Overcomplete discrete wavelet transform based respiratory sound discrimination with feature and decision level fusion. (September 2017)

• Record Type: Journal Article
• Authors: Ulukaya, Sezer; Serbes, Gorkem; Kahya, Yasemin P.
• Is Part Of: Biomedical signal processing and control. Volume 38(2017)
• Page Start: 322
• Page End: 336
• Publication Date: 2017-09
• Subjects: Pulmonary sound -- Rational dilation wavelet transform -- Q-factor -- Ensemble learning -- Adventitious lung sounds -- Statistical feature extraction; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.06.018
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: A rational dilation wavelet transform based lung sounds classification system is proposed. Adaptive time-frequency representations of lung sounds are obtained by tuning Q-factors. Statistical features derived from optimum decomposition are used in classification. Ensemble learning is employed as feature fusion and weighted majority voting. Proposed system shows the best performance while being significantly faster than its nearest competitor. Abstract: Background and objective: Crackle, wheeze and normal lung sound discrimination is vital in diagnosing pulmonary diseases. Previous works suffer from limited frequency resolution and lack of the ability to deal with oscillatory signals (wheezes). The main objective of this study is to propose a novel wavelet based lung sound classification system that is capable of adaptively representing crackle, wheeze and normal lung sound signal time-frequency properties. Methods: A method which is based on rational dilation wavelet transform is proposed to classify lung sounds into three main categories, namely, normal, wheeze and crackle. Six different feature extraction methods were used with five different classifiers all of which were compared with the proposed method on 600 lung sound episodes in a cross validation scheme. Six statistical subset features were extracted from raw features and fed into classifiers. After comparative evaluation of the proposed method, an ensemble learning scheme was built to increase the performance of the proposed method. Results: It has been shown that performance of the proposed method was superior to previous methods in terms of accuracy. Moreover, its computational time was far less than its nearest competitor (S transform). It has also been shown that the proposed method was able to cope with oscillatory type signals as well as transient sounds performing 95.17% average accuracy for energy subset and 97.38% ensemble average accuracy showing a promising time-frequency tool for biological signals. Conclusions: The proposed method has shown better performance even using only one subset of extracted features. It provides better time-frequency resolution for all types of signals of interest and is less redundant than continuous wavelet transform and significantly faster than its nearest competitor.