Computer-Aided Detection of Respiratory Sounds in Bronchial Asthma Patients Based on Machine Learning Method

Abstract

The aim of the study is to develop a method for detection of pathological respiratory sound, caused by bronchial asthma, with the aid of machine learning techniques.Materials and Methods. To build and train neural networks, we used the records of respiratory sounds of bronchial asthma patients at different stages of the disease (n=951) aged from several months to 47 years old and healthy volunteers (n=167). The sounds were recorded with calm breathing at four points: at the oral cavity, above the trachea, on the chest (second intercostal space on the right side), and at a point on the back.Results. The method developed for computer-aided detection of respiratory sounds allows to diagnose sounds typical for bronchial asthma in 89.4% of cases with 89.3% sensitivity and 86.0% specificity regardless of sex and age of the patients, stage of the disease, and the point of sound recording.

eHealth Technologies for Monitoring Pediatric Asthma at Home: Scoping Review

“…Many studies focused on the development of algorithms to accurately identify wheeze [ 128 , 129 , 135 , 138 , 139 ] or cough [ 118 , 126 , 140 , 141 ] by signal processing. Wheezing algorithms traditionally used short-time Fourier transformations to retrieve dominant sound components and extract identifying wheezing features [ 128 , 139 ].…”

“…Many studies focused on the development of algorithms to accurately identify wheeze [ 128 , 129 , 135 , 138 , 139 ] or cough [ 118 , 126 , 140 , 141 ] by signal processing. Wheezing algorithms traditionally used short-time Fourier transformations to retrieve dominant sound components and extract identifying wheezing features [ 128 , 139 ]. New methods have been developed to increase its sensitivity for low-intensity wheezes [ 129 ] and to decrease the computation time for real-time application [ 138 ].…”

“…None of the retrieved studies used video recordings. There were several ways that cough or wheeze were recorded at home, such as microphones placed in the room [22], close to the mouth [125][126][127][128][129], or at the chest or trachea [128][129][130]. Yu et al [131] used a soft stethoscope that reduced ambient sounds, and Satat et al [132] used an array of stethoscopes to allow localization of respiratory sounds.…”

Method for Detecting Pathology of Internal Organs Using Bioelectrography

“…Such methods include studies that allow, on the basis of the X-ray images and machinelearning methods used, us to identify the pathology of the respiratory organs [1][2][3]. The methods based on the classification of untreated lung sounds and the detection of pneumonia and chronic obstructive pulmonary diseases can be attributed [4][5][6]. A separate area is the work on the detection of oncological diseases [7][8][9].…”

“…The listed methods have a high resource intensity in terms of the applied software and hardware. Summarizing the results [1][2][3][4][5][6][7][8][9][10][11][12][13][14], it is advisable to note that with almost all the methods, there is no possibility of automating the detection of pathology or deviations in the functioning of a particular organ (Table 1). In the table, a «+» sign indicates the presence of this property from the column header in the method from the first column in this source, «−» its absence.…”