Unknown

Dataset Information

0

Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling.


ABSTRACT: Recognizing specific heart sound patterns is important for the diagnosis of structural heart diseases. However, the correct recognition of heart murmur depends largely on clinical experience. Accurately identifying abnormal heart sound patterns is challenging for young and inexperienced clinicians. This study is aimed at the development of a novel algorithm that can automatically recognize systolic murmurs in patients with ventricular septal defects (VSDs). Heart sounds from 51 subjects with VSDs and 25 subjects without a significant heart malformation were obtained in this study. Subsequently, the soundtracks were divided into different training and testing sets to establish the recognition system and evaluate the performance. The automatic murmur recognition system was based on a novel temporal attentive pooling-convolutional recurrent neural network (TAP-CRNN) model. On analyzing the performance using the test data that comprised 178 VSD heart sounds and 60 normal heart sounds, a sensitivity rate of 96.0% was obtained along with a specificity of 96.7%. When analyzing the heart sounds recorded in the second aortic and tricuspid areas, both the sensitivity and specificity were 100%. We demonstrated that the proposed TAP-CRNN system can accurately recognize the systolic murmurs of VSD patients, showing promising potential for the development of software for classifying the heart murmurs of several other structural heart diseases.

SUBMITTER: Wang JK 

PROVIDER: S-EPMC7732853 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

altmetric image

Publications

Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling.

Wang Jou-Kou JK   Chang Yun-Fan YF   Tsai Kun-Hsi KH   Wang Wei-Chien WC   Tsai Chang-Yen CY   Cheng Chui-Hsuan CH   Tsao Yu Y  

Scientific reports 20201211 1


Recognizing specific heart sound patterns is important for the diagnosis of structural heart diseases. However, the correct recognition of heart murmur depends largely on clinical experience. Accurately identifying abnormal heart sound patterns is challenging for young and inexperienced clinicians. This study is aimed at the development of a novel algorithm that can automatically recognize systolic murmurs in patients with ventricular septal defects (VSDs). Heart sounds from 51 subjects with VSD  ...[more]

Similar Datasets

| S-EPMC4296393 | biostudies-literature
| S-EPMC4987479 | biostudies-literature
| S-EPMC8299768 | biostudies-literature
| S-EPMC9238637 | biostudies-literature
2014-02-10 | E-GEOD-54675 | biostudies-arrayexpress
| S-EPMC6200681 | biostudies-other
| S-EPMC4322411 | biostudies-literature
2014-02-10 | GSE54675 | GEO
| S-EPMC5218845 | biostudies-literature
| S-EPMC8311277 | biostudies-literature