Enhanced wavelet transformation for feature extraction in highly variated ECG signal

C. L.P. Lim; W. L. Woo; S. S. Dlay

doi:10.1049/cp.2015.1763

Enhanced wavelet transformation for feature extraction in highly variated ECG signal

C. L.P. Lim, W. L. Woo, S. S. Dlay

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

8 Citations (Scopus)

Abstract

This paper proposes an adaptive signal extraction method that uses Discrete Wavelet Transformation coupled with adaptive parameters to address variated heartwave signal due to varying heartrates. The characteristic features of the heartwave signal comprises of the P-wave, QRS-complex, Twave, onset and offset of P-wave and T-wave. Using statistically deduced parameters, the PR and QT interval parameters were incorporated where the signal extraction method can be made adaptive to varying heartrate that resulted in a very reliable signal extraction methods. Work was tested on the public database where individuals underwent treadmill testing and 95% of the heartwave signal characteristics were successfully extracted.

Original language	English
Title of host publication	2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)
Publisher	IEEE
ISBN (Electronic)	978-1-78561-137-7
ISBN (Print)	978-1-78561-136-0
DOIs	https://doi.org/10.1049/cp.2015.1763
Publication status	Published - 17 Nov 2016
Externally published	Yes
Event	2nd IET International Conference on Intelligent Signal Processing 2015, ISP 2015 - London, United Kingdom Duration: 1 Dec 2015 → 2 Dec 2015

Conference

Conference	2nd IET International Conference on Intelligent Signal Processing 2015, ISP 2015
Country/Territory	United Kingdom
City	London
Period	1/12/15 → 2/12/15

Keywords

Daubercis
Discrete Wavelet Transformation
ECG Signal
Heartwave

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10.1049/cp.2015.1763

Cite this

@inproceedings{67a85787a24c4e3cbb90f6caaa79a938,

title = "Enhanced wavelet transformation for feature extraction in highly variated ECG signal",

abstract = "This paper proposes an adaptive signal extraction method that uses Discrete Wavelet Transformation coupled with adaptive parameters to address variated heartwave signal due to varying heartrates. The characteristic features of the heartwave signal comprises of the P-wave, QRS-complex, Twave, onset and offset of P-wave and T-wave. Using statistically deduced parameters, the PR and QT interval parameters were incorporated where the signal extraction method can be made adaptive to varying heartrate that resulted in a very reliable signal extraction methods. Work was tested on the public database where individuals underwent treadmill testing and 95% of the heartwave signal characteristics were successfully extracted.",

keywords = "Daubercis, Discrete Wavelet Transformation, ECG Signal, Heartwave",

author = "Lim, {C. L.P.} and Woo, {W. L.} and Dlay, {S. S.}",

year = "2016",

month = nov,

day = "17",

doi = "10.1049/cp.2015.1763",

language = "English",

isbn = "978-1-78561-136-0",

booktitle = "2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)",

publisher = "IEEE",

address = "United States",

note = "2nd IET International Conference on Intelligent Signal Processing 2015, ISP 2015 ; Conference date: 01-12-2015 Through 02-12-2015",

}

TY - GEN

T1 - Enhanced wavelet transformation for feature extraction in highly variated ECG signal

AU - Lim, C. L.P.

AU - Woo, W. L.

AU - Dlay, S. S.

PY - 2016/11/17

Y1 - 2016/11/17

N2 - This paper proposes an adaptive signal extraction method that uses Discrete Wavelet Transformation coupled with adaptive parameters to address variated heartwave signal due to varying heartrates. The characteristic features of the heartwave signal comprises of the P-wave, QRS-complex, Twave, onset and offset of P-wave and T-wave. Using statistically deduced parameters, the PR and QT interval parameters were incorporated where the signal extraction method can be made adaptive to varying heartrate that resulted in a very reliable signal extraction methods. Work was tested on the public database where individuals underwent treadmill testing and 95% of the heartwave signal characteristics were successfully extracted.

AB - This paper proposes an adaptive signal extraction method that uses Discrete Wavelet Transformation coupled with adaptive parameters to address variated heartwave signal due to varying heartrates. The characteristic features of the heartwave signal comprises of the P-wave, QRS-complex, Twave, onset and offset of P-wave and T-wave. Using statistically deduced parameters, the PR and QT interval parameters were incorporated where the signal extraction method can be made adaptive to varying heartrate that resulted in a very reliable signal extraction methods. Work was tested on the public database where individuals underwent treadmill testing and 95% of the heartwave signal characteristics were successfully extracted.

KW - Daubercis

KW - Discrete Wavelet Transformation

KW - ECG Signal

KW - Heartwave

U2 - 10.1049/cp.2015.1763

DO - 10.1049/cp.2015.1763

M3 - Conference contribution

AN - SCOPUS:85008423080

SN - 978-1-78561-136-0

BT - 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)

PB - IEEE

T2 - 2nd IET International Conference on Intelligent Signal Processing 2015, ISP 2015

Y2 - 1 December 2015 through 2 December 2015

ER -

Enhanced wavelet transformation for feature extraction in highly variated ECG signal

Abstract

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Keywords

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