Journal articles: 'Breath sounds'

1

Goettel, Nicolai, and Matthias J. Herrmann. "Breath Sounds." Anesthesia & Analgesia 128, no. 3 (March 2019): e42. http://dx.doi.org/10.1213/ane.0000000000003969.

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Tatarunis, Paula. "Breath Sounds." Lancet 353, no. 9160 (April 1999): 1282. http://dx.doi.org/10.1016/s0140-6736(05)75209-0.

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&NA;. "ASSESSING BREATH SOUNDS." Nursing 26, no. 6 (June 1996): 50. http://dx.doi.org/10.1097/00152193-199606000-00019.

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Wodicka, George R. "Breath sounds methodology." Annals of Biomedical Engineering 24, S1 (September 1995): 180–82. http://dx.doi.org/10.1007/bf02771006.

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Furman, E. G., A. O. Charushin, E. S. Eirikh, G. B. Furman, V. L. Sokolovsky, S. V. Malinin, V. S. Sheludko, D. A. Polyanskaya, N. M. Kalinina, and D. K. Shtivelman. "Capabilities of computer analysis of breath sounds in patients with COVID-19." Perm Medical Journal 38, no. 3 (July 16, 2021): 97–109. http://dx.doi.org/10.17816/pmj38397-109.

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Objective. To develop methods for a rapid distance computer diagnosis of COVID-19 based on the analysis of breath sounds. It is known that changes in breath sounds can be the indicators of respiratory organs diseases. Computer analysis of these sounds can indicate their typical changes caused by COVID-19, and can be used for a rapid preliminary diagnosis of this disease. Materials and methods. The method of fast Fourier transform (FFT) was used for computer analysis of breath sounds, recorded near the mouth of 14 COVID-19 patients (aged 1880 years) and 17 healthy volunteers (aged 548 years). The frequency of breath sound records ranged from 44 to 96 kHz. Unlike the conventional methods of computer analysis for diagnosis of diseases based on respiratory sound studying, we offer to test a high-frequency part of FFT (20006000 kHz). Results. While comparing the breath sound FFT in patients and healthy volunteers, we developed the methods for COVID-19 computer diagnosis and determined the numerical criteria in patients and healthy persons. These criteria do not depend on sex and age of the examined persons. Conclusions. The offered computer methods based on the analysis of breath sound FFT in patients and volunteers permit to diagnose COVID -19 with relatively high diagnostic parameters. These methods can be used in development of noninvasive means for preliminary self-express diagnosis of COVID-19.

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SCHWARTZ, NATHAN. "Monitoring Bilateral Breath Sounds." Anesthesiology 66, no. 5 (May 1, 1987): 711. http://dx.doi.org/10.1097/00000542-198705000-00037.

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Schwartz, Nathan. "Monitoring Bilateral Breath Sounds." Anesthesia & Analgesia 73, no. 6 (December 1991): 828. http://dx.doi.org/10.1213/00000539-199112000-00034.

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Lee, Edward. "Monitoring Bilateral Breath Sounds." Anesthesia & Analgesia 73, no. 6 (December 1991): 828. http://dx.doi.org/10.1213/00000539-199112000-00035.

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9

Greco, Frank A. "Interpretation of Breath Sounds." American Journal of Respiratory and Critical Care Medicine 169, no. 11 (June 2004): 1260. http://dx.doi.org/10.1164/ajrccm.169.11.966.

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Jones, Frederick L. "Poor Breath Sounds with Good Voice Sounds." Chest 93, no. 2 (February 1988): 312–13. http://dx.doi.org/10.1378/chest.93.2.312.

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11

Gavriely, N., and D. W. Cugell. "Airflow effects on amplitude and spectral content of normal breath sounds." Journal of Applied Physiology 80, no. 1 (January 1, 1996): 5–13. http://dx.doi.org/10.1152/jappl.1996.80.1.5.

