Extraction of Linear and Non-Linear Features of Electrocardiogram Signal and Classification

Sudip Deb, Sheikh Md Rabiul Islam, Fatema Tuj Johura, Xu Huang

Research output: A Conference proceeding or a Chapter in BookConference contribution

2 Citations (Scopus)

Abstract

ECG signal for a private creature is totally different because of the distinctive heart structure. The ambition of feature extraction of electrocardiogram signal would permit productive detection of irregularities and economic projection due to any kind of heart confusion. Some dominant feature options are going to be extracted from ECG signals namely frequency, mean, median, skewness, kurtosis, standard deviation, different kinds of norms and so on. So, there is a need for strong and robust mathematical model to extract such helpful parameters. This research work is related to an associate degree reconciling mathematical analysis model i.e. Hilbert Huang Transform (HHT). The Hilbert-Huang transform technique is enforced to evaluate the nonlinear and non-stationary representation of the graphical signal. It is distinctive and totally disparate from the current ways of investigation and will not crave a prior function supporting information. The efficiency of the planned theme is confirmed through different classification techniques.

Original languageEnglish
Title of host publicationICEEE 2017
Subtitle of host publication2nd International Conference on Electrical and Electronic Engineering
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-4
Number of pages4
ISBN (Electronic)9781538633410
ISBN (Print)9781538633403
DOIs
Publication statusPublished - 27 Dec 2017
Event2nd International Conference on Electrical and Electronic Engineering, ICEEE 2017 - Rajshahi, Bangladesh
Duration: 27 Dec 201729 Dec 2017

Conference

Conference2nd International Conference on Electrical and Electronic Engineering, ICEEE 2017
CountryBangladesh
CityRajshahi
Period27/12/1729/12/17

Fingerprint

electrocardiography
Electrocardiography
kurtosis
applications of mathematics
skewness
Feature extraction
confusion
irregularities
norms
Mathematical transformations
pattern recognition
Mathematical models
economics
standard deviation
mathematical models
Economics
projection

Cite this

Deb, S., Rabiul Islam, S. M., Johura, F. T., & Huang, X. (2017). Extraction of Linear and Non-Linear Features of Electrocardiogram Signal and Classification. In ICEEE 2017: 2nd International Conference on Electrical and Electronic Engineering (pp. 1-4). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CEEE.2017.8412857
Deb, Sudip ; Rabiul Islam, Sheikh Md ; Johura, Fatema Tuj ; Huang, Xu. / Extraction of Linear and Non-Linear Features of Electrocardiogram Signal and Classification. ICEEE 2017: 2nd International Conference on Electrical and Electronic Engineering. IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 1-4
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abstract = "ECG signal for a private creature is totally different because of the distinctive heart structure. The ambition of feature extraction of electrocardiogram signal would permit productive detection of irregularities and economic projection due to any kind of heart confusion. Some dominant feature options are going to be extracted from ECG signals namely frequency, mean, median, skewness, kurtosis, standard deviation, different kinds of norms and so on. So, there is a need for strong and robust mathematical model to extract such helpful parameters. This research work is related to an associate degree reconciling mathematical analysis model i.e. Hilbert Huang Transform (HHT). The Hilbert-Huang transform technique is enforced to evaluate the nonlinear and non-stationary representation of the graphical signal. It is distinctive and totally disparate from the current ways of investigation and will not crave a prior function supporting information. The efficiency of the planned theme is confirmed through different classification techniques.",
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Deb, S, Rabiul Islam, SM, Johura, FT & Huang, X 2017, Extraction of Linear and Non-Linear Features of Electrocardiogram Signal and Classification. in ICEEE 2017: 2nd International Conference on Electrical and Electronic Engineering. IEEE, Institute of Electrical and Electronics Engineers, pp. 1-4, 2nd International Conference on Electrical and Electronic Engineering, ICEEE 2017, Rajshahi, Bangladesh, 27/12/17. https://doi.org/10.1109/CEEE.2017.8412857

Extraction of Linear and Non-Linear Features of Electrocardiogram Signal and Classification. / Deb, Sudip; Rabiul Islam, Sheikh Md; Johura, Fatema Tuj; Huang, Xu.

ICEEE 2017: 2nd International Conference on Electrical and Electronic Engineering. IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 1-4.

Research output: A Conference proceeding or a Chapter in BookConference contribution

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T1 - Extraction of Linear and Non-Linear Features of Electrocardiogram Signal and Classification

AU - Deb, Sudip

AU - Rabiul Islam, Sheikh Md

AU - Johura, Fatema Tuj

AU - Huang, Xu

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Y1 - 2017/12/27

N2 - ECG signal for a private creature is totally different because of the distinctive heart structure. The ambition of feature extraction of electrocardiogram signal would permit productive detection of irregularities and economic projection due to any kind of heart confusion. Some dominant feature options are going to be extracted from ECG signals namely frequency, mean, median, skewness, kurtosis, standard deviation, different kinds of norms and so on. So, there is a need for strong and robust mathematical model to extract such helpful parameters. This research work is related to an associate degree reconciling mathematical analysis model i.e. Hilbert Huang Transform (HHT). The Hilbert-Huang transform technique is enforced to evaluate the nonlinear and non-stationary representation of the graphical signal. It is distinctive and totally disparate from the current ways of investigation and will not crave a prior function supporting information. The efficiency of the planned theme is confirmed through different classification techniques.

AB - ECG signal for a private creature is totally different because of the distinctive heart structure. The ambition of feature extraction of electrocardiogram signal would permit productive detection of irregularities and economic projection due to any kind of heart confusion. Some dominant feature options are going to be extracted from ECG signals namely frequency, mean, median, skewness, kurtosis, standard deviation, different kinds of norms and so on. So, there is a need for strong and robust mathematical model to extract such helpful parameters. This research work is related to an associate degree reconciling mathematical analysis model i.e. Hilbert Huang Transform (HHT). The Hilbert-Huang transform technique is enforced to evaluate the nonlinear and non-stationary representation of the graphical signal. It is distinctive and totally disparate from the current ways of investigation and will not crave a prior function supporting information. The efficiency of the planned theme is confirmed through different classification techniques.

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Deb S, Rabiul Islam SM, Johura FT, Huang X. Extraction of Linear and Non-Linear Features of Electrocardiogram Signal and Classification. In ICEEE 2017: 2nd International Conference on Electrical and Electronic Engineering. IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 1-4 https://doi.org/10.1109/CEEE.2017.8412857