基于遗传算法和最小二乘支持向量机的胎儿状态智能评估
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摘要
胎心宫缩图是一种临床常用的评估胎儿健康状况的电子监护技术,具有易受主观因素影响导致诊断率较低的缺点。为降低误诊率,辅助医生做出准确的医疗决策,本文提出了一种基于胎心率信号分析胎儿状态的智能评估方法。首先,本文将来自捷克技术大学—布尔诺大学医院公开数据库的信号进行预处理后,对其中的胎心率信号进行多模态特征提取,然后利用设计的基于k—最近邻遗传算法选择最优特征子集,最后采用最小二乘支持向量机法对其分类。实验结果显示,利用本文提出的方法对胎儿状态进行分类,其准确度可达91%,灵敏度为89%,特异度为94%,质量指标为92%,受试者工作特征曲线下面积为92%,具有较好的分类性能,可辅助临床医生对胎儿状态做出有效评估。
Cardiotocography(CTG) is a commonly used technique of electronic fetal monitoring(EFM) for evaluating fetal well-being,which has the disadvantage of lower diagnostic rate caused by subjective factors.To reduce the rate of misdiagnosis and assist obstetricians in making accurate medical decisions,this paper proposed an intelligent assessment approach for analyzing fetal state based on fetal heart rate(FHR) signals.First,the FHR signals from the public database of the Czech Technical University-University Hospital in Brno(CTU-UHB) was preprocessed,and the comprehensive features were extracted.Then the optimal feature subset based on the k-nearest neighbor(KNN) genetic algorithm(GA) was selected.At last the classification using least square support vector machine(LS-SVM) was executed.The experimental results showed that the classification of fetal state achieved better performance using the proposed method in this paper:the accuracy is 91%,sensitivity is 89%,specificity is 94%,quality index is 92%,and area under the receiver operating characteristic curve is 92%,which can assist clinicians in assessing fetal state effectively.
Cardiotocography(CTG) is a commonly used technique of electronic fetal monitoring(EFM) for evaluating fetal well-being,which has the disadvantage of lower diagnostic rate caused by subjective factors.To reduce the rate of misdiagnosis and assist obstetricians in making accurate medical decisions,this paper proposed an intelligent assessment approach for analyzing fetal state based on fetal heart rate(FHR) signals.First,the FHR signals from the public database of the Czech Technical University-University Hospital in Brno(CTU-UHB) was preprocessed,and the comprehensive features were extracted.Then the optimal feature subset based on the k-nearest neighbor(KNN) genetic algorithm(GA) was selected.At last the classification using least square support vector machine(LS-SVM) was executed.The experimental results showed that the classification of fetal state achieved better performance using the proposed method in this paper:the accuracy is 91%,sensitivity is 89%,specificity is 94%,quality index is 92%,and area under the receiver operating characteristic curve is 92%,which can assist clinicians in assessing fetal state effectively.
引文
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2程志厚,宋树良.胎儿电子监护学.北京:人民卫生出版社,2001:112-154.
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4叶海慧,葛明珠,王正平.胎心率监护计算机分析在临床的应用.中国妇幼保健,2007,22(7):904-906.
5 Zhao Z Z,Zhang Y,Deng Y J.A comprehensive feature analysis of the fetal heart rate signal for the intelligent assessment of fetal state.J Clin Med,2018,7(8):223.
6 Huang M,Hsu Y.Fetal distress prediction using discriminant analysis,decision tree,and artificial neural network.J Biomed Sci Eng,2012,5(9):526-533.
7陆尧胜,尤启杭,李晓东.基于模糊理论和欧氏距离自动分析胎儿状态.生物医学工程学杂志,2016,33(3):436-441,447.
8 Georgoulas G,Karvelis P,Spilka J,et al.Investigating pH based evaluation of fetal heart rate(FHR)recordings.Health Technol,2017,7(2):241-254.
9 Stylios C D,Georgoulas G,Karvelis P,et al.Least squares support vector machines for FHR classification and assessing the pH based categorization//XIV Mediterranean Conference on Medical and Biological Engineering and Computing.Paphos:Springer International Publishing,2016,57:1205-1209.
10 Comert Z,Kocamaz A F.A study based on gray level Cooccurrence matrix and neural network community for determination of hypoxic fetuses.Int Artif Intell Data Process Sym,2016,4(11):569-573.
