The goal of this study was to determine if the characteristics of voluntary coughs could be used to distinguish between individuals with normal and abnormal lung function. The approach was to measure a wide variety of features describing both the acoustical and airflow characteristics of a voluntary cough in both the time and frequency domains. It should be pointed out that the features were selected arbitrarily and there was no attempt to optimize their selection. Once they were determined, all the features were normalized with respect to their maximum values. The next step was to use a principal component analysis to eliminate redundant information contained in the feature set. Then, the principal components of the features were used to define a reduced number of orthogonal vectors representing each cough.
A unique approach for developing a classifier for categorizing voluntary coughs was used that was based on the subspace projection of the principal components into a vector space. One of the most important parameters of the classifier was determining K, the number of principal components needed in the analysis. The initial expectations were that the results would be more accurate using the highest value of K. This was not the case, however, and inclusion of some of the cough parameters appeared to increase noise. It was found in preliminary experiments that increasing K to preserve 95% of the energy contained in the data sets enhanced the performance of the classifier. In contrast, however, for both female and male groups, the classifier performance deteriorated when K was increased to preserve 99% of the energy in the cough parameters.
Due to the limited number of samples, the classifier was trained using all the data from all the subjects in each group except one. The coughs of that subject were evaluated using the trained system. This process was repeated for each member of the male and female test groups.
An analysis of the overall performance of our optimal classification system showed that there were 3 misclassifications within the group of the 58 male subjects. There were 0 subjects with normal lung function that were classified as having abnormal lung function and 3 subjects who had abnormal lung function but were identified as having normal lung function. Out of the total population of 54 women subjects, 3 were misclassified. There were 0 subjects with normal lung function who were classified incorrectly and 3 subjects with abnormal lung function who were recognized as having normal lung function. Figure 5 shows the sensitivity and specificity of the cough analysis method for detecting abnormal lung function in male and female test subjects. The classification criteria can be chosen so that a sensitivity and specificity can be selected depending upon the type of errors that are acceptable for a given testing scheme.
Even though the original feature set was reduced by choosing the largest eigenvectors during the classification process, optimization of the selection of the feature set as well as different methods of feature normalization remains an area of research to be explored. It should also be pointed out that only one type of classifier was tested in the present study. It is possible that for a given feature set, other classifiers using neural networks, genetic algorithms, etc., may provide even better results.
Under certain circumstances, using cough airflow and sound analysis to detect abnormal lung function has several advantages compared with conventional pulmonary function testing methods. First, cough analysis may be useful as a screening method to quickly evaluate changes in lung function of a large population of test subjects in a short period of time. Future studies should evaluate the utility of cough analysis in early disease detection. Experience has shown that subjects show little reluctance to performing a voluntary cough for testing purposes. The procedure is performed easily and quickly and requires a minimum of training since test subjects are usually very familiar with a voluntary cough maneuver. Another advantage is that voluntary coughs can be performed by the very young, the physically challenged, and geriatric subjects who may not be able to easily perform conventional pulmonary function tests. It is also possible that cough feature analysis can be useful in tracking the progression or recovery of pulmonary disorders without performing more strenuous flow-volume tests.
In the future voluntary coughs could be used to distinguish between types of pulmonary disorders such as obstructive and restrictive lung diseases. There is some preliminary evidence that voluntary cough characteristics may be related to changes in specific airway resistance in animals  which may also hold true for humans. It should be noted that the accuracy of cough feature analysis could still be improved in a variety of ways. For instance, new features may be identified and extracted to provide additional information and increase the accuracy of the classification system. The acoustic and airflow features could be fused at different levels to improve accuracy , and existing features that add noise, but contribute little information to the classification system, could be eliminated . Preliminarily experiments have shown that fusion of the data at the feature level  improved the performance of the classifier.
A limitation of this study is that variables such as age, body height, body weight and race, which are known to have an effect on forced pulmonary function indices, were not considered when classifying coughs from test subjects. These factors have been shown to be important when calculating percent predicted values of many pulmonary function indices. As additional test results involving voluntary cough analysis become available, consideration of these parameters should lead to an increased ability of the cough analysis system to discriminate between groups of subjects with normal and abnormal lung function.
It is possible that more appropriate features may be extracted from the data and that other features that do not contribute or even reduce the classification accuracy of the system can be eliminated. However, the classification technique presented in this research provides a highly accurate method of distinguishing between subjects with normal and abnormal lung function based on voluntary cough characteristics.