Classification of Respiratory Abnormalities Using Adaptive Neuro Fuzzy Inference System

被引:0
作者
Asaithambi, Mythili [1 ]
Manoharan, Sujatha C. [2 ]
Subramanian, Srinivasan [1 ]
机构
[1] Anna Univ, Dept Instrumentat Engg, MIT Campus, Madras 600025, Tamil Nadu, India
[2] Anna Univ, Dept Elect & Commun Engn, Chennai, Tamil Nadu, India
来源
INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2012), PT III | 2012年 / 7198卷
关键词
Flow-volume spirometry; forced expiratory maneuver; Adaptive Neuro Fuzzy Inference System; Multiple Adaptive Neuro Fuzzy Inference System; Complex-valued Adaptive Neuro Fuzzy Inference System;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Spirometric evaluation of pulmonary function plays a critical role in the diagnosis, differentiation and management of respiratory disorders. In spirometry, there is a requirement that a large database is to be analyzed by the physician for effective investigation. Hence, there is a need for automated evaluation of spirometric parameters to diagnose respiratory abnormalities in order to ease the work of the physician. In this work, a neuro fuzzy based Adaptive Neuro Fuzzy Inference System (ANFIS), Multiple ANFIS and Complex valued ANFIS models are employed in classifying the spirometric data. Four different membership functions which include triangular, trapezoidal, Gaussian and Gbell are employed in these classification models. Results show that all the models are capable of classifying respiratory abnormalities. Also, it is observed that CANFIS model with Gaussian membership function performs better than other models and achieved higher accuracy. This study seems to be clinically relevant as this could be useful for mass screening of respiratory diseases.
引用
收藏
页码:65 / 73
页数:9
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