11 Chudacek V,Spilka J,Bursa M,et al.Open access intrapartum CTG database.BMC Pregnancy Childbirth,2014,14(1):16-27.
12翟云,杨炳儒,曲武.不平衡类数据挖掘研究综述.计算机科学,2010,37(10):27-32.
13 Chawla N V,Bowyer K W,Hall L O,et al.SMOTE:synthetic minority over-sampling technique.J Artif Intell Res,2002,16(1):321-357.
14周志华.机器学习.北京:清华大学出版社,2016:321-385.
15 Goldberg D E.Genetic algorithm in search,optimization,and machine learning.Addison Wesley,1989,XIII(7):2104-2116.
16雷英杰,张善文.MATLAB遗传算法工具箱及应用.西安:西安电子科技大学出版社,2005:234-291.
17 Ukil A.Support vector machine.Comput Sci,2002,1(4):1-28.
18 Syukenj A K,Gestel T V,Brabanter J D,et al.Least square support vector machine.Euphytica,2002,2(2):1599-1604.
19 Kai M T.Confusion matrix.Boston:Springer US,2011:1-109.
20汪云云,陈松灿.基于AUC的分类器评价和设计综述.模式识别与人工智能,2011,24(1):64-71.
2程志厚,宋树良.胎儿电子监护学.北京:人民卫生出版社,2001:112-154.
3 Ayres-de-Campos D,Spong C Y,Chandraharan E,et al.FIGO consensus guidelines on intrapartum fetal monitoring:cardiotocography.International Journal of Gynecology&Obstetrics,2015,131(1):13-24.
4叶海慧,葛明珠,王正平.胎心率监护计算机分析在临床的应用.中国妇幼保健,2007,22(7):904-906.
5 Zhao Z Z,Zhang Y,Deng Y J.A comprehensive feature analysis of the fetal heart rate signal for the intelligent assessment of fetal state.J Clin Med,2018,7(8):223.
6 Huang M,Hsu Y.Fetal distress prediction using discriminant analysis,decision tree,and artificial neural network.J Biomed Sci Eng,2012,5(9):526-533.
7陆尧胜,尤启杭,李晓东.基于模糊理论和欧氏距离自动分析胎儿状态.生物医学工程学杂志,2016,33(3):436-441,447.
8 Georgoulas G,Karvelis P,Spilka J,et al.Investigating pH based evaluation of fetal heart rate(FHR)recordings.Health Technol,2017,7(2):241-254.
9 Stylios C D,Georgoulas G,Karvelis P,et al.Least squares support vector machines for FHR classification and assessing the pH based categorization//XIV Mediterranean Conference on Medical and Biological Engineering and Computing.Paphos:Springer International Publishing,2016,57:1205-1209.
10 Comert Z,Kocamaz A F.A study based on gray level Cooccurrence matrix and neural network community for determination of hypoxic fetuses.Int Artif Intell Data Process Sym,2016,4(11):569-573.
11 Chudacek V,Spilka J,Bursa M,et al.Open access intrapartum CTG database.BMC Pregnancy Childbirth,2014,14(1):16-27.
12翟云,杨炳儒,曲武.不平衡类数据挖掘研究综述.计算机科学,2010,37(10):27-32.
13 Chawla N V,Bowyer K W,Hall L O,et al.SMOTE:synthetic minority over-sampling technique.J Artif Intell Res,2002,16(1):321-357.
14周志华.机器学习.北京:清华大学出版社,2016:321-385.
15 Goldberg D E.Genetic algorithm in search,optimization,and machine learning.Addison Wesley,1989,XIII(7):2104-2116.
16雷英杰,张善文.MATLAB遗传算法工具箱及应用.西安:西安电子科技大学出版社,2005:234-291.
17 Ukil A.Support vector machine.Comput Sci,2002,1(4):1-28.
18 Syukenj A K,Gestel T V,Brabanter J D,et al.Least square support vector machine.Euphytica,2002,2(2):1599-1604.
19 Kai M T.Confusion matrix.Boston:Springer US,2011:1-109.
20汪云云,陈松灿.基于AUC的分类器评价和设计综述.模式识别与人工智能,2011,24(1):64-71.