| Nowadays, cardiovascular disease has become the largest killer of the national health and electrocardiogram detection is an important method for the diagnosis of cardiovascular disease. P-wave is one of the electrophysiological indicators of electrocardiogram and this indicator has great value for the diagnosis and prediction of atrial fibrillation. P-wave mainly includes P-wave maximum (Pmax) and P-wave dispersion (Pd). In the clinical, the majority of medical workers approve of 110ms and 40ms as the reference value of Pmax and Pd. However, there are some problems of this in the practical application. On one hand,110ms and 40ms is not the uniform results. On the other hand, doctors use the same reference in different areas and the lass of various values in various areas has seriously affected the accuracy of clinical diagnosis. In addition, extensive research has been conducted by medical community on the influence factors of Pmax and Pd, including intrinsic factors such as age, gender and physical characteristics, as well as the external environment factors such as seasonal variation, air temperature, altitude mineral bath and so on. Existing researchs show that P-wave is not only affected by physical condition, the effects of natural geographical environment on it cannot be ignored either. So this article selects Pmax and Pd reference value for research to make up the lack of overlooking the geographical factors when formulate the medical references. And also this article aims to analyze how geographical factors affect P-wave refercence value of healthy people, explore the principle how geographical factors affect P-wave reference value.Through literature search and some other survey methods, this article collects 9357 measured data of Pd and 7600 measured data of Pmax, and deviede the data into three age groups——young, middle-aged and middle elderly. Based on the existing research materials and geographical environment characteristics in our country, this article selects 9 geographical factors to do research, including altitude(X1), annual sunshine duration(X2), annual average temperature(X3), annual average relative humidity(X4), annual rainfall(X5), annual temperature range(X6), annual average wind speed(X7), clay cation exchange capacity(X8) and silt cation exchange capacity (X9). Through correlation analysis (including single correlation analysis, partial correlation analysis and multiple correlation analysis), this article derived the correlation geographical factors from the database and analyze how these geographical factors affect P-wave reference value. On the basis of correlation analysis, make multiple linear regression analysis and curve estimation analysis between P-wave and geographical factors. In addition, by using combination forecasting, this article gets comnination curve models, which containis more than one geographical factor. After comparing the prediction accuracy of the models, select the optimum forecast model of each P-wave indicator and ues the models to predict P-wave reference values of 2322 cities in China. The distribution maps of Pmax and Pd reference values of different age groups could be fitted out by disjunctive kringing interpolation of ArcGIS10.0 software.Summarizes the results of correlation analysis and prediction model building. The influencial geographical factors and optimum forecast models of each age group are as follows:(1) Youth Pd:annual average temperature (-0.410,0.007), annual rainfall (-0.408, 0.007) and clay cation exchange capacity (0.487,0.001). Optimum forecast models:(?)=3.285+2.333Xs-0.06431X82+0.009895X83(2) Middle-aged Pd:annual average temperature (-0.248,0.021) and annual rainfall (-0.248,0.021). Optimum forecast models:(?)= 24.07+0.01405X5-0.000007374X52(3) Middle and elderly Pd:altitude (0.231,0.032), annual average temperature (-0.227,0.036) and annual wind speed (-0.214,0.048). Optimum forecast models:(?)=0.331Y3+0.331Y5+0.338Y7=36.42-0.9421X3+0.07409X32-0.001707X33-0.007911X5+(8.705E-7)X52-(2.786E-9)X53-1.376X7+0.2009X72(4) Yourh Pmax:annual average temperature (-0.520,0.009), annual average relative humidity (-0.452,0.009), annual temperature range (0.498,0.013), clay cation exchange capacity (-0.414.0.040) and silt cation exchange capacity (0.528,0.008). Optimum forecast models:(?)=111.9+2.028X3-0.9389X4+0.9185X6-0.2072X8 +0.7968X9±17.58(5) Middle-aged Pmax:altitude (0.323,0.002) and silt cation exchange capacity (-0.275,0.011). Optimum forecast models:F=103.7+0.004079X1-0.3129X9+1.560(6) Middle and elderly Pmax:annual average relative humidity (0.216,0.047), annual rainfall (0.249,0.021) and annual temperature range (-0.221,0.042). Optimum forecast models:Y=107.3*0.9981X6This article selects healthy youth, middle-aged and elderly people as research objects, analyzes the relationships between geographical factors and P-wave reference value. Establishes forecast models by different methods and seleces the optimum ones to ensure the prediction accuracy and practicability of the models. The distribution maps can clearly reveal the geography distribution of Pmax and Pd reference values, providing scientific basis for making various Pmax and Pd reference values in various areas. |
PDF Full Text Request