changes in sleep ECG signals can potentially aid in sleep stage classification.
- Findings suggest utility for sleep disorder diagnosis based on ECG network analysis.
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