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.
In this paper application of the mRMR (minimum Redundancy Maximum Relevance) algorithm to reduction of the number of lung sounds features used for asthma wheezes recognition is proposed. The paper presents the reduction of following features: Tonal Index (TI), Kurtosis (K), Energy
Ratio (ER), correlation feature (CF1), Difference to Mean ratio (D2M), Eigen Value Decomposition feature (EVD), Linear Prediction feature (LP),Spectral Flatness (SF), Spectral Peaks Entropy (SPE), and two features that has not been presented yet in wheezes detection: Audio Spectral Envelope (ASE) taken from ISO/IEC MPEG-7 standard and Vector Comparison (VC). As a classifier the SVM algorithm was used.
The signal tonality detection is a common problem in many different technical fields, e.g. radar, sonar, perceptual audio encoders, birds recognition etc. Wheezes recognition at asthma patients is another example, which we are interested in, where the multi-tone signal is to be detected in colored noise of normal breath. The spectrum of the typical breath decreases roughly exponentially with frequency and this situation can cause serious problems for tonality descriptors designed for the white noise background. Therefore, the aim of this paper is to examine the impact of spectrum shape of background noise (its colority) on the detection of multi-tonal sinusoidal signal lying in a specified range of frequency.
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