Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb,Croatia.
Published: April 2014
AI Article Synopsis
Article Abstract
Building upon the findings from the field of automated recognition of respiratory sound patterns, we propose a wearable wireless sensor implementing on-board respiratory sound acquisition and classification, to enable continuous monitoring of symptoms, such as asthmatic wheezing. Low-power consumption of such a sensor is required in order to achieve long autonomy. Considering that the power consumption of its radio is kept minimal if transmitting only upon (rare) occurrences of wheezing, we focus on optimizing the power consumption of the digital signal processor (DSP). Based on a comprehensive review of asthmatic wheeze detection algorithms, we analyze the computational complexity of common features drawn from short-time Fourier transform (STFT) and decision tree classification. Four algorithms were implemented on a low-power TMS320C5505 DSP. Their classification accuracies were evaluated on a dataset of prerecorded respiratory sounds in two operating scenarios of different detection fidelities. The execution times of all algorithms were measured. The best classification accuracy of over 92%, while occupying only 2.6% of the DSP's processing time, is obtained for the algorithm featuring the time-frequency tracking of shapes of crests originating from wheezing, with spectral features modeled using energy.
Objective: To develop and try out a convenient and accessible for use technology for testing the pulmonary function in children and adults, including in patients with chronic bronchopulmonary pathology.
Material And Methods: The article presents the results of Lazarev sound-breathing test (LST) in different cohorts of children and adults, including pregnant women and children suffering from chronic bronchopulmonary diseases, in particular cystic fibrosis.
Results: A direct dependence of the LST indicators' level from the age of children and adolescents has been obtained.
Objectives: To investigate the clinical implications of the obstructive pattern on plain chest radiography, defined as peribronchial cuffing or hyperinflation, in young children with severe lower respiratory tract infections (LRTIs).
Methods: We reviewed all children aged 3‒59 months with LRTIs who underwent radiography and polymerase chain reaction in a Korean emergency department from 2016 through 2020. The radiographs were read as consolidation, peribronchial cuffing, or hyperinflation, with each interrater reliability computed.
Objective: Previous studies have reported that the noise generated by dental equipment can interfere with the auscultation of respiratory sounds during sedation. Therefore, this study aimed to identify whether positing the acoustic sensor on the chest or cervical position would be least susceptible to interference from dental suction device noise, a prominent noise noted during respiratory sound monitoring during dental sedation.
Methods: This prospective cohort study was conducted with 30 students.
Background: Lung auscultation using a smartphone built-in microphone is promising for home monitoring of pediatric respiratory diseases. Our aim was to compare respiratory sounds recorded by a smartphone and a digital stethoscope by assessing the proportion of quality recordings and adventitious sounds detected by each device.
Methods: A comparative early feasibility study with children from a public school in Northern Portugal was conducted.
This report details a case of acute idiopathic velopharyngeal insufficiency in a previously healthy eight-year-old girl, presenting with sudden voice alteration and nasal regurgitation following mild respiratory symptoms. Physical examination identified unilateral velar paralysis with open rhinolalia, without additional neurological deficits. Extensive diagnostic evaluation, including nasopharyngoscopy, cerebral and cervical imaging, and infectious serologies, yielded unremarkable findings.