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Even though it is well known that breath-sound amplitude (BSA) increases with airflow, the exact quantitative relationships and their distribution within the relevant frequency range have not yet been determined. To evaluate these relationships, the spectral content of tracheal and chest wall breath sounds was measured during breath hold, inspiration, and expiration in six normal men. Average spectra were measured at six flow rates from 0.5 to 3.0 l/s. The areas under the spectral curves of the breath sounds minus the corresponding areas under the breath-hold spectra (BSA) were found to have power relationships with flow (F), best modeled as BSA = k.F alpha, where k and alpha are constants. The overall mean +/- SD value of the power (alpha) was 1.66 +/- 0.35, significantly less than the previously reported second power. Isoflow inspiratory chest wall sound amplitudes were 1.99 +/- 0.70- to 2.43 +/- 0.65-fold larger than the amplitudes of the corresponding expiratory sounds, whereas tracheal sound amplitudes were not dependent on respiratory phase. Isoflow breath sounds from the left posterior base were 32% louder than those from the right lung base (P < 0.01). BSA-F relationships were not frequency dependent during expiration but were significantly stronger in higher than in lower frequencies during inspiration over both posterior bases. These data are compatible with sound generation by turbulent flow in a bifurcating network with 1) flow separation, 2) downstream movement of eddies, and 3) collision of fast-moving cores of the inflowing air with carinas, all occurring during inspiration but not during expiration.

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Fesenko, V. A., and O. S. Poreva. "Analysis of breath sounds using higher-order spectra." Electronics and Communications 16, no. 2 (March 28, 2011): 119–25. http://dx.doi.org/10.20535/2312-1807.2011.16.2.268267.

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The basic properties of the Higher-Order Spectra and the possibility of application it to he analysis of respiratory sound is considered. Breathing sounds were analyzed by calculating the cumulants of third order. It is shown that the investigated sounds from the analysis on the basis of the spectra of higher orders may be attributed to a class of respiratory sounds. It is shown that this approach is more effective than traditional ways of solving this problem

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Huq, Saiful, and Zahra Moussavi. "Acoustic breath-phase detection using tracheal breath sounds." Medical & Biological Engineering & Computing 50, no. 3 (February 24, 2012): 297–308. http://dx.doi.org/10.1007/s11517-012-0869-9.

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14

Gavriely, N., and M. Herzberg. "Parametric representation of normal breath sounds." Journal of Applied Physiology 73, no. 5 (November 1, 1992): 1776–84. http://dx.doi.org/10.1152/jappl.1992.73.5.1776.

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The spectral content of normal tracheal and chest wall breath sounds has been calculated using the fast Fourier transform (FFT) (J. Appl. Physiol. 50: 307–314, 1981). Parameter estimation methods, in particular autoregressive (AR) modeling, are alternative techniques for measuring lung sounds. The outcome of AR modeling of 38 complete breaths picked up simultaneously over the chest walls and tracheae of five normal males was evaluated. The sounds were treated as noise, bounded by a quasi-periodic envelope generated by the cyclic action of breathing, thus causing the sounds to become inherently nonstationary. Normalization of the sounds to their corresponding variance envelopes eliminated the nonstationarity, an important requirement for most signal-processing methods. Subsequently, the AR model order was sought using formal criteria. Orders 6–8 were found to be suitable for normal chest wall sounds, whereas tracheal sounds required at least orders 12–16. Using orders 6 and 12, we compared the prominent spectral features of chest wall and tracheal sounds calculated by AR with those found in the spectra calculated by FFT. The polar representation of the AR roots, calculated from the AR coefficients, showed that normal lung sounds from a group of individuals are characterized by a low variability, suggesting that this method may provide an alternative representation of the sounds. The data presented here show that normal lung sounds, when measured in the frequency domain by either FFT or AR modeling, have a characteristic pattern that is independent of the analysis method.

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Rohrmeier, Christian, Michael Herzog, Tobias Ettl, and Thomas S. Kuehnel. "Distinguishing snoring sounds from breath sounds: a straightforward matter?" Sleep and Breathing 18, no. 1 (June 21, 2013): 169–76. http://dx.doi.org/10.1007/s11325-013-0866-8.

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Murgia, Mauro, Ilaria Santoro, Giorgia Tamburini, Valter Prpic, Fabrizio Sors, Alessandra Galmonte, and Tiziano Agostini. "Ecological sounds affect breath duration more than artificial sounds." Psychological Research 80, no. 1 (January 31, 2015): 76–81. http://dx.doi.org/10.1007/s00426-015-0647-z.

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Ploysongsang, Yongyudh, Vijay K. Iyer, and Panapakkam A. Ramamoorthy. "Inspiratory and Expiratory Vesicular Breath Sounds." Respiration 57, no. 5 (1990): 313–17. http://dx.doi.org/10.1159/000195863.

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MAHONEY, DIANA. "Tracheal Breath Sounds May Predict Apnea." Internal Medicine News 44, no. 13 (August 2011): 73. http://dx.doi.org/10.1016/s1097-8690(11)70693-x.

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González-Camarena, R., A. Angeles-Olguín, T. Aljama-Corrales, and S. Charleston-Villalobos. "Imaging the Thoracic Distribution of Normal Breath Sounds." Methods of Information in Medicine 49, no. 05 (2010): 443–47. http://dx.doi.org/10.3414/me09-02-0039.

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Summary Objective: To assess the feasibility to generate a confident image of normal breath sounds (BS) based on the quantitative analysis of multichannel sensors and imaging them in three known clinical classes, i.e., tracheal, bronchial and vesicular, identifying their spatial distribution with high resolution on the posterior thoracic surface. Methods: Three parametrization techniques, the percentile frequencies, the univariate AR modeling, and the eigenvalues of the covariance matrix were evaluated when applied to BS. These sounds were acquired in twelve healthy subjects by a 5 × 5 sensor array on the posterior thoracic surface plus the sound at the tracheal position, to obtain feature vectors that fed a supervised multilayer neural network. Based on BS classification rate, the spatial distribution of each BS class was obtained by constructing an image using deterministic interpolation. Results: The univariate AR modeling was the best parametrization technique producing a classification performance of 96% during the validation phase and just 4% of not classified feature vectors. Corresponding values for the percentile frequencies were 92% and 7.7%, whereas for the eigenvalues were 91% and 9.0%. Conclusion: This work shows that it is possible to generate confident images associated with the distribution of normal BS classes. Therefore, a detailed image about the spatial distribution of BS in humans might be helpful for detecting lung diseases.

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Senthilnathan, Harini, Parijat Deshpande, and Beena Rai. "Breath Sounds as a Biomarker for Screening Infectious Lung Diseases." Engineering Proceedings 2, no. 1 (November 14, 2020): 65. http://dx.doi.org/10.3390/ecsa-7-08200.

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Periodic monitoring of breath sounds is essential for early screening of obstructive upper respiratory tract infections, such as inflammation of the airway typically caused by viruses. As an immediate first step, there is a need to detect abnormalities in breath sounds. The adult average male lung capacity is approximately 6 liters and the manifestation of pulmonary diseases, unfortunately, remains undetected until their advanced stages when the disease manifests into severe conditions. Additionally, such rapidly progressing conditions, which arise due to viral infections that need to be detected via adventitious breath sounds to take immediate therapeutic action, demand frequent monitoring. These tests are usually conducted by a trained physician by means of a stethoscope, which requires an in-person visit to the hospital. During a pandemic situation such as COVID-19, it is difficult to provide periodic screening of large volumes of people with the existing medical infrastructure. Fortunately, smartphones are ubiquitous, and even developing countries with skewed doctor-to-patient ratios typically have a smartphone in every household. With this technology accessibility in mind, we present a smartphone-based solution that monitors breath sounds from the user via the in-built microphone of their smartphone and our Artificial Intelligence (AI) -based anomaly detection engine. The presented automated classifier for abnormal breathing sounds is able to detect abnormalities in the early stages of respiratory dysfunctions with respect to their individual normal baseline vesicular breath sounds, with an accuracy of 95%, and it can flag them, and thus enhances the possibility of early detection.

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Kunisawa, Akira, Yuichi Ichinose, Hiroshi Kiyokawa, Kinya Furukawa, Norihiko Kawate, and Harubumi Kato. "Tracheal Hamartoma Detected by Abnormal Breath Sounds." Journal of Bronchology 7, no. 2 (April 2000): 160–63. http://dx.doi.org/10.1097/00128594-200007020-00012.

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Sainsbury, D. A. "Monitoring Heart and Breath Sounds by Telemetry." Anaesthesia and Intensive Care 13, no. 4 (November 1985): 415–16. http://dx.doi.org/10.1177/0310057x8501300416.

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Anderson, Kenneth, Yihong Qiu, Arthur R. Whittaker, and Margaret Lucas. "Breath sounds, asthma, and the mobile phone." Lancet 358, no. 9290 (October 2001): 1343–44. http://dx.doi.org/10.1016/s0140-6736(01)06451-0.

