Heart beats in the cloud: distributed analysis of electrophysiological 'Big Data' using cloud computing for epilepsy clinical research.
Objective: The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop no...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 21; no. 2; pp. 263 - 272 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Mar2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104021499&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104021499 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Mar2014 vid: 21 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104021499 NLM24326538 2012470488 10.1136/amiajnl-2013-002156 NLM24326538 PMC3932471 104021499 ppf: 263 ppct: 9 formats: tig: atl: Heart beats in the cloud: distributed analysis of electrophysiological 'Big Data' using cloud computing for epilepsy clinical research. aug: au: Sahoo, Satya S Jayapandian, Catherine Garg, Gaurav Kaffashi, Farhad Chung, Stephanie Bozorgi, Alireza Chen, Chien-Hun Loparo, Kenneth Lhatoo, Samden D Zhang, Guo-Qiang affil: Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA. sug: subj: Algorithms Computer Communication Networks Economics Resource Databases Electrocardiography Epilepsy Physiopathology Signal Processing, Computer Assisted Arrhythmia Complications Arrhythmia Diagnosis Research, Medical Privacy and Confidentiality Cost Benefit Analysis Death, Sudden Heart Function Tests Epilepsy Complications Health Insurance Portability and Accountability Act Human Internet United States ab: Objective: The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop novel algorithms and informatics approaches using new cloud computing technologies as well as ontologies for collaborative multicenter studies.Materials and Methods: We present the Cloudwave platform, which (a) defines parallelized algorithms for computing cardiac measures using the MapReduce parallel programming framework, (b) supports real-time interaction with large volumes of electrophysiological signals, and (c) features signal visualization and querying functionalities using an ontology-driven web-based interface. Cloudwave is currently used in the multicenter National Institute of Neurological Diseases and Stroke (NINDS)-funded Prevention and Risk Identification of SUDEP (sudden unexplained death in epilepsy) Mortality (PRISM) project to identify risk factors for sudden death in epilepsy.Results: Comparative evaluations of Cloudwave with traditional desktop approaches to compute cardiac measures (eg, QRS complexes, RR intervals, and instantaneous heart rate) on epilepsy patient data show one order of magnitude improvement for single-channel ECG data and 20 times improvement for four-channel ECG data. This enables Cloudwave to support real-time user interaction with signal data, which is semantically annotated with a novel epilepsy and seizure ontology.Discussion: Data privacy is a critical issue in using cloud infrastructure, and cloud platforms, such as Amazon Web Services, offer features to support Health Insurance Portability and Accountability Act standards.Conclusion: The Cloudwave platform is a new approach to leverage of large-scale electrophysiological data for advancing multicenter clinical research. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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