IEEE J Biomed Health Inform - Joint application of audio spectral envelope and tonality index in an e-asthma monitoring system.

Tópicos

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Resumo

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.

Resumo Limpo

paper present detail recent introduc high effici method automat detect asthmat wheez breath sound fluctuat audio spectral envelop ase mpeg standard valu tonal index ti mpeg audio specif joint use discrimin featur wheezi sound support vector machin svm polynomi kernel serv classifi advantag propos approach describ paper eg detect weak wheez good roc characterist independ nois color sinc method comput complex suitabl remot asthma monitor use mobil devic person medic assist main contribut paper consist present implement detail concern propos approach first time ie pseudocod method adjust valu ase ti paramet one two fft requir analysi next overlap signal fragment effici method also addit confirm adaboost classifi builtin mechan featur rank well previous perform minimalredundancymaximalrelev test

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