scholarly journals Neural Network Analysis and Evaluation of the Fetal Heart Rate

Algorithms ◽  
2009 ◽  
Vol 2 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Yasuaki Noguchi ◽  
Fujihiko Matsumoto ◽  
Kazuo Maeda ◽  
Takashi Nagasawa
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Network Analysis ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Neural Network Analysis ◽  
Analysis And Evaluation

scholarly journals Neural network analysis of heart rhythm variability for diagnosis of immobilization syndrome and objectivization of effectiveness of early rehabilitation

2020 ◽  
Vol 23 (1) ◽  
pp. 9-17
Author(s):  
Julia Yu. Nekrasova ◽  
D. S. Yankevich ◽  
М. М. Kanarsky ◽  
A. S. Markov
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Intensive Care ◽  
Network Analysis ◽  
Heart Rhythm ◽  
Neural Network Analysis ◽  
Heart Rhythm Variability ◽  
Post Intensive Care Syndrome

The article discusses the use of a neural network analysis of heart rate variability for the diagnosis of immobilization syndrome and post-intensive care syndrome (PICS) in patients with disorders of consciousness for monitoring the quality of the rehabilitation process. It is shown that there are statistical differences between the curves characterizing the heart rate variability of healthy patients and patients with impaired consciousness. The use of a neural network allows to automatically evaluate the severity of the immobilization syndrome and Post Intensive Care Syndrome, as well as the effectiveness of measures for their prevention and the overall quality of the work of medical personnel.

scholarly journals DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Zhidong Zhao ◽  
Yanjun Deng ◽  
Yang Zhang ◽  
Yefei Zhang ◽  
Xiaohong Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Convolutional Neural Network ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Geometric Mean ◽  
Area Under The Curve ◽  
Classification Performance ◽  
Visual Interpretation ◽  
Feature Extraction And Selection

Abstract Background Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer variability. Objective: Therefore, computer aided diagnosis (CAD) systems based on advanced artificial intelligence (AI) technology have recently been developed to assist obstetricians in making objective medical decisions. Methods In this work, we present an 8-layer deep convolutional neural network (CNN) framework to automatically predict fetal acidemia. After signal preprocessing, the input 2-dimensional (2D) images are obtained using the continuous wavelet transform (CWT), which provides a better way to observe and capture the hidden characteristic information of the FHR signals in both the time and frequency domains. Unlike the conventional machine learning (ML) approaches, this work does not require the execution of complex feature engineering, i.e., feature extraction and selection. In fact, 2D CNN model can self-learn useful features from the input data with the prerequisite of not losing informative features, representing the tremendous advantage of deep learning (DL) over ML. Results Based on the test open-access database (CTU-UHB), after comprehensive experimentation, we achieved better classification performance using the optimal CNN configuration compared to other state-of-the-art methods: the averaged ten-fold cross-validation of the accuracy, sensitivity, specificity, quality index defined as the geometric mean of the sensitivity and specificity, and the area under the curve yielded results of 98.34, 98.22, 94.87, 96.53 and 97.82%, respectively Conclusions Once the proposed CNN model is successfully trained, the corresponding CAD system can be served as an effective tool to predict fetal asphyxia objectively and accurately.

Sign in / Sign up

Export Citation Format

Share Document