NDLI: An effective low-complexity multi-vital-signs compression technique for embedded-link e-home healthcare

Content Provider	IEEE Xplore Digital Library
Author	Jia-Li Ma Yuan-Lian Cui Ming-Chui Dong
Copyright Year	2013
Description	Author affiliation: INESC, Univ. of Macau, Macau, China (Jia-Li Ma; Yuan-Lian Cui; Ming-Chui Dong)
Abstract	Transplanting the existing e-home healthcare system to source-limited embedded-link device for home-use health monitoring, intelligent medical diagnosis and wireless transmission is attractive. Yet, constrains of portable storage, computing and transmission promote the need of data compression for such applications. Existing compression techniques are mostly desktop-computer-based and computation-consuming, making them unsuitable for mobile device. To tackle such a bottleneck problem, this paper addresses an effective low-complexity multi-vital-signs compression technique based on orthogonal polynomial decomposition (OPD) algorithm using Hermite functions. The technique is proposed and operated on the designated healthcare system with optimized parameters. Validated and tested with cardiovascular disease (CVD) diagnosis based on sphygmogram both experimentally and clinically, the proposed technique achieves comparable good performance with distortion less than 2% and compression ratio up to 6, and preserves significant pathological features of multi-vital-signs for clinical diagnosis. The proposed technique is highly robust even for freaky and pathological signals. In addition, the compressed results reflecting morphological features can be directly adapted to the subsequent medical analysis without further decompression.
Starting Page	1177
Ending Page	1181
File Size	1100447
Page Count	5
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6609716
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	Data compression Pathology Electrocardiography Polynomials Algorithm design and analysis Monitoring
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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