Assessment of arteriosclerosis based on multiscale cross approximate entropy of human finger pulse wave

Abstract

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.