Incorporating Knowledge Base for Deep Classification of Fetal Heart Rate

Autor:	Changping Ji, Jianqiang Li, Min Fang, Muhammad Umair Raza, Jie Chen
Rok vydání:	2021
Předmět:	Fetal monitoring Noise Fetal heart rate Knowledge base Computer science business.industry Speech recognition Unbalanced data business Convolutional neural network Noisy data Task (project management)
Zdroj:	Intelligent Computing Theories and Application ISBN: 9783030845315 ICIC (3)
DOI:	10.1007/978-3-030-84532-2_12
Popis:	In recent years, remote fetal monitoring has become more and more popular, and it has also brought many challenges. Fetal heart rate records are generally recorded by pregnant women using a fetal monitor at home. Due to the improper operation of the pregnant woman and the surrounding noise, this makes it difficult for the doctor to give an accurate diagnosis. However, the existing methods are difficult to perform well in an environment with noisy data and unbalanced data. To solve the shortcomings of existing methods, we design a novel framework, classification fetal heart rate based on convolutional neural network incorporating knowledge base. In particular, we built a knowledge base for the task of fetal heart rate classification, which can solve the problem of noise and imbalance in the data. To verify the effectiveness of our proposed framework, we conduct extensive experiments on a real-world dataset. The experimental results show that the performance of our framework is better than other methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8f662654b171c05739134eb99d8660f8 https://doi.org/10.1007/978-3-030-84532-2_12 Zobrazit plný text záznamu