Artificial Neural Networks for Acoustic Lung Signals Classification

被引：0

作者：

Orjuela-Canon, Alvaro D. ^{[1
]}

Gomez-Cajas, Diego F. ^{[1
]}

Jimenez-Moreno, Robinson ^{[2
]}

机构：

[1] Univ Antonio Narino, Elect & Biomed Fac, GIBIO, Bogota, Colombia

[2] Univ Antonio Narino, GRITEL, Dept Elect Engn, Bogota, Colombia

来源：

PROGRESS IN PATTERN RECOGNITION IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2014 | 2014年 / 8827卷

关键词：

Artificial Neural Networks; Multilayer Perceptron; Mel Frequency Cepstral Coefficients; Acoustic Lung Signals; SOUNDS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A classification of acoustic lung signals for the respiratory disease diagnosis problem is studied in the present work. Models based on artificial neural networks, using Mel Frequency Cepstral Coefficients for training are employed in this task. Results show that neural networks are comparable, and in some cases better, with other classification techniques as Gaussian Mixture Models, that work on the same database.

引用

页码：214 / 221

页数：8

共 50 条

[1] Classification of telephone signals with use of artificial neural networks
Tarczynski, A
Skorkowski, G
Bushchenko, Y
Igbinedion, I
8TH WORLD MULTI-CONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL VI, PROCEEDINGS: IMAGE, ACOUSTIC, SIGNAL PROCESSING AND OPTICAL SYSTEMS, TECHNOLOGIES AND APPLICATIONS, 2004, : 108 - 113
[2] Classification of Electroencephalogram Signals Using Artificial Neural Networks
Rodrigues, Pedro Miguel
Teixeira, Joao Paulo
2010 3RD INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS (BMEI 2010), VOLS 1-7, 2010, : 808 - 812
[3] Use of artificial neural networks for classification of noisy seismic signals
K. V. Kislov
V. V. Gravirov
Seismic Instruments, 2017, 53 (1) : 87 - 101
[4] Audio signals and artificial neural networks for classification of plastic resins for recycling
Tessarini, Leticia
Fileti, Ana Maria Frattini
DIGITAL CHEMICAL ENGINEERING, 2022, 5
[5] Feature-based classification of myoelectric signals using artificial neural networks
P. J. Gallant
E. L. Morin
L. E. Peppard
Medical and Biological Engineering and Computing, 1998, 36 : 485 - 489
[6] Feature-based classification of myoelectric signals using artificial neural networks
Gallant, PJ
Morin, EL
Peppard, LE
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 1998, 36 (04) : 485 - 489
[7] Classification of modulation signals using statistical signal characterization and artificial neural networks
Hossen, Abdulnasir
Al-Wadahi, Fakhri
Jervase, Joseph A.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2007, 20 (04) : 463 - 472
[8] Classification of Uroflowmetry and EMG Signals of Pediatric Patients using Artificial Neural Networks
Yalcinkaya, Fikret
Caliskan, Ozan
Erogul, Osman
Irkilata, Cem
Kopru, Burak
Coguplugil, Emrah
2017 MEDICAL TECHNOLOGIES NATIONAL CONGRESS (TIPTEKNO), 2017,
[9] Classification of Respiratory Pathology in Pulmonary Acoustic Signals Using Parametric Features and Artificial Neural Network
Palaniappan, Rajkumar
Sundaraj, Kenneth
Sundaraj, Sebastian
Huliraj, N.
Revadi, S. S.
Archana, B.
2014 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH (IEEE ICCIC), 2014, : 134 - 139
[10] A multi-level feature fusion artificial neural network for classification of acoustic emission signals
Huang, Jinliang
Zhu, Zhaolin
Chen, Zhihao
Lu, Haotian
Yang, Zijin
ANNALS OF THE NEW YORK ACADEMY OF SCIENCES, 2025, 1544 (01) : 223 - 241

← 1 2 3 4 5 →