Original Contribution: Stacked generalization
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elimination of vesicular sounds from pulmonary crackle waveforms
Computer Methods and Programs in Biomedicine
Feature extraction for pulmonary crackle representation via wavelet networks
Computers in Biology and Medicine
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Assessing the variability in respiratory acoustic thoracic imaging (RATHI)
Computers in Biology and Medicine
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Pulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders. Crackles are very common adventitious sounds which have transient characteristic. From the characteristics of crackles such as timing and number of occurrences, the type and the severity of the pulmonary diseases can be obtained. In this study, a novel method is proposed for crackle detection, which uses time- frequency and time-scale analysis, and the performance comparison for different window types in time-frequency analysis and also for different wavelet types in time-scale analysis is presented. In the proposed method, various feature sets are extracted using time-frequency and time-scale analysis for different windows and wavelet types. The extracted feature sets are fed into support vector machines both individually and as an ensemble of networks. Besides, as a preprocessing stage in order to improve the success of the model, frequency bands containing no-information are removed using dual tree complex wavelet transform, which is a shift invariant transform with limited redundancy and an improved version of discrete wavelet transform. The comparative results of individual feature sets and ensemble of sets with pre-processed and non pre-processed data for different windows and wavelets are proposed.