![]() Pediatric cardiologists evaluated the heart sounds and classified the case as either normal/innocent or pathologic. Patients undergoing evaluation underwent examination, echocardiography, and heart sound recording. They hypothesized that their system could accurately classify auscultatory findings as normal/innocent or pathologic. We conclude that the most likely mechanism of crackle generation is sudden airway closing during expiration and sudden airway reopening during inspiration.īecause pediatric cardiologists can accurately diagnose innocent murmurs by physical exam alone, the authors developed a system for remote cardiac auscultation. This hypothesis holds that expiratory crackles are caused by sudden airway closure events that are similar in mechanism but opposite in sign and far less energetic than the explosive opening events that generate inspiratory crackles. Inspiratory crackles were almost twice as numerous as expiratory crackles (n = 3,308 vs 1,841) and had predominately negative polarity (76% of inspiratory crackles vs 31% of expiratory crackles).ĬONCLUSION: These observations are quantitatively consistent with the so-called stress-relaxation quadrupole hypothesis of crackle generation. RESULTS: Spectral, temporal, and spatial characteristics of expiratory and inspiratory crackles in these patients were found to be similar, but two characteristics were strikingly different: crackle numbers and crackle polarities. Multiple crackle characteristics were calculated for each crackle, including frequency, amplitude, crackle transmission coefficient, and crackle polarity. These patients included 37 with pneumonia, 5 with heart failure, and 13 with interstitial fibrosis. ![]() METHODS: Patients with a significant number of both inspiratory and expiratory crackles were examined using a multichannel lung sound analyzer. The goal of this research was to gain insights into the mechanism of crackle generation by systematic examination of the relationship between inspiratory and expiratory crackle characteristics. OBJECTIVE: Although crackles are frequently heard on auscultation of the chest of patients with common cardiopulmonary disorders, the mechanism of production of these sounds is inadequately understood. The database was segmented to extract 360 representative individual beats (180 per class). This work was carried out using a database that contains 164 phonocardiographic recordings (81 normal and 83 records with murmurs). computational load) for the automatic detection of murmurs. The conclusion is that fractal type features were the most robust family of parameters (in the sense of accuracy vs. However, an accuracy around 94% can be reached just by using the two main features of the fractal family therefore, considering the difficulties related to the automatic intrabeat segmentation needed for spectral and perceptual features, this scheme becomes an interesting alternative. The contribution of each family of features extracted was evaluated by means of a simple k-nearest neighbors classifier, showing that fractal features provide the best accuracy (97.17%), followed by time-varying & time-frequency (95.28%), and perceptual features (88.7%). In the second stage, the main components extracted from each family were combined together with the goal of improving the accuracy of the system. With the aim of improving the performance of the system, the accuracy of the system was tested using several combinations of the aforementioned families of parameters. Taking into account the variability of the phonocardiographic signals induced by valve disorders, three families of features were analyzed: (a) time-varying & time-frequency features (b) perceptual and (c) fractal features. This work presents a comparison of different approaches for the detection of murmurs from phonocardiographic signals.
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