title: Detection of lungs status using morphological complexities of respiratory sounds.
abstract: Traditionally, the clinical diagnosis of a respiratory disease is made from a careful clinical examination including chest auscultation. Objective analysis and automatic interpretation of the lung sound based on its physical characters are strongly warranted to assist clinical practice. In this paper, a new method is proposed to distinguish between the normal and the abnormal subjects using the morphological complexities of the lung sound signals. The morphological embedded complexities used in these experiments have been calculated in terms of texture information (lacunarity), irregularity index (sample entropy), third order moment (skewness), and fourth order moment (Kurtosis). These features are extracted from a mixed data set of 10 normal and 20 abnormal subjects and are analyzed using two different classifiers: extreme learning machine (ELM) and support vector machine (SVM) network. The results are obtained using 5-fold cross-validation. The performance of the proposed method is compared with a wavelet analysis based method. The developed algorithm gives a better accuracy of 92.86% and sensitivity of 86.30% and specificity of 86.90% for a composite feature vector of four morphological indices.
authors.affiliation: Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, Kharagpur 721 302, India.
authors.forename: Ashok
authors.identifier: 0000-0002-8410-9198
authors.initials: A
authors.lastname: Mondal
authors.affiliation: Institute of Pulmocare and Research, Kolkata, Kolkata 700 064, India.
authors.forename: Parthasarathi
authors.initials: P
authors.lastname: Bhattacharya
authors.affiliation: Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, Kharagpur 721 302, India.
authors.forename: Goutam
authors.identifier: 0000-0001-6187-1684
authors.initials: G
authors.lastname: Saha
mesh_terms: D001185:Artificial Intelligence; D001314:Auscultation / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008171:Lung Diseases / Q000175:diagnosis* / Q000503:physiopathology*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012135:Respiratory Sounds / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000379:methods