In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
A New Feature Extraction Method Based on Feature Integration
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3
Breast cancer diagnosis using least square support vector machine
Digital Signal Processing
Computers & Mathematics with Applications
Feature extraction for pulmonary crackle representation via wavelet networks
Computers in Biology and Medicine
Respiratory sound classification by using an incremental supervised neural network
Pattern Analysis & Applications
Computer Methods and Programs in Biomedicine
Computers in Biology and Medicine
Identification of EMG signals using discriminant analysis and SVM classifier
Expert Systems with Applications: An International Journal
IEEE Transactions on Signal Processing
Pulmonary crackle detection using time-frequency and time-scale analysis
Digital Signal Processing
Signal-adaptive discrete evolutionary transform as a sparse time-frequency representation
Digital Signal Processing
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This paper proposed various feature extraction procedures to separate crackles and rhonchi of pathological lung sounds from normal lung sounds. The feature extraction process for distinguishing crackles and rhonchus from normal sounds comprises three signal-processing modules with the following functions: (1) f"m"i"n/f"m"a"x was the frequency ratio from the conventional technique of power spectral density (PSD) based on the Welch method. (2) The average instantaneous frequency (IF) and the exchange time of the instantaneous frequency were calculated by the Hilbert Huang transform (HHT). (3) The eigenvalues were obtained from the singular spectrum analysis (SSA) method. In the classification process, a support vector machine (SVM) was used to distinguish the crackles, rhonchus and normal lung sounds. The results showed that the selected features positively represented the characteristic changes in sounds. The PSD frequency ratio and the eigenvalues demonstrate higher classification accuracy (between 90% and 100%) than the calculations of average and exchange time of IF. The calculated features are extremely promising for the evaluation and classification of other biomedical signals as well as other lung sounds.