A High performance electronic nose system for the recognition of myocardial infarction and coronary artery diseases

Abstract Electronic noses are devices that detect the number and level of chemicals in an odor. This is accomplished by means of chemical gas sensors in the device’s structure. The device recognizes the odors that were introduced to it via the software. When we breathe air, several gas exchanges take place in our lungs (e.g., O2-CO2 at the alveola-capillary level). Some gases in our blood are presented with our breath. The detection of these gases can provide information about our health. Nowadays, many diseases can be diagnosed with very high accuracy by using an electronic nose. Despite advances in diagnoses and treatments, cardiovascular disease is still the leading cause of mortality worldwide. Coronary artery disease is the leading cause of cardiovascular mortality and morbidity. The ability to diagnose coronary artery disease from the breath will accelerate the diagnosis, and thus, the initiation of treatment, which may save many lives. This study, involved an investigation of whether or not diseases (e.g., myocardial infarction, stable coronary artery disease) can be diagnosed from exhaled respiratory air using an electronic nose. This involved collecting data on exhaled breath from 33 patients diagnosed with myocardial infarction that underwent a primary percutaneous coronary intervention, 22 patients with stable coronary artery disease and 26 patients without heart disease. An electronic nose containing 19 gas sensors was manufactured for this study. The respondents’ breath was collected in a sterile manner. The statistical features including mean, skewness, kurtosis and derivative variance were extracted from the breath samples. These features were classified for the entire database using the support vector machine classifier by selecting 66 % as a training set and 34 % as a test set. The breath from the myocardial infarction patients were separated from that of the healthy individuals and the stable coronary artery disease patients with a classification accuracy rate of 97.19 %. The breath from the stable coronary artery disease patients were separated from the breath of the healthy control subjects with a classification accuracy rate of 81.48 %. The results reveal that the proposed method has great potential for myocardial infarction, stable coronary artery disease and healthy subjects when the electronic nose is used to record the exhaled respiratory air of the participants.