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Assessment of arteriosclerosis based on multiscale cross approximate entropy of human finger pulse wave
Na Ta1, Hai-Cheng Wei2, Miao-Miao Li1
1School of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, China.
Insights
Multiscale cross-approximate entropy analysis of pulse wave signals effectively assesses arteriosclerosis. This method reveals insights into autonomic nerve function and the impact of diabetes on arterial health.
Area of Science:
- Biomedical Engineering
- Cardiovascular Research
- Signal Processing
Background:
- Arteriosclerosis poses a significant threat to human health.
- Pulse wave signals contain rich information indicative of arteriosclerosis severity.
Purpose of the Study:
- To propose and validate a novel method for assessing arteriosclerosis using multiscale cross-approximate entropy of finger pulse wave signals.
- To investigate the relationship between pulse wave characteristics, autonomic nerve function, and arteriosclerosis.
Main Methods:
- A method based on multiscale cross-approximate entropy was applied to human finger pulse wave signals from 86 subjects.
- Analysis focused on the climb time and pulse wave peak interval across 1000 pulse cycles.
- Independent sample t-tests were used to compare entropy values between different groups.
Main Results:
- Small-scale cross-approximate entropy of climb time and pulse wave peak interval correlated with arteriosclerosis severity, reflecting autonomic nerve function.
- Large-scale cross-approximate entropy confirmed the influence of diabetes on the degree of arteriosclerosis.
Conclusions:
- Multiscale cross-approximate entropy serves as a comprehensive index for evaluating the degree of human arteriosclerosis.
- The findings highlight the utility of pulse wave analysis in understanding cardiovascular health.
Background:
Arteriosclerosis is one of the diseases that endanger human health. There is a large amount of information in pulse wave signals to reflect the degree of arteriosclerosis.
Objective:
The degree of arteriosclerosis is assessed by analyzing pulse wave signal and calculating multi-scale entropy values.
Methods:
A method based on the multiscale cross-approximate entropy of the pulse wave of the human finger is proposed to assess the degree of arteriosclerosis. A total of 86 subjects were divided into three groups. The data of 1000 pulse cycles were selected in the experiment, and the multiscale cross-approximate entropy was calculated for the climb time and pulse wave peak interval. Independent sample t-test analysis gives the small-scale cross-approximate entropy of the two time series of climb time and pulse wave peak interval as p< 0.001 in Groups 1 and 2. The large-scale cross-approximate entropy of the two time series of climb time and pulse wave peak interval is p< 0.017 in Groups 2 and 3.
Results:
Using the proposed algorithm, the results showed that the small-scale cross-approximate entropy of climb time and pulse wave peak interval could reflect the degree of arteriosclerosis in the human body from the perspective of autonomic nerve function. The large-scale cross-approximate entropy of climb time and pulse wave peak interval confirmed the effect of diabetes on the degree of arteriosclerosis.
Conclusions:
The results demonstrate the multiscale cross-approximate entropy is a comprehensive index to evaluate the degree of human arteriosclerosis.
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