Detection of Obstructive Respiratory Abnormality Using Flow–Volume Spirometry and Radial Basis Function Neural Networks

Abstract

In this work detection of pulmonary abnormalities carried out using flow-volume spirometer and Radial Basis Function Neural Network (RBFNN) is presented. The spirometric data were obtained from adult volunteers (N=100) with standard recording protocol. The pressure and resistance parameters were derived using the theoretical approximation of the activation function representing pressure-volume relationship of the lung. The pressure-time and resistance-expiration volume curves were obtained during maximum expiration. The derived values together with spirometric data were used for classification of normal and obstructive abnormality using RBFNN. The results revealed that the proposed method is useful for detecting the pulmonary functions into normal and obstructive conditions. RBFNN was found to be effective in differentiating the pulmonary data and it was confirmed by measuring accuracy, sensitivity, specificity and adjusted accuracy. As spirometry still remains central in the observations of pulmonary function abnormalities these studies seems to be clinically relevant.

Detection of Respiratory Abnormalities Using Artificial Neural Networks

“…These transducers have already been used for precise flow-volume measurements with high accuracy and stability [15] . The acceptability and reproducibility criteria were adopted as per the recommendations given by American Thoracic Society (ATS ) [11,16] . Spirometric data on Vital Capacity (VC), Forced Capacity (FVC), Forced Expiratory Volume in 1st second (FEV1), ratio of FEV1 to FVC (FEV1%), ratio of FEV1 to VC and Peak Expiratory Flow (PEF) were collected.…”

“…In other words, when a problem is investigated by means of a number of neural networks, the final result is more reliable and suffers less error since malfunctioning of a network may be compensated by other networks and also each network receives inputs different from that of others and thus may yield more accurate results [10,13,14,15,17,19] . Data were divided into two groups (train and test) randomly.…”

“…Although experimental rules may determine the number of neurons needed in these layers, the best approach is that of trial and error [15,20] . The next point to be taken into consideration is the fact that neural networks should be trained using appropriate number of training cycles; otherwise, it may result in underestimation or overestimation [21,16] .…”

“…On the other hand, the memory nodes and feedback connections increase its computational complexity [12] . In 2007, Mahesh Veezhinathan and Swaminathan Ramakrishnan carried out a study to detect obstructive respiratory abnormalities using Radial Basis Function artificial networks in which the results showed that the proposed method is useful for classifying the pulmonary functions into normal and obstructive conditions [13] . After a while and in a more recent study, they developed another research for the classification and detection of restrictive respiratory abnormalities by means of artificial neural networks.…”

Response of Forced Expiratory Volume/Forced Vital Capacity Ratio to Acapella Device and Breathing Exercises After Upper Abdominal Surgeries

“…Pulmonary function tests are often performed using spirometry and based on sex, height, weight and age. When the patient performs the test, actual results will be compared with the predicted value expected of a person of gender, height, and age to see if he or she falls within the "normal" range, or has a restrictive or obstructive Breathing Exercises After Upper Abdominal Surgeries component based on the results [4].…”