NDLI: Electrocardiogram analysis based on the Karhunen-Loève Transform

Content Provider	IEEE Xplore Digital Library
Author	Hong Yan Yanjun Li
Copyright Year	2010
Description	Author affiliation: China Astronaut Research and Training Center, Beijing, China (Hong Yan; Yanjun Li)
Abstract	Karhunen-Loève Transform (KLT) is the statistically best block transform. In terms of decorrelation of the signal samples and repacking energy distributing, the signal dependent transform of KLT was used in noise eliminating, data compression and features extraction for ECG. Experimental results proved that KLT was proper for suppressing noise of low self-correlated property, e.g. white noise, but has little use for noise of high self-correlation such as power line interference. KLT got high compression ratio at the cost of low information fidelity, though the coding procedure might remove certain noise. KLT was more remarkable for its role in feature extraction, e.g. to distinguish between normal sinus beats from abnormal waveform morphologies. In conclusion, KLT is well suited in ECG processing of noise canceling and data compression, especially a good candidate for robust feature extraction.
Starting Page	887
Ending Page	890
File Size	571051
Page Count	4
File Format	PDF
ISBN	9781424464951
e-ISBN	9781424464982
DOI	10.1109/BMEI.2010.5639892
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	data compression Data compression principal component analysis (PCA) Transforms Interference Electrocardiography Feature extraction features extraction Karhunen-Loève Transform (KLT) Electrocardiogram (ECG) Signal to noise ratio noise canceling
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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