Gabor feature extraction for electrocardiogram signals

Gwo Giun Lee; Jhen Yue Hu; Chun Fu Chen; Huan Hsiang Lin

doi:10.1109/BioCAS.2012.6418436

Gabor feature extraction for electrocardiogram signals

Gwo Giun Lee, Jhen Yue Hu, Chun Fu Chen, Huan Hsiang Lin

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

In this paper, the useful features for clinical diagnosis from electrocardiogram (ECG) signals have been extracted to speed up the diagnosis decision from doctors. Based on the background information of ECG signals, after analyzing some presented methods for ECG feature extraction, algorithm for each feature extraction have been proposed. The major methods for feature extraction we proposed contain short-time Fourier transform (STFT), Gabor filter, and matching process using Gaussian models with various scales. According to the experimental results, less comparative error shows that the proposed algorithm surpasses state-of-arts as stated in the literature for extracting features on ECG signals.

Original language	English
Title of host publication	2012 IEEE Biomedical Circuits and Systems Conference
Subtitle of host publication	Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications
Pages	304-307
Number of pages	4
DOIs	https://doi.org/10.1109/BioCAS.2012.6418436
Publication status	Published - 2012 Dec 1
Event	2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Hsinchu, Taiwan Duration: 2012 Nov 28 → 2012 Nov 30

Publication series

Name	2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications

Other

Other	2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012
Country/Territory	Taiwan
City	Hsinchu
Period	12-11-28 → 12-11-30

All Science Journal Classification (ASJC) codes

Hardware and Architecture
Biomedical Engineering

Access to Document

10.1109/BioCAS.2012.6418436

Cite this

Lee, G. G., Hu, J. Y., Chen, C. F., & Lin, H. H. (2012). Gabor feature extraction for electrocardiogram signals. In 2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications (pp. 304-307). Article 6418436 (2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications). https://doi.org/10.1109/BioCAS.2012.6418436

Lee, Gwo Giun ; Hu, Jhen Yue ; Chen, Chun Fu et al. / Gabor feature extraction for electrocardiogram signals. 2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications. 2012. pp. 304-307 (2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications).

@inproceedings{f562471d1fe7464aa77a2517b420eefc,

title = "Gabor feature extraction for electrocardiogram signals",

abstract = "In this paper, the useful features for clinical diagnosis from electrocardiogram (ECG) signals have been extracted to speed up the diagnosis decision from doctors. Based on the background information of ECG signals, after analyzing some presented methods for ECG feature extraction, algorithm for each feature extraction have been proposed. The major methods for feature extraction we proposed contain short-time Fourier transform (STFT), Gabor filter, and matching process using Gaussian models with various scales. According to the experimental results, less comparative error shows that the proposed algorithm surpasses state-of-arts as stated in the literature for extracting features on ECG signals.",

author = "Lee, {Gwo Giun} and Hu, {Jhen Yue} and Chen, {Chun Fu} and Lin, {Huan Hsiang}",

year = "2012",

month = dec,

day = "1",

doi = "10.1109/BioCAS.2012.6418436",

language = "English",

isbn = "9781467322935",

series = "2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications",

pages = "304--307",

booktitle = "2012 IEEE Biomedical Circuits and Systems Conference",

note = "2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 ; Conference date: 28-11-2012 Through 30-11-2012",

}

Lee, GG, Hu, JY, Chen, CF & Lin, HH 2012, Gabor feature extraction for electrocardiogram signals. in 2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications., 6418436, 2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications, pp. 304-307, 2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012, Hsinchu, Taiwan, 12-11-28. https://doi.org/10.1109/BioCAS.2012.6418436

Gabor feature extraction for electrocardiogram signals. / Lee, Gwo Giun; Hu, Jhen Yue; Chen, Chun Fu et al.
2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications. 2012. p. 304-307 6418436 (2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Gabor feature extraction for electrocardiogram signals

AU - Lee, Gwo Giun

AU - Hu, Jhen Yue

AU - Chen, Chun Fu

AU - Lin, Huan Hsiang

PY - 2012/12/1

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N2 - In this paper, the useful features for clinical diagnosis from electrocardiogram (ECG) signals have been extracted to speed up the diagnosis decision from doctors. Based on the background information of ECG signals, after analyzing some presented methods for ECG feature extraction, algorithm for each feature extraction have been proposed. The major methods for feature extraction we proposed contain short-time Fourier transform (STFT), Gabor filter, and matching process using Gaussian models with various scales. According to the experimental results, less comparative error shows that the proposed algorithm surpasses state-of-arts as stated in the literature for extracting features on ECG signals.

AB - In this paper, the useful features for clinical diagnosis from electrocardiogram (ECG) signals have been extracted to speed up the diagnosis decision from doctors. Based on the background information of ECG signals, after analyzing some presented methods for ECG feature extraction, algorithm for each feature extraction have been proposed. The major methods for feature extraction we proposed contain short-time Fourier transform (STFT), Gabor filter, and matching process using Gaussian models with various scales. According to the experimental results, less comparative error shows that the proposed algorithm surpasses state-of-arts as stated in the literature for extracting features on ECG signals.

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M3 - Conference contribution

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Lee GG, Hu JY, Chen CF, Lin HH. Gabor feature extraction for electrocardiogram signals. In 2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications. 2012. p. 304-307. 6418436. (2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications). doi: 10.1109/BioCAS.2012.6418436

Gabor feature extraction for electrocardiogram signals

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