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Brown, Lawrence H., John E. Gough, Dolly M. Bryan-Berg, and Richard C. Hunt. "Assessment of Breath Sounds During Ambulance Transport." Annals of Emergency Medicine 29, no. 2 (February 1997): 228–31. http://dx.doi.org/10.1016/s0196-0644(97)70273-7.

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25

Gibbs, T. "Book: Auscultation Skills: Breath and Heart Sounds." BMJ 320, no. 7229 (January 22, 2000): 256. http://dx.doi.org/10.1136/bmj.320.7229.256.

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Evdokimova, Victoria Vladimirovna, Leonid Sergeevich Afanasov, and Anna Gennadievna Maslovskaya. "SPECTRAL AND FRACTAL CHARACTERISTICS OF BREATH SOUNDS." Messenger AmSU, no. 101 (2023): 18–29. http://dx.doi.org/10.22250/20730268_2023_101_18.

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Beaudoin, Richard. "Dashon Burton's Song Sermon: Corporeal Liveness and the Solemnizing Breath." Journal of the Society for American Music 16, no. 1 (February 2022): 1–23. http://dx.doi.org/10.1017/s1752196321000456.

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AbstractAmerican bass-baritone Dashon Burton's 2015 recording of the song sermon “He Never Said a Mumberlin’ Word” provides a case study of the interaction between sung melodies and audible breaths in the expression of a lyric. Acknowledging the relationship between Burton's performance and earlier notated arrangements by Roland Hayes, J. Rosamond Johnson, William Arms Fischer, and John W. Work, this study draws an arc from the macro-level of the song sermon's oral and notated history to the micro-level of Burton's sung syllables and, finally, to a spectrographic examination of his individual breaths. One of these, the solemnizing breath that inaugurates the fifth stanza of the work, is pinpointed as the expressive denouement of Burton's track. A contrast is drawn between the liturgical framework of Hayes's arrangement and the images of anti-racist protest marches that accompany Burton's recording. An emphasis on the granular aspects of Burton's voice invokes Sanden's concept of corporeal liveness, Barthes's writings on cinematic sound, and Bain's recognition of breath sounds as music. Studying the narrative totality of Burton's utterance engenders a connection between singer and orator, and builds a historical continuity between Burton and his antecedents.

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Shirota, Kazuhiko. "Acoustic Analysis of Neonatal Breath Sounds: Changes during the Breath Adaptation Period." Nihon Kikan Shokudoka Gakkai Kaiho 49, no. 5 (1998): 442–50. http://dx.doi.org/10.2468/jbes.49.442.

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Neili, Z., M. Fezari, and A. Redjati. "ELM and K-nn machine learning in classification of breath sounds signals." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 3528. http://dx.doi.org/10.11591/ijece.v10i4.pp3528-3536.

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The acquisition of Breath sounds (BS) signals from a human respiratory system with an electronic stethoscope, provide and offer prominent information which helps the doctors to diagnosis and classification of pulmonary diseases. Unfortunately, this BS signals with other biological signals have a non-stationary nature according to the variation of the lung volume, and this nature makes it difficult to analyze and classify between several diseases. In this study, we were focused on comparing the ability of the extreme learning machine (ELM) and k-nearest neighbour (K-nn) machine learning algorithms in the classification of adventitious and normal breath sounds. To do so, the empirical mode decomposition (EMD) was used in this work to analyze BS, this method is rarely used in the breath sounds analysis. After the EMD decomposition of the signals into Intrinsic Mode Functions (IMFs), the Hjorth descriptors (Activity) and Permutation Entropy (PE) features were extracted from each IMFs and combined for classification stage. The study has found that the combination of features (activity and PE) yielded an accuracy of 90.71%, 95% using ELM and K-nn respectively in binary classification (normal and abnormal breath sounds), and 83.57%, 86.42% in multiclass classification (five classes).

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Prado, Patricia Rezende do, Ana Rita de Cássia Bettencourt, and Juliana de Lima Lopes. "Defining characteristics and related factors of the nursing diagnosis for ineffective breathing pattern." Revista Brasileira de Enfermagem 72, no. 1 (February 2019): 221–30. http://dx.doi.org/10.1590/0034-7167-2018-0061.

