Classification of Impulse Oscillometric Patterns of Lung Function in Asthmatic Children using Artificial Neural Networks

Impulse oscillometry (IOS) is an innovative patient-friendly pulmonary testing technique which measures the respiratory system impedance (Z) by using the spectral components of pressure to flow ratio which yields resistance and reactance values at different frequencies. The high dimensionality of IOS measurement data makes the analysis of this information difficult. Artificial neural networks (ANNs) are mathematical models composed of a large number of highly interconnected neurons that are able to learn and generalize from data. An ANN-based approach to the analysis of IOS data can potentially provide an efficient and automatic method to recognize and classify pulmonary diseases. This would help characterize major respiratory illnesses such as asthma based on IOS measurements. Asthma can be difficult to diagnose, because the symptoms are sometimes similar to other lung conditions. A data set composed of 361 impulse oscillometric patterns from asthmatic children was used in this study. The ANN was capable of distinguishing between relatively constricted and nonconstricted airway conditions in these patients. Using all of the 361 patterns during training as well as in the feed-forward stage, a classification accuracy of 95.01% was obtained for validation. When the ANN was presented with only 60% of the original 361 patterns in the data set during training and with the remaining 40% as unseen patterns, the generalization stage, a classification accuracy of 98.61% was achieved. These results show that ANNs can successfully be trained with the IOS data, enabling them to generalize the IOS parameter relationships to classify previously unseen pulmonary patterns, such as in asthma. The next step is to obtain expert rules by extracting them from the knowledge acquired by the neural network and develop a fully automated classification system to aid physicians in classifying and characterizing pulmonary diseases based on the patient-friendly IOS measurements

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