[1]陆彦邑,尹军,毕玉田,等. 常见伤口感染细菌高场不对称波形离子迁移谱识别算法[J].中国医学物理学杂志,2018,35(2):205-209.[doi:DOI:10.3969/j.issn.1005-202X.2018.02.017]
　LU Yanyi,YIN Jun,BI Yutian,et al. Algorithmic identification for high-field asymmetric ion mobility spectra of common wound-infecting bacteria[J].Chinese Journal of Medical Physics,2018,35(2):205-209.[doi:DOI:10.3969/j.issn.1005-202X.2018.02.017]
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 常见伤口感染细菌高场不对称波形离子迁移谱识别算法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
35卷
期数:
2018年第2期
页码:
205-209
栏目:
医学信号处理与医学仪器
出版日期:
2018-02-08

文章信息/Info

Title:
 Algorithmic identification for high-field asymmetric ion mobility spectra of common wound-infecting bacteria
文章编号:
1005-202X（2018）02-0205-05
作者:
 陆彦邑; 尹军; 毕玉田; 严军; 黎敏; 何庆华
 第三军医大学第三附属医院野战外科研究所/国家创伤烧伤复合伤重点实验室， 重庆 400042
Author(s):
 LU Yanyi;  YIN Jun;  BI Yutian;  YAN Jun;  LI Min;  HE Qinghua
 State Key Laboratory of Trauma, Burns and Combined Injury/Surgery Institute of the Third Military Medical University, Chongqing 400042, China
关键词:
 ; 高场不对称波形离子迁移谱; 常见伤口感染细菌; 模式识别算法
Keywords:
 ; Keywords: high-field asymmetric ion mobility spectra;  common wound-infecting bacteria;  pattern recognition algorithm
分类号:
TN911.73
DOI:
DOI:10.3969/j.issn.1005-202X.2018.02.017
文献标志码:
A
摘要:
 目的：使用模式识别算法对常见伤口感染细菌（大肠杆菌、金黄色葡萄球菌、铜绿假单胞菌）TH肉汤培养液及纯TH培养液的高场不对称波形离子迁移谱（FAIMS图谱）进行分类识别。 方法：使用FAIMS分析仪收集了4种样品的训练及测试样本。预处理后，用主成分分析和线性判别分析对样本进行降维和特征提取，得到了训练集和测试集的空间三维分布模型，再用最近邻规则算法进行样本识别。 结果：特征提取后，正负模式样本均具有良好的分离效果，并且正模式可分性明显优于负模式。当K取合适的值时，正负模式识别率分别可达90%和70%以上。对于本文的数据模型，K取值等于或最接近每类样本数的奇数为最佳。 结论：该种算法可用于常见伤口感染细菌肉汤培养液FAIMS图谱的分类及识别。
Abstract:
 Objective To classify and recognize the high-field asymmetric ion mobility spectra (FAIMS) of common wound-infecting bacteria (escherichia coli, staphylococcus aureus and pseudomonas aeruginosa) cultured in thioglycolate (TH) broth and bare TH broth by pattern recognition algorithm. Methods The spectra of training set and testing set of four types of samples were obtained by FAIMS analyzer. After pretreatment, principal component analysis and linear discriminant analysis were used to reduce the dimension and extract features for obtaining the three-dimensional spatial didtribution patterns of training set and testing set. Finally, k-nearest neighbors algorithm was used to identify the four types of samples. Results After feature extraction, the samples showed good separation effects under both positive and negative modes, and the separability of positive mode was superior to that of negative mode. When K was appropriately assigned, the recognition rate under positive and negative modes could be up to 90% and 70%, respectively, or even higher. For the data model in this study, the optimal K was equal to the odd number closest to sample number of each type. Conclusion The proposed algorithm can be used to classify and recognize the FAIMS of common wound-infecting bacteria cultured in TH broth.

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备注/Memo

备注/Memo:
 【收稿日期】2017-10-17
【基金项目】国家国际科技合作专项（2014DFA31560）；重庆市科技研发基地建设计划（cstc2013gjhz10003）
【作者简介】陆彦邑，硕士，研究实习员，研究方向：生物医学信号处理，E-mail: 90911xbh@163.com
【通信作者】何庆华，博士，副研究员，研究方向：生物医学信号处理，E-mail: qinghuahe@126.com
更新日期/Last Update: 2018-01-29