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5 results on '"Li, Mengke"'

3. The Design and Implementation of Cardiotocography Signals Classification Algorithm Based on Neural Network

Author

Xu Yang, Taoyi Wang, Haijing Tang, and Li Mengke

Subjects
Support Vector Machine, Cardiotocography, Article Subject, Computer science, 0206 medical engineering, 02 engineering and technology, lcsh:Computer applications to medicine. Medical informatics, Convolutional neural network, General Biochemistry, Genetics and Molecular Biology, Pregnancy, 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), Humans, Preprocessor, Signal processing, General Immunology and Microbiology, Artificial neural network, Applied Mathematics, Signal Processing, Computer-Assisted, Workload, General Medicine, Heart Rate, Fetal, 020601 biomedical engineering, Support vector machine, Recurrent neural network, Modeling and Simulation, Calibration, embryonic structures, lcsh:R858-859.7, Female, 020201 artificial intelligence & image processing, Neural Networks, Computer, Algorithm, Algorithms, Research Article
Abstract

Mobile medical care is a hot issue in current medical research. Due to the inconvenience of going to hospital for fetal heart monitoring and the limited medical resources, real-time monitoring of fetal health on portable devices has become an urgent need for pregnant women, which helps to protect the health of the fetus in a more comprehensive manner and reduce the workload of doctors. For the feature acquisition of the fetal heart rate (FHR) signal, the traditional feature-based classification methods need to manually read the morphological features from the FHR curve, which is time-consuming and costly and has a certain degree of calibration bias. This paper proposes a classification method of the FHR signal based on neural networks, which can avoid manual feature acquisition and reduce the error caused by human factors. The algorithm will directly learn from the FHR data and truly realize the real-time diagnosis of FHR data. The convolution neural network classification method named “MKNet” and recurrent neural network named “MKRNN” are designed. The main contents of this paper include the preprocessing of the FHR signal, the training of the classification model, and the experiment evaluation. Finally, MKNet is proved to be the best algorithm for real-time FHR signal classification.

Published
2018

5. The Design and Implementation of Cardiotocography Signals Classification Algorithm Based on Neural Network.

Author

Tang, Haijing, Wang, Taoyi, Li, Mengke, and Yang, Xu

Subjects
FETAL heart rate monitoring, SIGNAL classification, CLASSIFICATION algorithms, NEURAL computers, FETAL development
Abstract

Mobile medical care is a hot issue in current medical research. Due to the inconvenience of going to hospital for fetal heart monitoring and the limited medical resources, real-time monitoring of fetal health on portable devices has become an urgent need for pregnant women, which helps to protect the health of the fetus in a more comprehensive manner and reduce the workload of doctors. For the feature acquisition of the fetal heart rate (FHR) signal, the traditional feature-based classification methods need to manually read the morphological features from the FHR curve, which is time-consuming and costly and has a certain degree of calibration bias. This paper proposes a classification method of the FHR signal based on neural networks, which can avoid manual feature acquisition and reduce the error caused by human factors. The algorithm will directly learn from the FHR data and truly realize the real-time diagnosis of FHR data. The convolution neural network classification method named "MKNet" and recurrent neural network named "MKRNN" are designed. The main contents of this paper include the preprocessing of the FHR signal, the training of the classification model, and the experiment evaluation. Finally, MKNet is proved to be the best algorithm for real-time FHR signal classification. [ABSTRACT FROM AUTHOR]

Published
2018
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