The Non-linear Method of Deriving 12-Lead ECG from 3-Lead ECG

碩士 === 中原大學 === 生物醫學工程研究所 === 98 === Inferring accurately the standard 12-lead ECG with less leads should be the future trend, but ECG based on inferring, if interfered, may have a greater impact on inferring a multiple lead ECG waveform. For most patients, the derived 12-lead ECG from the use of fi...

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Bibliographic Details
Main Authors: Shang-Chi Chou, 周尚齊
Other Authors: Ching-Sung Weng
Format: Others
Language:zh-TW
Published: 2011
Online Access:http://ndltd.ncl.edu.tw/handle/26500563748101112877
Description
Summary:碩士 === 中原大學 === 生物醫學工程研究所 === 98 === Inferring accurately the standard 12-lead ECG with less leads should be the future trend, but ECG based on inferring, if interfered, may have a greater impact on inferring a multiple lead ECG waveform. For most patients, the derived 12-lead ECG from the use of fixed coefficients may be very close to the standard 12-lead ECG, but for others, the two results may be greatly different. Therefore, in terms of the derived ECG, how to design a precise algorithm and reduce the individual differences is a very important issue. The main purpose of this study was to use the global search characteristics of genetic algorithm to process weighting optimization of the neural network, as a method of three lead ECG inferring 12-lead ECG nonlinear. After optimizing the neural network, the database from PTB 249 patients (including: myocardial infarction, cardiomyopathy, heart failure, bundle branch block, arrhythmia, cardiac hypertrophy, valvular heart disease, myocarditis and so on ), which were divided into a total of 549 groups of fifteen-lead ECG, were inferred and verified. The results were compared with those inferred by the multiple linear regression and commission machine. The results showed that the neural network after optimization surpassed the multiple linear regression method in terms of the two indicators: root mean square error and correlation coefficient (root mean square error of neural network after optimizing genetic algorithm: 0.073 ± 0.04, correlation coefficient: 0.898 ± 0.043; root mean square error of multiple linear regression: 0.083 ± 0.05, correlation coefficient: 0.858 ± 0.066). Also, when compared with the neural network commonly used to improve the generalization ability, the results were close (root mean square error of commission machine : 0.073 ± 0.039, correlation coefficient: 0.895 ± 0.047), but the computation time was significantly shorter (when members of the commission were increasing, the gap between the computing time was more obvious). This indicated that the method proposed by the study indeed could infer a satisfactory standard 12-lead ECG by using the three lead. The method could be applied to the future design of immediate cardiograph systems for home nursing.