An Approach for Prediction of Acute Hypotensive Episodes via the Hilbert-Huang Transform and Multiple Genetic Programming Classifier
Acute hypotensive episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. This study presents a methodology to predict AHE for ICU patients based on big data time series. The experimental data we used is mean arterial pressure...
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| Format: | Article |
| Language: | English |
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Wiley
2015-08-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/354807 |
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| author | Dazhi Jiang Liyu Li Bo Hu Zhun Fan |
| author_facet | Dazhi Jiang Liyu Li Bo Hu Zhun Fan |
| author_sort | Dazhi Jiang |
| collection | DOAJ |
| description | Acute hypotensive episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. This study presents a methodology to predict AHE for ICU patients based on big data time series. The experimental data we used is mean arterial pressure (MAP), which is transformed from arterial blood pressure (ABP) data. Then, the Hilbert-Huang transform method was used to calculate patient's MAP time series and some features, which are the bandwidth of the amplitude modulation, the frequency modulation, and the power of intrinsic mode function (IMF), were extracted. Finally, the multiple genetic programming (Multi-GP) is used to build the classification models for detection of AHE. The methodology is applied in the datasets of the 10th PhysioNet and Computers Cardiology Challenge in 2009 and Multiparameter Intelligent Monitoring for Intensive Care (MIMIC-II). We achieve the accuracy of 83.33% in the training set and 91.89% in the testing set of the 2009 challenge's dataset and the 84.13% in the training set and 82.41% in the testing set of the MIMIC-II dataset. |
| format | Article |
| id | doaj-art-effda54121824d8abe44712cdd3aae29 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-effda54121824d8abe44712cdd3aae292025-08-20T03:06:27ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-08-011110.1155/2015/354807354807An Approach for Prediction of Acute Hypotensive Episodes via the Hilbert-Huang Transform and Multiple Genetic Programming ClassifierDazhi Jiang0Liyu Li1Bo Hu2Zhun Fan3 State Key Laboratory of Software Engineering, Computer School, Wuhan University, Wuhan 430074, China Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China Department of Electronic and Information Engineering, School of Engineering, Shantou University, Shantou 515063, ChinaAcute hypotensive episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. This study presents a methodology to predict AHE for ICU patients based on big data time series. The experimental data we used is mean arterial pressure (MAP), which is transformed from arterial blood pressure (ABP) data. Then, the Hilbert-Huang transform method was used to calculate patient's MAP time series and some features, which are the bandwidth of the amplitude modulation, the frequency modulation, and the power of intrinsic mode function (IMF), were extracted. Finally, the multiple genetic programming (Multi-GP) is used to build the classification models for detection of AHE. The methodology is applied in the datasets of the 10th PhysioNet and Computers Cardiology Challenge in 2009 and Multiparameter Intelligent Monitoring for Intensive Care (MIMIC-II). We achieve the accuracy of 83.33% in the training set and 91.89% in the testing set of the 2009 challenge's dataset and the 84.13% in the training set and 82.41% in the testing set of the MIMIC-II dataset.https://doi.org/10.1155/2015/354807 |
| spellingShingle | Dazhi Jiang Liyu Li Bo Hu Zhun Fan An Approach for Prediction of Acute Hypotensive Episodes via the Hilbert-Huang Transform and Multiple Genetic Programming Classifier International Journal of Distributed Sensor Networks |
| title | An Approach for Prediction of Acute Hypotensive Episodes via the Hilbert-Huang Transform and Multiple Genetic Programming Classifier |
| title_full | An Approach for Prediction of Acute Hypotensive Episodes via the Hilbert-Huang Transform and Multiple Genetic Programming Classifier |
| title_fullStr | An Approach for Prediction of Acute Hypotensive Episodes via the Hilbert-Huang Transform and Multiple Genetic Programming Classifier |
| title_full_unstemmed | An Approach for Prediction of Acute Hypotensive Episodes via the Hilbert-Huang Transform and Multiple Genetic Programming Classifier |
| title_short | An Approach for Prediction of Acute Hypotensive Episodes via the Hilbert-Huang Transform and Multiple Genetic Programming Classifier |
| title_sort | approach for prediction of acute hypotensive episodes via the hilbert huang transform and multiple genetic programming classifier |
| url | https://doi.org/10.1155/2015/354807 |
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