The cost of doing science on the cloud: the Montage example
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Proceedings of the 1st ACM International Health Informatics Symposium
Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
SPINE: a domain-specific framework for rapid prototyping of WBSN applications
Software—Practice & Experience
Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing
IEEE Transactions on Parallel and Distributed Systems
Dynamic Fractal Clustering Technique for SOAP Web Messages
SCC '11 Proceedings of the 2011 IEEE International Conference on Services Computing
ECG data compression using wavelets and higher order statistics methods
IEEE Transactions on Information Technology in Biomedicine
BodyCloud: Integration of Cloud Computing and body sensor networks
CLOUDCOM '12 Proceedings of the 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom)
Editorial: Integration of Cloud computing and body sensor networks
Future Generation Computer Systems
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E-health applications deal with a huge amount of biological signals such as ECG generated by body sensor networks (BSN). Moreover, many healthcare organizations require access to these records. Therefore, cloud is widely used in healthcare systems to serve as a central service repository. To minimize the traffic going to and coming from cloud ECG compression is one of the proposed solutions to overcome this problem. In this paper, a new fractal based ECG lossy compression technique is proposed. It is found that the ECG signal self-similarity characteristic can be used efficiently to achieve high compression ratios. The proposed technique is based on modifying the popular fractal model to be used in compression in conjunction with the iterated function system. The ECG signal is divided into equal blocks called range blocks. Subsequently, another down-sampled copy of the ECG signal is created which is called domain. For each range block the most similar block in the domain is found. As a result, fractal coefficients (i.e. parameters defining fractal compression model) are calculated and stored inside the compressed file for each ECG signal range block. In order to make our technique cloud friendly, the decompression operation is designed in such a way that allows the user to retrieve part of the file (i.e. ECG segment) without decompressing the whole file. Therefore, the clients do not need to download the full compressed file before they can view the result. The proposed algorithm has been implemented and compared with other existing lossy ECG compression techniques. It is found that the proposed technique can achieve a higher compression ratio of 40 with lower Percentage Residual Difference (PRD) Value less than 1%.