Artificial intelligence techniques used in respiratory sound analysis - a systematic review

被引:37
作者
Palaniappan, Rajkumar [1 ]
Sundaraj, Kenneth [1 ]
Sundaraj, Sebastian [2 ]
机构
[1] Univ Malaysia Perlis, Al Rehab Res Grp, Arau 02600, Perlis, Malaysia
[2] Klang Gen Hosp, Dept Anesthesiol, Klang 41200, Selangor, Malaysia
来源
BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK | 2014年 / 59卷 / 01期
关键词
artificial intelligence; lung disease; respiratory sounds; statistical computing; systematic review; LUNG SOUNDS; NEURAL-NETWORK; BREATH SOUNDS; FEATURE SETS; CLASSIFICATION; EXTRACTION; FREQUENCY; CRACKLES;
D O I
10.1515/bmt-2013-0074
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Artificial intelligence (AI) has recently been established as an alternative method to many conventional methods. The implementation of AI techniques for respiratory sound analysis can assist medical professionals in the diagnosis of lung pathologies. This article highlights the importance of AI techniques in the implementation of computer-based respiratory sound analysis. Articles on computer-based respiratory sound analysis using AI techniques were identified by searches conducted on various electronic resources, such as the IEEE, Springer, Elsevier, PubMed, and ACM digital library databases. Brief descriptions of the types of respiratory sounds and their respective characteristics are provided. We then analyzed each of the previous studies to determine the specific respiratory sounds/pathology analyzed, the number of subjects, the signal processing method used, the AI techniques used, and the performance of the AI technique used in the analysis of respiratory sounds. A detailed description of each of these studies is provided. In conclusion, this article provides recommendations for further advancements in respiratory sound analysis.
引用
收藏
页码:7 / 18
页数:12
相关论文
共 83 条
[1]   An Automated Computerized Auscultation and Diagnostic System for Pulmonary Diseases [J].
Abbas, Ali ;
Fahim, Atef .
JOURNAL OF MEDICAL SYSTEMS, 2010, 34 (06) :1149-1155
[2]   COMPARISON OF THE ACOUSTIC PROPERTIES OF 6 POPULAR STETHOSCOPES [J].
ABELLA, M ;
FORMOLO, J ;
PENNEY, DG .
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 1992, 91 (04) :2224-2228
[3]  
Aboofazeli M, 2004, P ANN INT IEEE EMBS, V26, P3816
[4]   Design of a DSP-based instrument for real-time classification of pulmonary sounds [J].
Alsmadi, Sameer ;
Kahya, Yasemin P. .
COMPUTERS IN BIOLOGY AND MEDICINE, 2008, 38 (01) :53-61
[5]  
Alsmadi SS, 2002, P ANN INT IEEE EMBS, P1771, DOI 10.1109/IEMBS.2002.1106645
[6]  
[Anonymous], 2012, INT J COMPUTER SCI
[7]  
[Anonymous], 2003, P 20 INT C INT C MAC, DOI DOI 10.5555/3041838.3041845
[8]   Unsupervised Classification of Respiratory Sound Signal into Snore/No-Snore classes [J].
Azarbarzin, Ali ;
Moussavi, Zahra .
2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2010, :3666-3669
[9]  
Bahoura M, 2003, CCECE 2003: CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1-3, PROCEEDINGS, P1457
[10]  
Bahoura M, 2004, ANN INT C IEEE CAN C, P9