Measurement and estimation of human forced expiratory noise parameters using a microphone with a stethoscope head and a lapel microphone

Glazova, Anna; Коренбаум, В. И.; Костив, А. Е.; Kabancova, O I; Tagiltcev, Alexandr; Shin, S. N.

doi:10.1088/1361-6579/aac556

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Respiratory related monitoring has been investigated previously as it relates to sleep (e.g., apneic periods) [4] and respiratory events [5] , [6] , [7] , [8] , [9] . A subset of this research focuses on audio-specific evaluations of cough in terms of identification, counting and classification [10] , [11] , [12] , [13] , [14] , [15] , [16] . The integration of this type of respiratory monitoring system has applications in existing assisted living systems aimed to support older adults living independently [17] , [18] .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications

Cohen-McFarlane

Wallace

et al. 2022

IEEE Open J. Eng. Med. Biol.

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Goal: The evaluation of respiratory events using audio sensing in an at-home setting can be indicative of worsening health conditions. This paper investigates the use of image-based transfer learning applied to five audio visualizations to evaluate three classification tasks (C1: wet vs. dry vs. whooping cough vs. restricted breathing; C2: wet vs. dry cough; C3: cough vs. restricted breathing). Methods: The five visualizations (linear spectrogram, logarithmic spectrogram, Mel-spectrogram, wavelet scalograms, and aggregate images) are applied to a pre-trained AlexNet image classifier for all tasks. Results: The aggregate image-based classifier achieved the highest overall performance across all tasks with C1, C2, and C3 having testing accuracies of 0.88, 0.88, and 0.91 respectively. However, the Mel-spectrogram method had the highest testing accuracy (0.94) for C2. Conclusions: The classification of respiratory events using aggregate image inputs to transfer learning approaches may help healthcare professionals by providing information that would otherwise be unavailable to them.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications

Cohen-McFarlane

Wallace

et al. 2022

IEEE Open J. Eng. Med. Biol.

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The Regression Parameters of Band Frequency Spectra of the Breath Sounds Vary in Course of a Respiratory Cycle

Oliynik

Sobol

2019

2019 IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO)

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Human forced expiratory noise. Origin, apparatus and possible diagnostic applications

Коренбаум

Почекутова

Костив

et al. 2020

The Journal of the Acoustical Society of America

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Forced expiratory (FE) noise is a powerful bioacoustic signal containing information on human lung biomechanics. FE noise is attributed to a broadband part and narrowband components—forced expiratory wheezes (FEWs). FE respiratory noise is composed by acoustic and hydrodynamic mechanisms. An origin of the most powerful mid-frequency FEWs (400–600 Hz) is associated with the 0th–3rd levels of bronchial tree in terms of Weibel [(2009). Swiss Med. Wkly. 139 (27–28), 375–386], whereas high-frequency FEWs (above 600 Hz) are attributed to the 2nd–6th levels of bronchial tree. The laboratory prototype of the apparatus is developed, which includes the electret microphone sensor with stethoscope head, a laptop with external sound card, and specially developed software. An analysis of signals by the new method, including FE time in the range from 200 to 2000 Hz and band-pass durations and energies in the 200-Hz bands evaluation, is applied instead of FEWs direct measures. It is demonstrated experimentally that developed FE acoustic parameters correspond to basic indices of lung function evaluated by spirometry and body plethysmography and may be even more sensitive to some respiratory deviations. According to preliminary experimental results, the developed technique may be considered as a promising instrument for acoustic monitoring human lung function in extreme conditions, including diving and space flights. The developed technique eliminates the contact of the sensor with the human oral cavity, which is characteristic for spirometry and body plethysmography. It reduces the risk of respiratory cross-contamination, especially during outpatient and field examinations, and may be especially relevant in the context of the COVID-19 pandemic.

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Measurement and estimation of human forced expiratory noise parameters using a microphone with a stethoscope head and a lapel microphone

Cited by 3 publications

References 23 publications

Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications

Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications

The Regression Parameters of Band Frequency Spectra of the Breath Sounds Vary in Course of a Respiratory Cycle

Human forced expiratory noise. Origin, apparatus and possible diagnostic applications

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