Joint application of audio spectral envelope and tonality index in an e-asthma monitoring system.

This paper presents in detail a recently introduced highly efficient method for automatic detection of asthmatic wheezing in breathing sounds. The fluctuation in the audio spectral envelope (ASE) from the MPEG-7 standard and the value of the tonality index (TI) from the MPEG-2 Audio specification are jointly used as discriminative features for wheezy sounds, while the support vector machine (SVM) with a polynomial kernel serves as a classifier. The advantages of the proposed approach are described in the paper (e.g., detecting weak wheezes, very good ROC characteristics, independence from noise color). Since the method is not computationally complex, it is suitable for remote asthma monitoring using mobile devices (personal medical assistants). The main contribution of this paper consists of presenting all the implementation details concerning the proposed approach for the first time, i.e., the pseudocode of the method and adjusting the values of the ASE and TI parameters after which only one (not two) FFT is required for analysis of a next overlapping signal fragment. The efficiency of the method has also been additionally confirmed by the AdaBoost classifier with a built-in mechanism to feature ranking, as well as a previously performed minimal-redundancy-maximal-relevance test.