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ABSTRACT Objective: To identify in the literature the defining characteristics and related factors of the nursing diagnosis "ineffective breathing pattern". Method: Integrative review with the steps: problem identification, literature search, evaluation and analysis of data and presentation of results. Results: Twenty articles and two dissertations were included. In children, the most prevalent related factor was bronchial secretion, followed by hyperventilation. The main defining characteristics were dyspnea, tachypnea, cough, use of accessory muscles to breathe, orthopnea and adventitious breath sounds. Bronchial secretion, cough and adventitious breath sounds are not included in the NANDA-International (NANDA-I). For adults and older adults, the related factors were fatigue, pain and obesity and the defining characteristics were dyspnea, orthopnea and tachypnea. Conclusion: This diagnosis manifests differently according to the patients’ age group. It was observed that some defining characteristics and related factors are not included in the NANDA-I. Their inclusion can improve this nursing diagnosis.

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Ogawa, Ryoma, Kyosuke Futami, and Kazuya Murao. "Nasal Breath Input: Exploring Nasal Breath Input Method for Hands-Free Input by Using a Glasses Type Device with Piezoelectric Elements." Journal of Data Intelligence 3, no. 4 (November 2022): 421–40. http://dx.doi.org/10.26421/jdi3.4-2.

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Research on hands-free input methods has been actively conducted. However, most of the previous methods are difficult to use at any time in daily life due to using speech sounds or body movements. In this study, to realize a hands-free input method based on nasal breath using wearable devices, we propose a method for recognizing nasal breath gestures, using piezoelectric elements placed on the nosepiece of a glasses-type device. In the proposed method, nasal vibrations generated by nasal breath are acquired as sound data from the devices. Next, the breath pattern is recognized based on the factors of breath count, time interval, and intensity. We implemented a prototype system. The evaluation results for 10 subjects showed that the proposed method can recognize eight types of nasal breath gestures at 0.82\% of F-value. The evaluation results also showed that the recognition accuracy is increased to more than 90\% by limiting gestures to those with a different breath count or different breath interval. Our study provides the first glasses type wearable sensing technology that uses nasal breathing for hands-free input.

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Yadollahi, Azadeh, and Zahra Moussavi. "Automatic breath and snore sounds classification from tracheal and ambient sounds recordings." Medical Engineering & Physics 32, no. 9 (November 2010): 985–90. http://dx.doi.org/10.1016/j.medengphy.2010.06.013.

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Rahman, Tawsifur, Nabil Ibtehaz, Amith Khandakar, Md Sakib Abrar Hossain, Yosra Magdi Salih Mekki, Maymouna Ezeddin, Enamul Haque Bhuiyan, et al. "QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds." Diagnostics 12, no. 4 (April 7, 2022): 920. http://dx.doi.org/10.3390/diagnostics12040920.

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Problem—Since the outbreak of the COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19 testing, which increases their chances of unintentionally spreading the virus. Currently, the primary diagnostic tool to detect COVID-19 is a reverse-transcription polymerase chain reaction (RT-PCR) test from the respiratory specimens of the suspected patient, which is invasive and a resource-dependent technique. It is evident from recent researches that asymptomatic COVID-19 patients cough and breathe in a different way than healthy people. Aim—This paper aims to use a novel machine learning approach to detect COVID-19 (symptomatic and asymptomatic) patients from the convenience of their homes so that they do not overburden the healthcare system and also do not spread the virus unknowingly by continuously monitoring themselves. Method—A Cambridge University research group shared such a dataset of cough and breath sound samples from 582 healthy and 141 COVID-19 patients. Among the COVID-19 patients, 87 were asymptomatic while 54 were symptomatic (had a dry or wet cough). In addition to the available dataset, the proposed work deployed a real-time deep learning-based backend server with a web application to crowdsource cough and breath datasets and also screen for COVID-19 infection from the comfort of the user’s home. The collected dataset includes data from 245 healthy individuals and 78 asymptomatic and 18 symptomatic COVID-19 patients. Users can simply use the application from any web browser without installation and enter their symptoms, record audio clips of their cough and breath sounds, and upload the data anonymously. Two different pipelines for screening were developed based on the symptoms reported by the users: asymptomatic and symptomatic. An innovative and novel stacking CNN model was developed using three base learners from of eight state-of-the-art deep learning CNN algorithms. The stacking CNN model is based on a logistic regression classifier meta-learner that uses the spectrograms generated from the breath and cough sounds of symptomatic and asymptomatic patients as input using the combined (Cambridge and collected) dataset. Results—The stacking model outperformed the other eight CNN networks with the best classification performance for binary classification using cough sound spectrogram images. The accuracy, sensitivity, and specificity for symptomatic and asymptomatic patients were 96.5%, 96.42%, and 95.47% and 98.85%, 97.01%, and 99.6%, respectively. For breath sound spectrogram images, the metrics for binary classification of symptomatic and asymptomatic patients were 91.03%, 88.9%, and 91.5% and 80.01%, 72.04%, and 82.67%, respectively. Conclusion—The web-application QUCoughScope records coughing and breathing sounds, converts them to a spectrogram, and applies the best-performing machine learning model to classify the COVID-19 patients and healthy subjects. The result is then reported back to the test user in the application interface. Therefore, this novel system can be used by patients in their premises as a pre-screening method to aid COVID-19 diagnosis by prioritizing the patients for RT-PCR testing and thereby reducing the risk of spreading of the disease.

