scholarly journals A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State

2018 ◽  
Vol 7 (8) ◽  
pp. 223 ◽  
Author(s):  
Zhidong Zhao ◽  
Yang Zhang ◽  
Yanjun Deng
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Medical Decision ◽  
Support Vector ◽  
Visual Interpretation ◽  
Adaptive Boosting ◽  
Original Dataset

Continuous monitoring of the fetal heart rate (FHR) signal has been widely used to allow obstetricians to obtain detailed physiological information about newborns. However, visual interpretation of FHR traces causes inter-observer and intra-observer variability. Therefore, this study proposed a novel computerized analysis software of the FHR signal (CAS-FHR), aimed at providing medical decision support. First, to the best of our knowledge, the software extracted the most comprehensive features (47) from different domains, including morphological, time, and frequency and nonlinear domains. Then, for the intelligent assessment of fetal state, three representative machine learning algorithms (decision tree (DT), support vector machine (SVM), and adaptive boosting (AdaBoost)) were chosen to execute the classification stage. To improve the performance, feature selection/dimensionality reduction methods (statistical test (ST), area under the curve (AUC), and principal component analysis (PCA)) were designed to determine informative features. Finally, the experimental results showed that AdaBoost had stronger classification ability, and the performance of the selected feature set using ST was better than that of the original dataset with accuracies of 92% and 89%, sensitivities of 92% and 89%, specificities of 90% and 88%, and F-measures of 95% and 92%, respectively. In summary, the results proved the effectiveness of our proposed approach involving the comprehensive analysis of the FHR signal for the intelligent prediction of fetal asphyxia accurately in clinical practice.

scholarly journals FETAL TYPE CLASSIFICATION BASED ON FETAL ELECTROCARDIOGRAPH IN NON-STRESS TESTS

2021 ◽  
pp. 2140052
Author(s):  
JIANLI LIU ◽  
YIMIN YANG ◽  
SONG ZHANG ◽  
XUWEN LI ◽  
LIN YANG ◽  
...  
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Classification Model ◽  
Support Vector ◽  
Stress Tests ◽  
Nonlinear Parameters ◽  
Design Algorithm ◽  
Long Time

Electronic fetal heart rate (FHR) monitoring is a technical means to evaluate the state of the fetus in the uterus by monitoring FHR. The main purpose is to detect intrauterine hypoxia and take corresponding medical measures timely. Because the fetus sleeps quietly for up to 1 hour sometimes, ultrasound Doppler is not easy to continuously detect for a long time. The electronic fetal monitor obtains the fetal heart rate, which not only improves the accuracy and comfort, but also the convenient implementation of long-term monitoring. It is beneficial to reduce perinatal fetal morbidity and mortality. This study used maternal–fetal Holter monitor which is based on the technology of fetal electrocardiograph (FECG) to collect the FHR, and then design algorithm to extract the baseline FHR, acceleration, variation, sleep-wake cycle and nonlinear parameters. There were significant differences in the 22 parameters between the normal and the suspicious group. Using the 22 characteristic parameters, the support vector machine was used to classify the normal and the suspected group of fetuses. 80% of the data was used to train a classification model. The remaining 20% of the data was used as a test set and its accuracy reached 93.75%.

Sparse Support Vector Machine for Intrapartum Fetal Heart Rate Classification

2017 ◽  
Vol 21 (3) ◽  
pp. 664-671 ◽  
Author(s):  
Jiri Spilka ◽  
Jordan Frecon ◽  
Roberto Leonarduzzi ◽  
Nelly Pustelnik ◽  
Patrice Abry ◽  
...  
Keyword(s):  
Heart Rate ◽  
Support Vector Machine ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Support Vector

FEATURE EXTRACTION AND CLASSIFICATION OF FETAL HEART RATE USING WAVELET ANALYSIS AND SUPPORT VECTOR MACHINES

2006 ◽  
Vol 15 (03) ◽  
pp. 411-432 ◽  
Author(s):  
GEORGE GEORGOULAS ◽  
CHRYSOSTOMOS STYLIOS ◽  
PETER GROUMPOS
Keyword(s):  
Heart Rate ◽  
Feature Extraction ◽  
Support Vector Machines ◽  
Fetal Heart Rate ◽  
Umbilical Artery ◽  
Fetal Heart ◽  
Support Vector ◽  
Ph Values ◽  
Vector Machines

Since the fetus is not available for direct observations, only indirect information can guide the obstetrician in charge. Electronic Fetal Monitoring (EFM) is widely used for assessing fetal well being. EFM involves detection of the Fetal Heart Rate (FHR) signal and the Uterine Activity (UA) signal. The most serious fetal incident is the hypoxic injury leading to cerebral palsy or even death, which is a condition that must be predicted and avoided. This research work proposes a new integrated method for feature extraction and classification of the FHR signal able to associate FHR with umbilical artery pH values at delivery. The proposed method introduces the use of the Discrete Wavelet Transform (DWT) to extract time-scale dependent features of the FHR signal and the use of Support Vector Machines (SVMs) for the categorization. The proposed methodology is tested on a data set of intrapartum recordings were the FHR categories are associated with umbilical artery pH values, This proposed approach achieved high overall classification performance proving its merits.

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.

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