NDLI: Eliminating cardiac electrical artifacts from cardiac autonomic nervous signals using a combination of empirical mode decomposition and independent component analysis.

Content Provider	IEEE Xplore Digital Library
Author	Kwang Jin Lee Eue Keun Choi Seung Min Lee Boreom Lee
Copyright Year	2013
Description	Author affiliation: Dept. of Biomed. Eng., Korea Univ., Seoul, South Korea (Seung Min Lee) \|\| Dept. of Med. Syst. Eng. (DMSE), Gwangju Inst. of Sci. & Technol. (GIST), Gwangju, South Korea (Kwang Jin Lee; Boreom Lee) \|\| Dept. of Internal Med., Seoul Nat. Univ. Hosp., Seoul, South Korea (Eue Keun Choi)
Abstract	Cardiac autonomic nervous (CAN) signals in ambulatory dogs can nowadays be measured by an implantable radio transmitter system. CAN signals are known to be related to heart failure. However, they are critically contaminated by cardiac electrical activities (CEA) which confound data analysis. We propose a method of analysis which combines empirical mode decomposition (EMD) and independent component analysis (ICA). This method composed of two steps: First, the EMD method decomposed a single channel recording into multichannel data, then we applied the ICA to these multichannel data. Using an ambulatory dog's CAN signal data from Seoul National University Hospital, we compared our approach with a commonly used high pass filter (HPF) method for various amplitudes of simulated CAN signals. Root-mean-squared errors between simulated CAN signals and CAN signals with CEA artifact were calculated for assessing the noise cancellation effect. Moreover, we observed changes in spectral content via power spectral density. Finally, we applied the proposed method to real data. Our method could not only extract and remove CEA artifact in CAN signals, but also preserved the spectral content of CAN signals.
Starting Page	5841
Ending Page	5844
File Size	1444362
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610880
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Independent component analysis Empirical mode decomposition Electromyography Algorithm design and analysis Educational institutions Cutoff frequency Electrocardiography
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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