Research on Cardiovascular Disease Classification and Recognition Method Based on Deep Learning
- Resource Type
- Conference
- Authors
- Zhang, Shaorui; Yu, Junsheng
- Source
- 2023 Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC) Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC), 2023. :1-3 Nov, 2023
- Subject
- Computing and Processing
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Signal Processing and Analysis
Wireless communication
Deep learning
Neural networks
Predictive models
Data models
Physiology
Cardiovascular diseases
DenseNet;Crdiovascular Dsease
Resnet
Pulse waves
- Language
- ISSN
- 2377-8512
With the continuous increase in the number of patients with cardiovascular diseases worldwide, it is of great significance to effectively and non-invasive identify cardiovascular diseases. Pulse waves contain rich physiological information in the human body and can effectively reflect cardiovascular function. We collected pulse wave data from cardiovascular disease patients aged 30–80 and normal individuals, and trained them using different neural networks based on this data. The results demonstrate that DenseNet had the best prediction with an accuracy of 76.62%.