Study on diagnostic methods of breathing disorders based on fuzzy logic inference and the neural network.
- Author:
Min CHEN
1
;
Xuezhi YIN
Author Information
1. Shanghai Medical Industry College, University of Shanghai for Science and Technology, Shanghai 200093, China. chenmin_521@126.com
- Publication Type:Journal Article
- MeSH:
Artificial Intelligence;
Fuzzy Logic;
Humans;
Neural Networks (Computer);
Respiration Disorders;
diagnosis
- From:
Chinese Journal of Medical Instrumentation
2011;35(4):260-262
- CountryChina
- Language:Chinese
-
Abstract:
This paper descries a new non-invasive method for diagnosis of breathing disorders based on adaptive-network-based fuzzy inference system (ANFIS). In this method, PetCO2, SpO2 and HR are chosen as inputs, and the breathing condition is selected as output ofANFIS. The inputs and output are then classified into fuzzy subsets by experts' knowledge. After, the fuzzy IF-THEN rules are built up according to the corresponding membership functions by set up of fuzzy subsets. The neural network was finally established and the membership functions and fuzzy rules were optimized by training. The results of experiment shows that ANFIS is more effective than BP Network regarding the diagnosis of breathing disorders.
