{"title":"High-fidelity data transmission of multi vital signs for distributed e-health applications","authors":"Jia-Li Ma, Mubo Chen, M. Dong","doi":"10.1109/ISBB.2014.6820923","DOIUrl":null,"url":null,"abstract":"Developing a portable e-health system based on mobile devices such as smart phone or iPad etc. to monitor and diagnose cardiovascular diseases (CVDs) easily, timely and flexibly is attractive and flourishing with the benefit of vigorous development of embedded-link technology and e-health. Yet, confronting the numerous CVDs, complicated medical diagnosis and users' increasing requirements, these source-limited portable devices are rather restricted to handle such emerging issues. Thus, a remote backyard on-line support and assistance center in such a distributed e-health system is necessitated to perform instant feedback upon users' help requests. To ensure prompt and reliable communication between thousands mobile devices and support center, fast and accurate multi vital signs transmission using high efficient compression/decompression technique becomes the kernel of success or failure. This paper tackles such a bottleneck problem, presents a versatile and high-fidelity multi vital signs compression method using adaptive Fourier decomposition to decompose signal beat into weighted orthogonal components. An intelligent signal type detection and automatic parameter adjustment scheme is designed and implemented, thus compress different bio-signals with compression ratio (CR) > 5.6 and normalized percentage relative difference (PRDN) <; 7.3%. Validated by transmitting various vital signs, such as electrocardiography (ECG), sphygmography (SPG) and heart sound (HS) signals, the proposed method exhibits universal and robust applicability for multi vital signs and achieves competitive performance compared with prior works, making distributed e-health applications be realistic.","PeriodicalId":265886,"journal":{"name":"2014 IEEE International Symposium on Bioelectronics and Bioinformatics (IEEE ISBB 2014)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Symposium on Bioelectronics and Bioinformatics (IEEE ISBB 2014)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBB.2014.6820923","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Developing a portable e-health system based on mobile devices such as smart phone or iPad etc. to monitor and diagnose cardiovascular diseases (CVDs) easily, timely and flexibly is attractive and flourishing with the benefit of vigorous development of embedded-link technology and e-health. Yet, confronting the numerous CVDs, complicated medical diagnosis and users' increasing requirements, these source-limited portable devices are rather restricted to handle such emerging issues. Thus, a remote backyard on-line support and assistance center in such a distributed e-health system is necessitated to perform instant feedback upon users' help requests. To ensure prompt and reliable communication between thousands mobile devices and support center, fast and accurate multi vital signs transmission using high efficient compression/decompression technique becomes the kernel of success or failure. This paper tackles such a bottleneck problem, presents a versatile and high-fidelity multi vital signs compression method using adaptive Fourier decomposition to decompose signal beat into weighted orthogonal components. An intelligent signal type detection and automatic parameter adjustment scheme is designed and implemented, thus compress different bio-signals with compression ratio (CR) > 5.6 and normalized percentage relative difference (PRDN) <; 7.3%. Validated by transmitting various vital signs, such as electrocardiography (ECG), sphygmography (SPG) and heart sound (HS) signals, the proposed method exhibits universal and robust applicability for multi vital signs and achieves competitive performance compared with prior works, making distributed e-health applications be realistic.