Automatic wheeze detection based on auditory modelling

Qiu, Yihong; Whittaker, A.R.; Lucas, Margaret; Anderson, Kenneth

doi:10.1243/095441105x28551

Cited by 26 publications

(12 citation statements)

References 19 publications

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“…Two studies 28,29 used an algorithm based on short-time Fourier transformation, and one study 25 used a modification of the algorithm proposed by Shabtai-Musih et al 31 and HomsCorbera et al 32 A total of 9 studies analyzed WHs (3 studies were conducted in children), 2 studies analyzed CRs, 5,27 and one study analyzed both WHs and CRs in children. 30 Two studies detected breathing cycles automatically; one study 5 used an analogous method reported by Qiu et al, 33 and the other study 30 used the algorithm of Huq and Moussavi. 34 Only 3 studies 26,27,29 considered the breathing phases (inspiration and expiration) in the analysis of the ARSs.…”

Section: Studies Included In Review 12mentioning

confidence: 99%

Computerized Adventitious Respiratory Sounds as Outcome Measures for Respiratory Therapy: A Systematic Review

2013

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INTRODUCTION:There is a need to develop simple, noninvasive, and sensitive outcome measures for respiratory therapy. Adventitious respiratory sounds (ie, crackles and wheezes) can be objectively characterized with computerized respiratory sound analysis (CORSA) and have been shown to contribute for diagnosis purposes; however, their potential for use as outcome measures is unknown. Thus, this systematic review synthesizes the evidence on the use of computerized adventitious respiratory sounds as outcome measures. METHODS: The Web of Knowledge, MEDLINE, EMBASE, and SCOPUS databases were searched. Reviewers independently selected studies according to the eligibility criteria. Effect sizes and 95% CIs were computed. RESULTS: Twelve studies with different designs (observational, n ‫؍‬ 3; quasi-experimental n ‫؍‬ 7; and randomized controlled trial, n ‫؍‬ 2) were included. Eight studies were conducted with adults, and 4 studies with children. Most studies explored only one type of adventitious respiratory sound. For wheezes, the occupation rate seemed to be the most promising parameter to be used as an outcome measure, with high/medium effect sizes (0.62-1.82). For crackles, the largest deflection width showed high effect sizes (1.31 and 1.04); however, this was explored in only one study. Crackle number and 2-cycle duration presented conflicting information, with high/poor effect sizes depending on the study. CONCLUSIONS: Specific variables of each adventitious respiratory sound detected and characterized by CORSA showed high effect sizes and, thus, the potential to be used as outcome measures. Further research with robust study designs and larger samples (both of children and adult populations), and following CORSA guidelines is needed to build evidence-based knowledge on this topic.

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Section: Studies Included In Review 12mentioning

confidence: 99%

Computerized Adventitious Respiratory Sounds as Outcome Measures for Respiratory Therapy: A Systematic Review

2013

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“…An alternative model of spectral crests was proposed in [18] with the aim of detecting only the audible sounds of wheezing. The audibility of a tonal signal masked in the noise of normal respiration was modeled by the ratio of the energy contained in the spectral crest to the energy contained in the noise of the normal respiratory sound.…”

Section: Review Of the Stft-based Wheeze Detection Algorithmsmentioning

confidence: 99%

“…The assumption is that such algorithms may provide the highest fidelity of wheeze classification, including estimation of the durations, number and frequency of the individual harmonic components composing the sound of wheezing. The algorithms differ by their spectral features: the first algorithm models the spectral crests using low-order statistical moments (mean and variance), building upon [17,19,20], and the second using energy (inspired by [18]).…”

Section: Analysis Of Implemented Algorithmsmentioning

confidence: 99%

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Low-Power Wearable Respiratory Sound Sensing

Oletić

Arsenali

Bilas

2014

Sensors

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Building upon the findings from the field of automated recognition of respiratory sound patterns, we propose a wearable wireless sensor implementing on-board respiratory sound acquisition and classification, to enable continuous monitoring of symptoms, such as asthmatic wheezing. Low-power consumption of such a sensor is required in order to achieve long autonomy. Considering that the power consumption of its radio is kept minimal if transmitting only upon (rare) occurrences of wheezing, we focus on optimizing the power consumption of the digital signal processor (DSP). Based on a comprehensive review of asthmatic wheeze detection algorithms, we analyze the computational complexity of common features drawn from short-time Fourier transform (STFT) and decision tree classification. Four algorithms were implemented on a low-power TMS320C5505 DSP. Their classification accuracies were evaluated on a dataset of prerecorded respiratory sounds in two operating scenarios of different detection fidelities. The execution times of all algorithms were measured. The best classification accuracy of over 92%, while occupying only 2.6% of the DSP's processing time, is obtained for the algorithm featuring the time-frequency tracking of shapes of crests originating from wheezing, with spectral features modeled using energy.

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“…Several methods for their detection has been proposed leaning on these characteristics as prerequisite in their analyses. Methods using short-time Fourier transform, based on a SNR threshold depending on time and frequency parameters, show good result for the detection of such abnormal sounds [24]. Other classical image processing approaches exploiting the informations given by the spectrogram allow to detect the time/frequency structure caused by the presence of continuous abnormal sounds [12].…”

Section: Introductionmentioning

confidence: 99%

Detection of Transient Signals in Lung Sounds: Local Approach Using a Markovian Tree with Frequency Selectivity

Cam

Collet

Salzenstein

2010

J Sign Process Syst

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We deal in this paper with the extraction of multiresolution statistical signatures for the characterization of transient signals in strongly noisy contexts. These short-time signals have sharp and highly variable frequency components. The time/frequency window to choose for our analysis is then a major issue. We have chosen the Wavelet Packet Transform (WPT) due to its ability to provide multiple windows analysis with different time/frequency resolutions. We propose a new oriented Hidden Markov Tree (HMT) dedicated to the tree structure of the WPT, which offers promising statistical characterization of time/frequency variations in a signal, by exploiting several time/frequency resolutions. This model is exploited in a Bayesian context for the segmentation of signals containing transient components. The chosen data likelihood is a Generalized Gaussian Distributions (GGD), well suited for the modeling of Wavelet Packet Coefficients (WPC) distributions. We demonstrate the efficiency of our method on synthetic signals with several Signal to Noise Ratio (SNR). Our application domain is related to biomedical signals, and more specifically for the detection of uprising abnormalities in pulmonary sounds. This original method shows remarkable ability to detect such sounds, which are usually buried in the normal lung noise and are often very difficult to perceive with the human earing.

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Automatic wheeze detection based on auditory modelling

Cited by 26 publications

References 19 publications

Computerized Adventitious Respiratory Sounds as Outcome Measures for Respiratory Therapy: A Systematic Review

Computerized Adventitious Respiratory Sounds as Outcome Measures for Respiratory Therapy: A Systematic Review

Low-Power Wearable Respiratory Sound Sensing

Detection of Transient Signals in Lung Sounds: Local Approach Using a Markovian Tree with Frequency Selectivity

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