Classification of respiratory sounds by using an artificial neural network

M. C. Sezgin; Z. Dokur; T. Ölmez; M. Korürek

Classification of respiratory sounds by using an artificial neural network

M. C. Sezgin
, Z. Dokur^*
, T. Ölmez
, M. Korürek

^*Bu çalışma için yazışmadan sorumlu yazar

Elektronik ve Hab.Müh.Bölümü

Istanbul Technical University

Araştırma sonucu: Dergiye katkı › Konferans makalesi › bilirkişi

7 Atıf (Scopus)

Özet

In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. Wavelet transform is applied to a window containing 256 samples. Elements of the feature vectors are obtained from the wavelet coefficients. The best feature elements are selected by using dynamic programming. Grow and Learn (GAL) neural network is used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.

Orijinal dil	İngilizce
Sayfa (başlangıç-bitiş)	697-699
Sayfa sayısı	3
Dergi	Annual Reports of the Research Reactor Institute, Kyoto University
Hacim	1
Yayın durumu	Yayınlandı - 2001
Etkinlik	23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Istanbul, Turkey Süre: 25 Eki 2001 → 28 Eki 2001

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

SKH 3 Sağlık ve Kaliteli Yaşam

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

@article{d62b3388091f4570b1538f986e4c8756,

title = "Classification of respiratory sounds by using an artificial neural network",

abstract = "In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. Wavelet transform is applied to a window containing 256 samples. Elements of the feature vectors are obtained from the wavelet coefficients. The best feature elements are selected by using dynamic programming. Grow and Learn (GAL) neural network is used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.",

keywords = "Artificial neural network, Classification of biomedical signals, Respiratory sounds",

author = "Sezgin, \{M. C.\} and Z. Dokur and T. {\"O}lmez and M. Kor{\"u}rek",

year = "2001",

language = "English",

volume = "1",

pages = "697--699",

journal = "Annual Reports of the Research Reactor Institute, Kyoto University",

issn = "0454-9244",

note = "23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; Conference date: 25-10-2001 Through 28-10-2001",

}

TY - JOUR

T1 - Classification of respiratory sounds by using an artificial neural network

AU - Sezgin, M. C.

AU - Dokur, Z.

AU - Ölmez, T.

AU - Korürek, M.

PY - 2001

Y1 - 2001

N2 - In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. Wavelet transform is applied to a window containing 256 samples. Elements of the feature vectors are obtained from the wavelet coefficients. The best feature elements are selected by using dynamic programming. Grow and Learn (GAL) neural network is used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.

AB - In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. Wavelet transform is applied to a window containing 256 samples. Elements of the feature vectors are obtained from the wavelet coefficients. The best feature elements are selected by using dynamic programming. Grow and Learn (GAL) neural network is used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.

KW - Artificial neural network

KW - Classification of biomedical signals

KW - Respiratory sounds

UR - https://www.scopus.com/pages/publications/0035781581

M3 - Conference article

AN - SCOPUS:0035781581

SN - 0454-9244

VL - 1

SP - 697

EP - 699

JO - Annual Reports of the Research Reactor Institute, Kyoto University

JF - Annual Reports of the Research Reactor Institute, Kyoto University

T2 - 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 25 October 2001 through 28 October 2001

ER -

Classification of respiratory sounds by using an artificial neural network

Özet

BM SKH

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap