4/5/2024 0 Comments Normal lung sounds![]() Clin Linguist Phon 22:917–936Įmmanouilidou D, Elhilal M (2013) Characterization of noise contaminations in lung sound recordings. Iyer SN, Oller DK (2008) Fundamental frequency development in typically developing infants and infants with severe-to-profound hearing loss. Lederman D (2010) Estimation of infants’ cry fundamental frequency using a modified SIFT algorithm. Braz J Med Biol Res 42:674–684Įllington LE, Gilman RH, Tielsch JM, Steinhoff M, Figueroa D, Rodriguez S, Caffo B, Tracey B, Elhilali M, West J, Checkley W (2012) Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study. Riella RJ, Nohama P, Maia JM (2009) Method for automatic detection of wheezing in lung sounds. Kandaswamy A, Kumar CS, Ramanathan RP, Jayaraman S, Malmurugan N (2004) Neural classification of lung sounds using wavelet coefficients. Conf Proc IEEE Eng Med Biol Soc 1:2856–2859 ![]() Kahya YP, Yeginer M, Bilgic B (2006) Classifying respiratory sounds with different feature sets. Waitman LR, Clarkson KP, Barwise JA, King PH (2000) Representation and classification of breath sounds recorded in an intensive care setting using neural networks. Int J Ther Rehabil 17:69–74Įlphick HE, Lancaster GA, Solis A, Majumdar A, Gupta R, Smyth RL (2004) Validity and reliability of acoustic analysis of respiratory sounds in infants. Morrow B, Angus L, Greenhough D, Hansen A, McGregor G, Olivier O, Shillington L, Van der Horn P, Argent A (2010) The reliability of identifying bronchial breathing by auscultation. Biomed Signal Process Control 3:244–254Įrtel PY, Lawrence M, Brown RK, Stern AM (1966) Stethoscope acoustics: II. Lu X, Bahoura M (2008) An integrated automated system for crackles extraction and classification. Reichert S, Gass R, Brandt C, Andrès E (2008) Analysis of respiratory sounds: state of the art. Respir Care 49:1490–1497Ībaza AA, Day JB, Reynolds JS, Mahmoud AM, Goldsmith WT, McKinney WG, Petsonk EL, Frazer DG (2009) Classification of voluntary cough sound and airflow patterns for detecting abnormal pulmonary function. Murphy RL, Vyshedskiy A, Power-Charnitsky VA, Bana DS, Marinelli PM, Wong-Tse A, Paciej R (2004) Automated lung sound analysis in patients with pneumonia. Guntupalli KK, Alapat PM, Bandi VD, Kushnir I (2008) Validation of automatic wheeze detection in patients with obstructed airways and in healthy subjects. Gurung A, Scrafford CG, Tielsch JM, Levine OS, Checkley W (2011) Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis. Grenier MC, Gagnon K, Genest J Jr, Durand J, Durand LG (1998) Clinical comparison of acoustic and electronic stethoscopes and design of a new electronic stethoscope. While sound-reduction techniques will improve analysis, we offer a novel, reproducible tool for sound analysis in real-world environments. A comparison with adult studies revealed differences in the extracted features for children. Lung sound extracted features varied significantly with child characteristics and lung site. Features like spectral peak width, lower-frequency Mel-frequency cepstral coefficients, and spectro-temporal modulations also showed variations with recording site. Older children had a faster decaying spectrum than younger ones. We identified ten distinct spectral and spectro-temporal signal parameters and most demonstrated linear relationships with age, height, and weight, while no differences with genders were noted. Mean age, height, and weight among study participants were 2.2 years (SD 1.4), 84.7 cm (SD 13.2), and 12.0 kg (SD 3.6), respectively and, 47 % were boys. Heavy-crying segments were automatically rejected and features extracted from spectral and temporal signal representations contributed to profiling of lung sounds. ![]() 151 (81 %) of the recordings were eligible for further analysis. Lung sounds were recorded at eight thoracic sites using a digital stethoscope. Methodsġ86 healthy children with normal pulmonary exams and without respiratory complaints were enrolled at a tertiary care hospital in Lima, Peru. ![]() We described features of normal lung sounds in young children using a novel signal processing approach to lay a foundation for identifying pathologic respiratory sounds. Recent advances in electronic auscultation and signal processing have yet to find clinical acceptance however, computerized lung sound analysis may be ideal for pediatric populations in settings, where skilled healthcare providers are commonly unavailable. Lung auscultation has long been a standard of care for the diagnosis of respiratory diseases.
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