Intelligent classification of cardiotocography based on a support vector machine and convolutional neural network: Multiscene research.
Int J Gynaecol Obstet
; 165(2): 737-745, 2024 May.
Article
en En
| MEDLINE
| ID: mdl-38009598
ABSTRACT
OBJECTIVE:
To propose a computerized system utilizing multiscene analysis based on a support vector machine (SVM) and convolutional neural network (CNN) to assess cardiotocography (CTG) intelligently.METHODS:
We retrospectively collected 2542 CTG records of singleton pregnancies delivered at the maternity ward of the First Affiliated Hospital of Xi'an Jiaotong University from October 10, 2020, to August 7, 2021. CTG records were divided into five categories (baseline, variability, acceleration, deceleration, and normality). Apart from the category of normality, the other four different categories of abnormal data correspond to four scenes. Each scene was divided into training and testing sets at 91 or 73. We used three computer algorithms (dynamic threshold, SVM, and CNN) to learn and optimize the system. Accuracy, sensitivity, and specificity were performed to evaluate performance.RESULTS:
The global accuracy, sensitivity, and specificity of the system were 93.88%, 93.06%, and 94.33%, respectively. In acceleration and deceleration scenes, when the convolution kernel was 3, the test data set reached the highest performance.CONCLUSION:
The multiscene research model using SVM and CNN is a potential effective tool to assist obstetricians in classifying CTG intelligently.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Cardiotocografía
/
Máquina de Vectores de Soporte
Límite:
Female
/
Humans
/
Pregnancy
Idioma:
En
Revista:
Int J Gynaecol Obstet
Año:
2024
Tipo del documento:
Article
País de afiliación:
China