Digital Respirosonography

Pasterkamp, Hans; Carson, Chris; Daien, David; Oh, Yong─Geun

doi:10.1378/chest.96.6.1405

Cited by 64 publications

(9 citation statements)

References 16 publications

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“…In addition, both respiratory phases have similar frequency content for similar airflow peaks and a silent period separating both phases could also be observed from both the tracheal sound waveform as well as its TFR. These results are in agreement with the findings reported in the literature when using CORSA systems [3,4,10]. …”

Section: Resultssupporting

confidence: 93%

“…The most widely-used TFR in the respiratory sounds field is the spectrogram (SP) given by the magnitude square of the short time Fourier transform (STFT) [4,10]. The idea behind the SP is that in order to study the properties of the signal s around time t , the original signal around that time is emphasized but it is suppressed at other times by multiplying by a window function w ( t ) centered at t , to produce a modified signal s t (τ) given by [38,39]

s_{t} (τ) = s (τ) w (τ - t)

where the modified signal is a function of two times, the fixed time t of interest, and the time τ .…”

Section: Methodsmentioning

confidence: 99%

“…This distribution has received different names depending on the application field, e.g. , respirosonogram in the respiratory sounds field [10], but the most common is simply spectrogram.…”

Section: Methodsmentioning

confidence: 99%

“…The stethoscope remains the most widely used instrument in clinical medicine and its use during auscultation still guides in diagnosis when other pulmonary function testing is not available [10]. However, auscultation with the mechanical stethoscope has limitations [3,11,12].…”

Section: Introductionmentioning

confidence: 99%

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Tracheal Sounds Acquisition Using Smartphones

Reyes

Reljin

Chon

2014

Sensors

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Tracheal sounds have received a lot of attention for estimating ventilation parameters in a non-invasive way. The aim of this work was to examine the feasibility of extracting accurate airflow, and automating the detection of breath-phase onset and respiratory rates all directly from tracheal sounds acquired from an acoustic microphone connected to a smartphone. We employed the Samsung Galaxy S4 and iPhone 4s smartphones to acquire tracheal sounds from N = 9 healthy volunteers at airflows ranging from 0.5 to 2.5 L/s. We found that the amplitude of the smartphone-acquired sounds was highly correlated with the airflow from a spirometer, and similar to previously-published studies, we found that the increasing tracheal sounds' amplitude as flow increases follows a power law relationship. Acquired tracheal sounds were used for breath-phase onset detection and their onsets differed by only 52 ± 51 ms (mean ± SD) for Galaxy S4, and 51 ± 48 ms for iPhone 4s, when compared to those detected from the reference signal via the spirometer. Moreover, it was found that accurate respiratory rates (RR) can be obtained from tracheal sounds. The correlation index, bias and limits of agreement were r2 = 0.9693, 0.11 (−1.41 to 1.63) breaths-per-minute (bpm) for Galaxy S4, and r2 = 0.9672, 0.097 (–1.38 to 1.57) bpm for iPhone 4s, when compared to RR estimated from spirometry. Both smartphone devices performed similarly, as no statistically-significant differences were found.

show abstract

Section: Resultssupporting

confidence: 93%

s_{t} (τ) = s (τ) w (τ - t)

where the modified signal is a function of two times, the fixed time t of interest, and the time τ .…”

Section: Methodsmentioning

confidence: 99%

“…This distribution has received different names depending on the application field, e.g. , respirosonogram in the respiratory sounds field [10], but the most common is simply spectrogram.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tracheal Sounds Acquisition Using Smartphones

Reyes

Reljin

Chon

2014

Sensors

View full text Add to dashboard Cite

show abstract

“…Newer techniques ofspectral analysis, based on sophisticated digital filters, are now being investigated, which have the potential for data compression and modelling the underlying mechanism of the sound produetion.34 Many other electronic techniques are now being used, including noise reduction by a range of digital filters5; multiple surface microphones to map the sources of a sound within the lunge; automatic pattem recognition; self organising feature maps using artificial neural networks7; and the capture, storage, and three dimensional spectrographic displays of lung sound and physiological data. 8 Nomenclature of lung sounds has been mainly descriptive, frequently relying on the use of onomatopoeic words. Unfortunately, Laennec's carefully constructed original classification was extensively modified during translation into English and this resulted in the use by other authors of the same terms to describe different sounds.…”

mentioning

confidence: 99%