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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Online Access: | http://dx.doi.org/10.1051/matecconf/20152201039 |
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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 |
_version_ |
1724307163857289216 |