Lung sound classification using cepstral-based statistical features.
Lung sounds convey useful information related to pulmonary pathology. In this paper, short-term spectral characteristics of lung sounds are studied to characterize the lung sounds for the identification of associated diseases. Motivated by the success of cepstral features in speech signal classification, we evaluate five different cepstral features to recognize three types of lung sounds: normal, wheeze and crackle. Subsequently for fast and efficient classification, we propose a new feature set computed from the statistical properties of cepstral coefficients. Experiments are conducted on a dataset of 30 subjects using the artificial neural network (ANN) as a classifier. Results show that the statistical features extracted from mel-frequency cepstral coefficients (MFCCs) of lung sounds outperform commonly used wavelet-based features as well as standard cepstral coefficients including MFCCs. Further, we experimentally optimize different control parameters of the proposed feature extraction algorithm. Finally, we evaluate the features for noisy lung sound recognition. We have found that our newly investigated features are more robust than existing features and show better recognition accuracy even in low signal-to-noise ratios (SNRs).
Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, Kharagpur 721302, India. Electronic address: nandinisengupta@iitkgp.ac.in.
Nandini
N
Sengupta
Speech and Image Processing Unit, School of Computing, University of Eastern Finland, Joensuu 80101, Finland. Electronic address: sahid@cs.uef.fi.
Md
M
Sahidullah
Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, Kharagpur 721302, India. Electronic address: gsaha@ece.iitkgp.ernet.in.
Goutam
G
Saha
D000465:Algorithms; D006801:Humans; D016571:Neural Networks, Computer; D012135:Respiratory Sounds / Q000145:classification*; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio
Artificial neural network (ANN); Auscultation; Discrete wavelet transform (DWT); Mel-frequency cepstral coefficients (MFCCs); Spectral features; Statistical features