Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines

2006 ◽  
Vol 53 (5) ◽  
pp. 875-884 ◽  
Author(s):  
G. Georgoulas ◽  
D. Stylios ◽  
P. Groumpos
Keyword(s):  
Heart Rate ◽  
Metabolic Acidosis ◽  
Support Vector Machines ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Signal Classification ◽  
Support Vector ◽  
Vector Machines

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 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%.

Intrapartum Computerized Fetal Heart Rate Parameters and Metabolic Acidosis at Birth

2003 ◽  
Vol 102 (4) ◽  
pp. 731-738
Author(s):  
Sumit K. Agrawal ◽  
Fred Doucette ◽  
Robert Gratton ◽  
Bryan Richardson ◽  
Robert Gagnon
Keyword(s):  
Heart Rate ◽  
Metabolic Acidosis ◽  
Fetal Heart Rate ◽  
Fetal Heart

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.

Graded classification of fetal heart rate tracings: association with neonatal metabolic acidosis and neurologic morbidity

2010 ◽  
Vol 202 (3) ◽  
pp. 258.e1-258.e8 ◽  
Author(s):  
Colm Elliott ◽  
Philip A. Warrick ◽  
Ernest Graham ◽  
Emily F. Hamilton
Keyword(s):  
Heart Rate ◽  
Metabolic Acidosis ◽  
Fetal Heart Rate ◽  
Fetal Heart
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