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Räsänen, Jukka, Michael E. Nemergut, and Noam Gavriely. "Changes in breath sound power spectra during experimental oleic acid-induced lung injury in pigs." Journal of Applied Physiology 116, no. 1 (January 1, 2014): 61–66. http://dx.doi.org/10.1152/japplphysiol.00651.2013.

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To evaluate the effect of acute lung injury on the frequency spectra of breath sounds, we made serial acoustic recordings from nondependent, midlung and dependent regions of both lungs in ten 35- to 45-kg anesthetized, intubated, and mechanically ventilated pigs during development of acute lung injury induced with intravenous oleic acid in prone or supine position. Oleic acid injections rapidly produced severe derangements in the gas exchange and mechanical properties of the lung, with an average increase in venous admixture from 16 ± 12 to 62 ± 16% ( P < 0.01), and a reduction in dynamic respiratory system compliance from 25 ± 4 to 14 ± 4 ml/cmH2O ( P < 0.01). A concomitant increase in sound power was seen in all lung regions ( P < 0.05), predominantly in frequencies 150–800 Hz. The deterioration in gas exchange and lung mechanics correlated best with concurrent spectral changes in the nondependent lung regions. Acute lung injury increases the power of breath sounds likely secondary to redistribution of ventilation from collapsed to aerated parts of the lung and improved sound transmission in dependent, consolidated areas.

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SCHWARTZ, NATHAN, and JAMES B. EISENKRAFT. "Continuous Auscultation of Heart and Bilateral Breath Sounds." Pediatrics 81, no. 5 (May 1, 1988): 745–46. http://dx.doi.org/10.1542/peds.81.5.745b.

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To the Editor.— The frequent determination of vital signs, such as heart rate and bilateral breath sounds, is a mainstay in the care of critically ill infants. Unfortunately, the routine determination of such vital signs involves the manipulation and disturbance of the infant, unnecessary risk of exposure to cold, increased risk of apnea, and infection.1,2 In addition, the frequent disruption of the infant's sleep pattern may take an unaccountable physiologic and psychologic toll. To deal with this common and challenging problem we have devised a simple and inexpensive monitoring device, using readily available supplies, which facilitates the continuous or intermittent evaluation of heart rate and bilateral breath sounds.

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Kiyokawa, Hiroshi, Matthew Greenberg, Kazuhiko Shirota, and Hans Pasterkamp. "Auditory Detection of Simulated Crackles in Breath Sounds." Chest 119, no. 6 (June 2001): 1886–92. http://dx.doi.org/10.1378/chest.119.6.1886.

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Davis, Renée L., and Michelle L. Papachrisanthou. "Asymmetrical Breath Sounds During Respiratory Syncytial Virus Season." Journal for Nurse Practitioners 13, no. 5 (May 2017): e255-e257. http://dx.doi.org/10.1016/j.nurpra.2017.02.009.

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Gavriely, N. "Analysis of breath sounds in bronchial provocation tests." American Journal of Respiratory and Critical Care Medicine 153, no. 5 (May 1996): 1469–71. http://dx.doi.org/10.1164/ajrccm.153.5.8630587.

