Methods for Adventitious Respiratory Sound Analyzing Applications Based on Smartphones: A Survey

被引:13
作者
Tabatabaei, Seyed Amir Hossein [1 ]
Fischer, Patrick [1 ]
Schneider, Henning [1 ]
Koehler, Ulrich [2 ]
Gross, Volker [3 ]
Sohrabi, Keywan [3 ]
机构
[1] Justus Liebig Univ Giessen, Inst Med Informat, D-35390 Giessen, Germany
[2] Univ Hosp Marburg & Giessen, Dept Internal Med Pneumol Intens Care & Sleep Med, D-35043 Marburg, Germany
[3] Univ Appl Sci, Fac Hlth Sci, D-35390 Giessen, Germany
关键词
Lung; Acoustics; Diseases; Sensors; Medical diagnostic imaging; Monitoring; Smartphone; signal processing; acoustic; adventitious respiratory sound; machine learning; classification parameters; COPD; feature engineering; EXTREME LEARNING-MACHINE; CRACKLES RALES; LUNG SOUNDS; NETWORKS; SENSORS; SIGNAL; COUGH; PRIVACY; DESIGN; MOTION;
D O I
10.1109/RBME.2020.3002970
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.
引用
收藏
页码:98 / 115
页数:18
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