Evaluation of Flow–Volume Spirometric Test Using Neural Network Based Prediction and Principal Component Analysis

Abstract

In this work, an attempt has been made to enhance the diagnostic relevance of spirometric pulmonary function test using neural networks and Principal Component Analysis (PCA). For this study, flow-volume curves (N = 175) using spirometers were generated under standard recording protocol. A method based on neural network is used to predict the most significant parameter, FEV(1). Further, PCA is used to analyze the interdependency of the parameters in the measured and predicted datasets. Results show that the back propagation neural network is able to predict FEV(1) both in normal and abnormal cases. The variation in the magnitude and direction of parameters in the contribution of the principal components shows that FEV(1) is a significant discriminator of normal and abnormal datasets and is further confirmed by the percentage variance in the first few principal components. It appears that this method of prediction and principal component analysis on the measured and predicted datasets could be useful for spirometric pulmonary function test with incomplete data.

Area under the expiratory flow-volume curve: predicted values by artificial neural networks

“…In addition, we found that this methodology outperformed more advanced regression regularization techniques in reducing the bias and the dispersion of the residuals. Nowadays, in an era of exploding computational capabilities, neural networks represent the backbone of many emerging artificial intelligence techniques, which could successfully be applied in our field [13][14][15][16] .…”

Comparison of Intelligent Computing Techniques for Classification of Clinical EEG Signals

“…The performance of the developed classifiers was estimated using False Positive (FP), False Negative (FN), True Positive (TP) and True Negative (TN) values [10]. Classification of a normal data as abnormal is considered as FP and classification of abnormal data as normal is considered FN.…”

“…PCA is a statistical method used to transform the input space into a new lower dimensional space. PCA technique has been investigated before by researchers for signal and image processing [10].…”

“…Such applications have been developed for signal processing, automatic control, information retrieval, database management and data classification [16,17,18,19,20,21]. Successful implementations of ANFIS in biomedical engineering have been reported, for classification [10,16,18], for modeling and controlling real systems [21] and data analysis [22]. Particle swarm optimization (PSO) is an evolutionary optimization technique motivated by the simulation of social behavior [23].…”