ECG waveform analysis by significant point extraction. II. Pattern matching

Quin Lan Cheng; Ho Soo Lee; Nitish V. Thakor

doi:10.1016/0010-4809(87)90031-0

ECG waveform analysis by significant point extraction. II. Pattern matching

Quin Lan Cheng, Ho Soo Lee, Nitish V. Thakor

School of Medicine

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

From a set of significant points which characterizes the ECG waveform, the pattern matching algorithm detects and classifies QRS complexes. R waves are detected from the analysis of global curvature. Next, the morphology of the QRS complex is determined. QRS complexes with different morphologies are classified by a correlation algorithm. This method is sensitive to changes in shape, such as that of abnormal QRS complexes. The algorithm should be useful in automated analysis of waveforms, such as ECG signals recorded in clinical environments.

Original language	English (US)
Pages (from-to)	428-442
Number of pages	15
Journal	Computers and Biomedical Research
Volume	20
Issue number	5
DOIs	https://doi.org/10.1016/0010-4809(87)90031-0
State	Published - Oct 1987

ASJC Scopus subject areas

Medicine (miscellaneous)

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10.1016/0010-4809(87)90031-0

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title = "ECG waveform analysis by significant point extraction. II. Pattern matching",

abstract = "From a set of significant points which characterizes the ECG waveform, the pattern matching algorithm detects and classifies QRS complexes. R waves are detected from the analysis of global curvature. Next, the morphology of the QRS complex is determined. QRS complexes with different morphologies are classified by a correlation algorithm. This method is sensitive to changes in shape, such as that of abnormal QRS complexes. The algorithm should be useful in automated analysis of waveforms, such as ECG signals recorded in clinical environments.",

author = "Cheng, {Quin Lan} and Lee, {Ho Soo} and Thakor, {Nitish V.}",

note = "Funding Information: This research was supported in part by a 1985 Presidential Young Investigator /\ward from the National Science Foundation and a Research Career Development Award from the National Institute of Health (to N.V.T.) and a grant from the Whitaker Foundation.",

year = "1987",

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doi = "10.1016/0010-4809(87)90031-0",

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T1 - ECG waveform analysis by significant point extraction. II. Pattern matching

AU - Cheng, Quin Lan

AU - Lee, Ho Soo

AU - Thakor, Nitish V.

N1 - Funding Information: This research was supported in part by a 1985 Presidential Young Investigator /\ward from the National Science Foundation and a Research Career Development Award from the National Institute of Health (to N.V.T.) and a grant from the Whitaker Foundation.

PY - 1987/10

Y1 - 1987/10

N2 - From a set of significant points which characterizes the ECG waveform, the pattern matching algorithm detects and classifies QRS complexes. R waves are detected from the analysis of global curvature. Next, the morphology of the QRS complex is determined. QRS complexes with different morphologies are classified by a correlation algorithm. This method is sensitive to changes in shape, such as that of abnormal QRS complexes. The algorithm should be useful in automated analysis of waveforms, such as ECG signals recorded in clinical environments.

AB - From a set of significant points which characterizes the ECG waveform, the pattern matching algorithm detects and classifies QRS complexes. R waves are detected from the analysis of global curvature. Next, the morphology of the QRS complex is determined. QRS complexes with different morphologies are classified by a correlation algorithm. This method is sensitive to changes in shape, such as that of abnormal QRS complexes. The algorithm should be useful in automated analysis of waveforms, such as ECG signals recorded in clinical environments.

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ECG waveform analysis by significant point extraction. II. Pattern matching

Abstract

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