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Vazquez, Rodrigo, Cristina Gheorghe, Frederick Ramos, Ramona Dadu, Yaw Amoateng-Adjepong, and Constantine A. Manthous. "Gurgling Breath Sounds May Predict Hospital-Acquired Pneumonia." Chest 138, no. 2 (August 2010): 284–88. http://dx.doi.org/10.1378/chest.09-2713.

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Hidalgo, Humberto A., Michael J. Wegmann, and William W. Waring. "Frequency Spectra of Normal Breath Sounds in Childhood." Chest 100, no. 4 (October 1991): 999–1002. http://dx.doi.org/10.1378/chest.100.4.999.

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Mansy, H. A., T. J. Royston, R. A. Balk, and R. H. Sandler. "Pneumothorax detection using computerised analysis of breath sounds." Medical & Biological Engineering & Computing 40, no. 5 (September 2002): 526–32. http://dx.doi.org/10.1007/bf02345450.

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Beck, Raphael, Giora Rosenhouse, Muhammad Mahagnah, Raymond M. Chow, David W. Cugell, and Noam Gavriely. "Measurements and Theory of Normal Tracheal Breath Sounds." Annals of Biomedical Engineering 33, no. 10 (October 2005): 1344–51. http://dx.doi.org/10.1007/s10439-005-5564-7.

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Ramsay, C. F. "P185 An evaluative study of breath sounds teaching." Thorax 66, Suppl 4 (December 1, 2011): A143. http://dx.doi.org/10.1136/thoraxjnl-2011-201054c.185.

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Gold, Menachem, Sasikala Mohan, and Lee Donner. "Man With Diminished Breath Sounds on the Right." Annals of Emergency Medicine 68, no. 3 (September 2016): 388–408. http://dx.doi.org/10.1016/j.annemergmed.2016.02.017.

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Bohadana, Abraham, Hava Azulai, Amir Jarjoui, George Kalak, and Gabriel Izbicki. "Influence of observer preferences and auscultatory skill on the choice of terms to describe lung sounds: a survey of staff physicians, residents and medical students." BMJ Open Respiratory Research 7, no. 1 (March 2020): e000564. http://dx.doi.org/10.1136/bmjresp-2020-000564.

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BackgroundIn contrast with the technical progress of the stethoscope, lung sound terminology has remained confused, weakening the usefulness of auscultation. We examined how observer preferences regarding terminology and auscultatory skill influenced the choice of terms used to describe lung sounds.MethodsThirty-one staff physicians (SP), 65 residents (R) and 47 medical students (MS) spontaneously described the audio recordings of 5 lung sounds classified acoustically as: (1) normal breath sound; (2) wheezes; (3) crackles; (4) stridor and (5) pleural friction rub. A rating was considered correct if a correct term or synonym was used to describe it (term use ascribed to preference). The use of any incorrect terms was ascribed to deficient auscultatory skill.ResultsRates of correct sound identification were: (i) normal breath sound: SP=21.4%; R=11.6%; MS=17.1%; (ii) wheezes: SP=82.8%; R=85.2%; MS=86.4%; (iii) crackles: SP=63%; R=68.5%; MS=70.7%; (iv) stridor: SP=92.8%; R=90%; MS=72.1% and (v) pleural friction rub: SP=35.7%; R=6.2%; MS=3.2%. The 3 groups used 66 descriptive terms: 17 were ascribed to preferences regarding terminology, and 49 to deficient auscultatory skill. Three-group agreement on use of a term occurred on 107 occasions: 70 involved correct terms (65.4%) and 37 (34.6%) incorrect ones. Rate of use of recommended terms, rather than accepted synonyms, was 100% for the wheezes and the stridor, 55% for the normal breath sound, 22% for the crackles and 14% for the pleural friction rub.ConclusionsThe observers’ ability to describe lung sounds was high for the wheezes and the stridor, fair for the crackles and poor for the normal breath sound and the pleural friction rub. Lack of auscultatory skill largely surpassed observer preference as a factor determining the choice of terminology. Wide dissemination of educational programs on lung auscultation (eg, self-learning via computer-assisted learning tools) is urgently needed to promote use of standardised lung sound terminology.

