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

IF 1.1 Q4 MEDICINE, RESEARCH & EXPERIMENTAL

Sovremennye Tehnologii v Medicine Pub Date : 2022-01-01 DOI:10.17691/stm2022.14.5.05

A Gelman, E G Furman, N M Kalinina, S V Malinin, G B Furman, V S Sheludko, V L Sokolovsky

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引用次数: 1

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.

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基于机器学习方法的支气管哮喘患者呼吸音的计算机辅助检测。

本研究的目的是在机器学习技术的帮助下，开发一种检测支气管哮喘引起的病理性呼吸音的方法。材料与方法:采用年龄在几个月~ 47岁的支气管哮喘不同阶段患者(n=951)和健康志愿者(n=167)的呼吸音记录构建和训练神经网络。这些声音是在四个点平静呼吸的情况下录制的:口腔、气管上方、胸部(右侧第二肋间隙)和背部的一个点。结果:建立的呼吸声计算机辅助检测方法，无论患者的性别、年龄、疾病分期和录音点如何，均能诊断出89.4%的支气管哮喘典型音，灵敏度为89.3%，特异性为86.0%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sovremennye Tehnologii v Medicine MEDICINE, RESEARCH & EXPERIMENTAL-

CiteScore

1.80

自引率

0.00%

发文量