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儿童青少年肺通气功能预测的后向传播神经网络方法

陈鑫, 张正国, 冯逵^*, 陈莉, 韩少梅, 朱广瑾

中国医学科学院基础医学研究所，北京协和医学院基础学院 生物医学工程系；生理学和病理生理学系；流行病学和统计学系，北京 100005

摘要

本文旨在研究儿童青少年肺通气功能预测的后向传播神经网络(backpropagation neural network, BPNN)方法，以期得到更准确的肺通气功能预计值。样本数据包括内蒙古自治区10~18岁汉族健康儿童青少年999人(男性500人，女性499人)，测量身高和体重，使用肺功能仪检测肺通气功能。利用BPNN和多元逐步回归，对用力肺活量(forced vital capacity, FVC)、用力呼气一秒量(forced expiratory volume in one second, FEV1)、最大呼气流量(peak expiratory flow, PEF)、用力呼出25%肺活量时呼气流量(forced expiratory flow at 25% of forced vital capacity, FEF25%)、用力呼出50%肺活量时呼气流量(forced expiratory flow at 50% of forced vital capacity, FEF50%)、最大呼气中段流量(maximal mid-expiratory flow, MMEF)、用力呼出75%肺活量时呼气流量(forced expiratory flow at 75% of forced vital capacity, FEF75%)，分性别建立BPNN预测模型和预计方程式，并利用均方差异(mean squared difference, MSD)和相关系数(R)评价BPNN、基于本工作所建立的线性回归方程(LR方程)、香港Ip等报道的Ip方程和国外较常用的Zapletal方程的预测准确程度。结果显示：无论性别，由BPNN所得各指标的预计值与实测值的MSD均小于其它各个预计方程式，且其预计值与实测值的R均大于其它各个预计方程式；由LR方程所得各个指标的预计值与实测值的MSD均小于Ip方程和Zapletal方程，且其R均大于Ip方程和Zapletal方程。FEF50%、MMEF、FEF75%等3个指标的变异系数(coefficient of variance, CV)均大于其它肺通气功能指标，而这3个指标由BPNN所得预计值和实测值的R较LR方程所得R的增幅ΔR(%)也相应大于其它指标。综上所述，进行肺通气功能预测的BPNN方法要优于传统的多元线性回归方法。肺通气指标的CV越大时，BPNN较传统回归方法的预测优势也越明显。

关键词： 用力呼气流量; 用力肺活量; 人工神经网络; 儿童; 青少年

分类号：R332

[Prediction of ventilatory function in children and adolescents using backpropagation neural networks.] [Article in Chinese]

CHEN Xin, ZHANG Zheng-Guo, FENG Kui^*, CHEN Li, HAN Shao-Mei, ZHU Guang-Jin

Department of Biomedical Engineering； Department of Physiology and Pathophysiology； Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China

Abstract

The aim of this study is to develop backpropagation neural networks (BPNN) for better prediction of ventilatory function in children and adolescents. Nine hundred and ninety-nine healthy children and adolescents (500 males and 499 females) aged 10–18 years, all of the Han Nationality, were selected from Inner Mongolia Autonomous Region, and their heights, weights, and ventilatory functions were measured respectively by means of physical examination and spirometric test. Using the approaches of BPNN and stepwise multiple regression, the prediction models and equations for forced vital capacity (FVC), forced expiratory volume in one second (FEV1), peak expiratory flow (PEF), forced expiratory flow at 25% of forced vital capacity (FEF25%), forced expiratory flow at 50% of forced vital capacity (FEF50%), maximal mid-expiratory flow (MMEF) and forced expiratory flow at 75% of forced vital capacity (FEF75%) were established. Through analyzing mean squared difference (MSD) and correlation coefficient (R) of the ventilatory function indexes, the present study compared the results of BPNN, linear regression equation based on this work (LR’s equation), prediction equations based on the studies of Ip et al. (Ip’s equation) and Zapletal et al. (Zapletal’s equation). The results showed, regardless of sex, the BPNN prediction models appeared to have smaller MSD and higher R values, compared with those from the other prediction equations; and the LR’s equation also had smaller MSD and higher R values compared with those from Ip’s and Zapletal’s equations. The coefficients of variance (CV) for FEF50%, MMEF and FEF75% were higher than those of the other ventilatory function parameters, and their increasing percentages of R values (ΔR, relative to R values by LR’s equation) derived by BPNN were correspondingly higher than those of the other indexes. In sum, BPNN approach for ventilatory function prediction outperforms the traditional regression methods. When CV of a certain ventilatory function parameter is higher, the superiority of BPNN would be more significant compared with traditional regression methods.

Key words: forced expiratory flow rates; forced vital capacity; artificial neural networks; child; adolescent

收稿日期：2011-01-18　　录用日期：2011-03-23

通讯作者：冯逵　　E-mail: fengkui@sina.com

引用本文：

陈鑫, 张正国, 冯逵, 陈莉, 韩少梅, 朱广瑾. 儿童青少年肺通气功能预测的后向传播神经网络方法[J]. 生理学报 2011; 63 (4): 377-386.

CHEN Xin, ZHANG Zheng-Guo, FENG Kui, CHEN Li, HAN Shao-Mei, ZHU Guang-Jin. [Prediction of ventilatory function in children and adolescents using backpropagation neural networks.] [Article in Chinese]. Acta Physiol Sin 2011; 63 (4): 377-386 (in Chinese with English abstract).