Fundamentals of Respiratory Sounds and Analysis

Moussavi, Zahra

doi:10.2200/s00054ed1v01y200609bme008

Cited by 37 publications

(43 citation statements)

References 64 publications

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“…5,8 Indeed, the appearance of the symptoms of respiratory illnesses like the wheeze for the asthma is localized in a precise frequencies band. The localization around these frequencies will facilitate the analysis of this type of sound.…”

Section: A Priori Mass Function Modelizationmentioning

confidence: 99%

An evidence segmentation scheme for asthma detection using a priori wavelet respiratory sound information

Belghith

Collet

2010

SPIE Proceedings

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This paper presents an evidential segmentation scheme of respiratory sounds for the detection of wheezes. The segmentation is based on the modeling of the data by evidence theory which is well suited to represent such uncertain and imprecise data. Moreover, this paper studies the efficiency of the fuzzy theory for modelizing data imprecision. The segmentation results are improved by adding a priori information to the segmentation scheme. The effectiveness of the method is demonstrated on synthetic and real signals

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Section: A Priori Mass Function Modelizationmentioning

confidence: 99%

An evidence segmentation scheme for asthma detection using a priori wavelet respiratory sound information

Belghith

Collet

2010

SPIE Proceedings

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“…Based on these simultaneous multisensor recordings (e.g., ≥16 microphones), an acoustic mapping and imaging of the chest is feasible [24]. Although such an approach offers lower resolution compared to computerized tomography, X-ray, or functional magnetic resonance imaging techniques, it provides a new perspective in the exploitation of LS diagnostic information [25]. Table 1.1 tabulates recommendations [20] regarding the acquisition of LS signals.…”

Section: Procedures and Considerationsmentioning

confidence: 99%

“…Stemming from these two initial efforts, more efficient heart sound reduction techniques have been developed over the years, using advanced signal processing methodologies. These can be further categorized into those that analyze the entire LS record and reduce heart sounds effect, and those that remove heart sounds included in portions of the LS record and then estimate the signal in the gaps [25]. Several indicative examples of heart sound cancellers (HSCs) from both categories are described in the succeeding subsections.…”

Section: Heart Sound Cancellationmentioning

confidence: 99%

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Lung Sounds: An Advanced Signal Processing Perspective

Hadjileontiadis

2008

Synthesis Lectures on Biomedical Engineering

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“…Respiratory sounds are spectrally constrained with the low-pass characteristic of the chest-wall tissue, and typically reside in the frequency band below 1 kHz. They are measured on skin-surface with an acoustic sensor (microphone or accelerometer) positioned on patent's chest, back or neck [2]. Respiratory sounds are digitized at sampling frequency f s of at least 2 kHz, and are typically quantized to 16-bit resolution [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Respiratory Sounds Sensing for Personal Mobile Asthma Monitoring

Oletić

Bilas

2016

IEEE Sensors J.

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Current medical practice of long-term chronic respiratory diseases treatment lacks a convenient method of empowering the patients and caregivers to continuously quantitatively track the intensity of respiratory symptoms. Such is "asthmatic wheezing", occurring in respiratory sounds. We envision a mobile, personalized asthma monitoring system comprising of a wearable, energy-constrained acoustic sensor and smartphone. In this article we address the energy-burden of acquisition and streaming of acoustic respiratory signal, and lessen it by applying the concept of compressed sensing (CS). First we analyse the adherence of normal and pathologic respiratory sounds frequency representations (DFT, DCT) to the sparse signal model. Given the pseudo-random non-uniform subsampling encoder implemented on MSP430 microcontroller, we review tradeoffs of accuracy and execution time of different CS algorithms, suitable for realtime respiratory spectrum recovery on smartphone. Working CS respiratory spectrum acquisition prototype is demonstrated, and evaluated. Prototype enables for real-time reconstruction of spectra dominated by approximately 8 frequency components with more than 80% accuracy, on Android smartphone using OMP algorithm, from only 25% signal samples (w.r.t. Nyquist rate) acquired and streamed by sensor at 8 kbit/s. Index Terms-M-health, asthmatic wheezing, compressive sensing, non-uniform sampling, orthogonal matching pursuit.1530-437X (c)

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Fundamentals of Respiratory Sounds and Analysis

Cited by 37 publications

References 64 publications

An evidence segmentation scheme for asthma detection using a priori wavelet respiratory sound information

An evidence segmentation scheme for asthma detection using a priori wavelet respiratory sound information

Lung Sounds: An Advanced Signal Processing Perspective

Energy-Efficient Respiratory Sounds Sensing for Personal Mobile Asthma Monitoring

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