ST Segment Extraction from Exercise ECG Signal Based on EMD and Wavelet Transform

Myocardial ischemia is always characterized by the changes in ST complex. But ischemia is not obvious at rest. Only in the state of exercise, abnormal ST will appear. The signal of ST is susceptible to noise interference which causes the inaccuracy of the ST segment detection. Combining the advantag...

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Main Authors: You Jia, Jiang Kai, Chen Hang, Wen Haoxiang
Format: Article
Language:English
Published: EDP Sciences 2015-01-01
Series:MATEC Web of Conferences
Subjects:
EMD
Online Access:http://dx.doi.org/10.1051/matecconf/20152201039
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spelling doaj-bbfff398a8f142cb8312620540303da52021-02-02T03:45:47ZengEDP SciencesMATEC Web of Conferences2261-236X2015-01-01220103910.1051/matecconf/20152201039matecconf_iceta2015_01039ST Segment Extraction from Exercise ECG Signal Based on EMD and Wavelet TransformYou Jia0Jiang Kai1Chen Hang2Wen Haoxiang3College of Biomedical Engineering& Instrument Science, Zhejiang UniversityCollege of Biomedical Engineering& Instrument Science, Zhejiang UniversityCollege of Biomedical Engineering& Instrument Science, Zhejiang UniversityCollege of Physics and Electromechanical Engineering, Shaoguan UniversityMyocardial ischemia is always characterized by the changes in ST complex. But ischemia is not obvious at rest. Only in the state of exercise, abnormal ST will appear. The signal of ST is susceptible to noise interference which causes the inaccuracy of the ST segment detection. Combining the advantages of empirical mode decomposition (EMD), the paper proposes a modified threshold method to filter a serious of noise from exercise ECG. Extracted from the ECG feature, it includes ST segment detection, with wavelet transform. In the end, the method is tested with synthetic exercise data and real exercise ECG data. The results of ST segment detection are accurate and this method can be applied in practical exercise.http://dx.doi.org/10.1051/matecconf/20152201039EMDwavelet transformST segmentexercise ECG
collection DOAJ
language English
format Article
sources DOAJ
author You Jia
Jiang Kai
Chen Hang
Wen Haoxiang
spellingShingle You Jia
Jiang Kai
Chen Hang
Wen Haoxiang
ST Segment Extraction from Exercise ECG Signal Based on EMD and Wavelet Transform
MATEC Web of Conferences
EMD
wavelet transform
ST segment
exercise ECG
author_facet You Jia
Jiang Kai
Chen Hang
Wen Haoxiang
author_sort You Jia
title ST Segment Extraction from Exercise ECG Signal Based on EMD and Wavelet Transform
title_short ST Segment Extraction from Exercise ECG Signal Based on EMD and Wavelet Transform
title_full ST Segment Extraction from Exercise ECG Signal Based on EMD and Wavelet Transform
title_fullStr ST Segment Extraction from Exercise ECG Signal Based on EMD and Wavelet Transform
title_full_unstemmed ST Segment Extraction from Exercise ECG Signal Based on EMD and Wavelet Transform
title_sort st segment extraction from exercise ecg signal based on emd and wavelet transform
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2015-01-01
description Myocardial ischemia is always characterized by the changes in ST complex. But ischemia is not obvious at rest. Only in the state of exercise, abnormal ST will appear. The signal of ST is susceptible to noise interference which causes the inaccuracy of the ST segment detection. Combining the advantages of empirical mode decomposition (EMD), the paper proposes a modified threshold method to filter a serious of noise from exercise ECG. Extracted from the ECG feature, it includes ST segment detection, with wavelet transform. In the end, the method is tested with synthetic exercise data and real exercise ECG data. The results of ST segment detection are accurate and this method can be applied in practical exercise.
topic EMD
wavelet transform
ST segment
exercise ECG
url http://dx.doi.org/10.1051/matecconf/20152201039
work_keys_str_mv AT youjia stsegmentextractionfromexerciseecgsignalbasedonemdandwavelettransform
AT jiangkai stsegmentextractionfromexerciseecgsignalbasedonemdandwavelettransform
AT chenhang stsegmentextractionfromexerciseecgsignalbasedonemdandwavelettransform
AT wenhaoxiang stsegmentextractionfromexerciseecgsignalbasedonemdandwavelettransform
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