Digital processing techniques of breath sounds for objective assistance of asthma diagnosis

Charbonneau, Georges; Racineux, Jean-Louis; Sudraud, M.; Tuchais, E

doi:10.1109/icassp.1982.1171790

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…Batra et al [2] explored features such as harmonic to noise ratio (HNR), jitter and shimmer in the sustained vowel phonation for identifying asthma patients. There are several works that classify asthma using respiratory sound based on pitch [6], [20], dominant frequency range [11], [12] and duration of the breath [22]. To the best of our knowledge, there is no work reported on predicting spirometry readings from cough and wheeze for asthma level monitoring.…”

Section: Introductionmentioning

confidence: 97%

Automatic prediction of spirometry readings from cough and wheeze for monitoring of asthma severity

Rao

Kausthubha

Yadav

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-We consider the task of automatically predicting spirometry readings from cough and wheeze audio signals for asthma severity monitoring. Spirometry is a pulmonary function test used to measure forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) when a subject exhales in the spirometry sensor after taking a deep breath. FEV1%, FVC% and their ratio are typically used to determine the asthma severity. Accurate prediction of these spirometry readings from cough and wheeze could help patients to non-invasively monitor their asthma severity in the absence of spirometry. We use statistical spectrum description (SSD) as the cue from cough and wheeze signal to predict the spirometry readings using support vector regression (SVR). We perform experiments with cough and wheeze recordings from 16 healthy persons and 12 patients. We find that the coughs are better predictor of spirometry readings compared to the wheeze signal. FEV1%, FVC% and their ratio are predicted with root mean squared error of 11.06%, 10.3% and 0.08 respectively. We also perform a three class asthma severity level classification with predicted FEV1% and obtain an accuracy of 77.77%.

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Section: Introductionmentioning

confidence: 97%

Automatic prediction of spirometry readings from cough and wheeze for monitoring of asthma severity

Rao

Kausthubha

Yadav

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…Figure 2 presents the histogram of frequencies for recorded wheezes after such operation (decomposition). [13,14] and recorded by authors of this paper. (SF) and Spectral Peaks Entropy (SPE) were presented in [10].…”

Section: Lung Soundsmentioning

confidence: 93%

“…Figure 1 presents the examples of spectra shapes in logarithmic scale taken from the literature [13,14] and recorded by authors during their research (some signals taken from the Internet are presented as well in [15,16]). Different signal levels results from involved specific signal amplification and scaling.…”

Section: Lung Soundsmentioning

confidence: 99%

Detection of multi-tones in white and colored noise with application to asthmatic wheezes detection

Wisniewski

Zieliński

2012

2012 International Conference on Signals and Electronic Systems (ICSES)

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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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“…1981] and proposed signal processing techniques in the frequency domain to be applied in the analysis and classification of breath sounds. More recent works have highlighted a variety of approaches to analyse breath sounds for the detection of dysfunctions or diseases as apnoea [PETSATODIS, T. et al, 2012;, asthma [OUD, M. & DOOIJES, E.H., 1996;, snores [PETSATODIS, T. et al, 2012;SNIDER, B.R. & KAIN, A., 2013;YADOLLAHI, A.…”

Section: Introductionmentioning

confidence: 99%

“…& MOUSSAVI, Z., 2010] or an acoustic sensor within a mask [AVALUR, D.S., 2013] are better options to obtain more robust signal but may cause discomfort to the user. On the other hand, non-invasive methods like a microphone oriented toward the user's head at a short distance [PETSATODIS, T. et al, 2012;SNIDER, B.R. & KAIN, A., 2013] are easier to build but are more sensitive to external noises.…”

Section: Introductionmentioning

confidence: 99%

Real-time Identification of Respiratory Movements through a Microphone

Castro

Martí-Puig

2015

ADCAIJ

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KEYWORD ABSTRACT Digital signal processing Pattern recognition Machine learning Respiratory analysis Acoustic featuresThis work presents a software application to identify, in real time, the respiratory movements -inspiration and expiration-through a microphone. The application, which has been developed in Matlab and named ASBSLAB for the GUI version and ASBSLABCONSOLE for the command-line version, is the result of a research and experimentation process. A total of 48 minutes of breathing movements from four subjects was recorded and 18 acoustic features were extracted to generate the data model. A first level of identification, based on the classification of tiny audio segments, was designed using kNN supervised method. The second level of identification implements a state machine that takes the results ordered in the time from kNN as input and identifies the whole respiratory movement, achieving a level of positive identifications above 95%. As computation time is a handicap, the application let the user choose easily the sample rate, the audio segment size and the set of acoustic features to use in the identification process. In addition, based on the number of features selected, this works suggests those that achieve best results.

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Digital processing techniques of breath sounds for objective assistance of asthma diagnosis

Cited by 8 publications

References 2 publications

Automatic prediction of spirometry readings from cough and wheeze for monitoring of asthma severity

Automatic prediction of spirometry readings from cough and wheeze for monitoring of asthma severity

Detection of multi-tones in white and colored noise with application to asthmatic wheezes detection

Real-time Identification of Respiratory Movements through a Microphone

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