The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures | |
Chou, Yongxin2; Wang, Ping1; Feng, Yufeng3 | |
刊名 | IEEE Access |
2020 | |
卷号 | 8页码:4133-4148 |
关键词 | Blood Blood pressure Computational complexity Decision trees Gaussian distribution Mixtures Arterial blood pressure Classification results Gaussian functions Information redundancies Kolmogorov-Smirnov test Log-normal functions Morphological model Probabilistic neural networks |
ISSN号 | 2169-3536 |
DOI | 10.1109/ACCESS.2019.2958304 |
英文摘要 | The morphological modeling methods are efficient in quantifying the change of arterial blood pressure (ABP) waves. The related works focus on minimizing the modeling error but ignore the classification related modeling expression in practical applications. In this study, we explored the optimal modeling method for ABP wave related classifications. Two types of conventional models, Gaussian or Lognormal kernel function mixtures, were employed to quantitively describe the change of ABP signals, and the parameters of different models were engaged to train the different classifiers by probabilistic neural network (PNN) and random forest (RF) for identifying the ABP waves by age, gender, and whether belonging to extreme bradycardia (EB) or extreme tachycardia (ET). Then, we defined some indexes about the performance of modeling and classifications as the references to compare the different models. The ABP signals of Fantasia and 2015 PhysioNet/CinC Challenge databases were exploited as the experimental data to select the optimal model. The modeling results show that the Lognormal kernel function mixtures have a lower error in ABP wave modeling. The two-sample Kolmogorov-Smirnov test (ks-test) results indicate that the parameters of all models are markedly different at a highly significant level (h = 1, p © 2013 IEEE. |
WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
语种 | 英语 |
出版者 | Institute of Electrical and Electronics Engineers Inc., United States |
WOS记录号 | WOS:000531571500001 |
内容类型 | 期刊论文 |
源URL | [http://ir.lut.edu.cn/handle/2XXMBERH/115714] |
专题 | 电气工程与信息工程学院 |
作者单位 | 1.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; 2.School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou; 215500, China; 3.Changshu No.1 People's Hospital, Changshu; 215500, China |
推荐引用方式 GB/T 7714 | Chou, Yongxin,Wang, Ping,Feng, Yufeng. The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures[J]. IEEE Access,2020,8:4133-4148. |
APA | Chou, Yongxin,Wang, Ping,&Feng, Yufeng.(2020).The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures.IEEE Access,8,4133-4148. |
MLA | Chou, Yongxin,et al."The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures".IEEE Access 8(2020):4133-4148. |
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