Geographical distribution of left ventricular Tei index based on principal component analysis
10.11817/j.issn.1672-7347.2014.11.010
- VernacularTitle:基于主成分分析的左室Tei指数参考值的地理分布
- Author:
Jinhui XU
;
Miao GE
;
Jinwei HE
;
Ranyin XUE
;
Shaofang YANG
;
Jilin JIANG
- Publication Type:Journal Article
- Keywords:
Tei index;
correlation analysis;
principal component analysis;
spatial distribution regularities
- From:
Journal of Central South University(Medical Sciences)
2014;(11):1163-1169
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To provide a scientific standard of left ventricular Tei index for healthy people from various region of China, and to lay a reliable foundation for the evaluation of left ventricular diastolic and systolic function. Methods: The correlation and principal component analysis were used to explore the left ventricular Tei index, which based on the data of 3 562 samples from 50 regions of China by means of literature retrieval. hTe nine geographical factors were longitude(X1), latitude(X2), altitude(X3), annual sunshine hours (X4), the annual average temperature (X5), annual average relative humidity (X6), annual precipitation (X7), annual temperature range (X8) and annual average wind speed (X9). ArcGIS sotfware was applied to calculate the spatial distribution regularities of letf ventricular Tei index. Results: hTere is a signiifcant correlation between the healthy people’s letf ventricular Tei index and geographical factors, and the correlationcoeffcients were 0.107 (r1), 0.301 (r2), 0.029 (r3), 0.277 (r4),?0.256(r5),?0.289(r6),?0.320(r7), 0.310 (r8) and 0.117 (r9), respectively. A linear equation between the Tei index and the geographical factor was obtained by regression analysis based on the three extracting principal components. hTe geographical distribution tendency chart for healthy people’s letf Tei index was iftted out by the ArcGIS spatial interpolation analysis. Conclusion: hTe geographical distribution for letf ventricular Tei index in China follows certain pattern. hTe reference value in North is higher than that in South, while the value in East is higher than that in West.
