计算机科学 ›› 2015, Vol. 42 ›› Issue (10): 35-38.
陈星池,赵海,窦圣昶,李思楠,李大舟
CHEN Xing-chi, ZHAO Hai, DOU Sheng-chang, LI Si-nan and LI Da-zhou
摘要： 针对人体生理状态判别问题,提出从时域中提取脉搏周期和主波高度这2个参数作为支持向量机的输入特征向量,通过有监督学习的训练方法构建二分类模型,从脉搏的角度将人的生理状态分为普通状态和事件状态。通过人体在运动、睡眠、喝酒3种状态下的实验,对SVM的分类性能进行了统计分析和评价,并验证了SVM对人体生理状态判别具有良好的效果。
| [1] Angius G,Barcellona D,Cauli E,et al.Myocardial infarction and Antiphospholipid Syndrome:A first study on finger PPG waveforms effects [C]∥Computing in Cardiology.2012:517-520 [2] 李国正,王猛,曾华军.支持向量机导论[M].北京:电子工业出版社,2000:3-6 Li Guo-zheng,Wang Meng,Zeng Hua-jun.An Introduction to Support Vector Machines[M].Beijing:Publishing House of Electronics Industry,2000:3-6 [3] Przemysaw J,Tadeusz L.Automated Classification of Power- Quality Disturbances Using SVM and RBF Networks [J].IEEE Transactions on Power Delivery,2006,21(3):1663-1669 [4] Liu De-hua,Qian Hui,Dai Guang,et al.An iterative SVM approach to feature selection and classification in high-dimensional datasets [J].Pattern Recognition,2013,46(9):2531-2537 [5] Ghoggali N,Melgani F,Bazi Y.A Multiobjective Genetic SVM Approach for Classification Problems with Limited Training Samples [J].IEEE Transactions on Goscience and Remote Sen-sing,2009,47(6):1707-1711 [6] Cheng Cao,Tutwiler R L,Slobounov S.Automatic Classification of Athletes With Residual Functional Deficits Following Concussion by Means of EEG Signal Using Support Vector Machine[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2008,16(4):327-335 [7] Lopez J,Dorronsoro J R.Simple Proof of Convergence of theSMO Algorithm for Different SVM Variants [J].IEEE Transactions on Neural Networks and Learning Systems,2012,23(7):1142-1147 [8] Zhou S,Wang Ke.Localization site prediction for membraneproteins by integrating rule and SVM classification [J].IEEE Transactions on Knowledge and Data Engineering,2005,17(12):1694-1705 |
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