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Munir, Usama, Adnan Younus, and Vlasios Brakoulias. "A brief intervention to improve rates and quality of physical examinations for admissions to acute adult psychiatry units." Australasian Psychiatry 27, no. 6 (June 5, 2019): 641–44. http://dx.doi.org/10.1177/1039856219847512.

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Objective: To determine the frequency and quality of physical examinations within 24 h of admission to an acute adult psychiatry unit, and whether a brief intervention involving feedback to clinicians could lead to improvement. Method: Retrospective review of the electronic medical records followed by four brief feedback sessions and email correspondence, followed by a further review of the medical records 1 month later. Results: The proportion of patients receiving a physical examination increased from 36/71 (50.7%) in the initial audit to 41/64 (64.1%) in the re-audit. The mean score of the quality of physical examinations improved from 7.5 to 9.3 (out of 15). The greatest improvement on re-audit occurred in the documentation of additional cardiac sounds (33.9% increase), additional breath sounds (17.7% increase), breath sounds (17.1% increase), cardiac sounds (14.2% increase) and bowel sounds (12.5% increase). Conclusion: This audit supports the use of brief peer-led feedback to improve the rates and quality of physical examinations.

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Jati, Ariya. "Ear-Pleasing Devices in The Police’s “Every Breath You Take”." Culturalistics: Journal of Cultural, Literary, and Linguistic Studies 2, no. 3 (October 23, 2018): 39–47. http://dx.doi.org/10.14710/culturalistics.v2i3.3167.

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This essay is concerned with sound devices in The Police’s “Every Breath You Take”. The sound devices include the rhythm, metre, and rhyme in the lyric. The study is led by the relation between poetry and music, and it is intended to allow the relation to be used in the teaching of English language and literature. The study applies a textual analysis, and it adopts Cuddon’s concept of poetic sounds. The analysis shows a rhythmical metrics in the rhyming lines of the lyric. In brief, the lyric is not musical, but it is also poetic. It is expected that the study will be suitable for general readership in English language and literature, with specific interest in music.

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Amrulloh, Yusuf A., and Jawahir A. K. Haq. "Artifacts removal from breath sound recordings in pediatric population." MATEC Web of Conferences 154 (2018): 01046. http://dx.doi.org/10.1051/matecconf/201815401046.

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Breath sound recordings from pediatric subjects pose more processing complications. Children, especially the younger ones, are not able to follow instructions to stay calm during recording. This makes their recordings not only contain stationary artifacts but also non-stationary artifacts such as movement of subjects and their heartbeats. Further, the breath sounds from pediatric subjects also have lower magnitude compared to adults. In this work, we proposed to address those problems by developing a method to remove the artifacts from breath sound recordings. We implemented a combination of a Butterworth band pass filter and a discrete wavelet filter. We tested three types of wavelets (Coiflet, Symlet and Daubechies). Ten level decompositions and a set of hard thresholds were implemented in our work. Our results show that our developed method was capable of removing the artifacts significantly while maintaining the signal of interest. The highest signal to noise ratio improvement (10.65dB) was achieved by 32 orders Symlet.

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Chaves, Daniel Bruno Resende, Lívia Maia Pascoal, Beatriz Amorim Beltrão, Marília Mendes Nunes, Tânia Alteniza Leandro, Viviane Martins da Silva, and Marcos Venícios de Oliveira Lopes. "Classification tree to screen for the nursing diagnosis Ineffective airway clearance." Revista Brasileira de Enfermagem 71, no. 5 (October 2018): 2353–58. http://dx.doi.org/10.1590/0034-7167-2017-0085.

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ABSTRACT Objective: to identify the defining characteristics of Ineffective airway clearance with better predictive power using classification trees. Method: the predictive power of the defining characteristics of Ineffective airway clearance was evaluated based on classification trees generated from the data of 249 children with acute respiratory infection. Results: Ineffective cough and adventitious breath sounds were identified as the main defining characteristics when screening for Ineffective airway clearance in accordance with trees based on three different computational algorithms. Conclusion: Ineffective coughing and adventitious breath sounds had better predictive capacity for Ineffective airway clearance in the sample.

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Imai, T. "Frequency Spectral Analysis of Breath Sounds in Newborn Infants." Japanese Journal of Pediatric Pulmonology 4, no. 1 (1993): 18–27. http://dx.doi.org/10.5701/jjpp.4.18.

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Journal articles on the topic 'Breath sounds'

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