Heart beats in the cloud: distributed analysis of electrophysiological 'Big Data' using cloud computing for epilepsy clinical research

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 algorit...

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Veröffentlicht in:Journal of the American Medical Informatics Association : JAMIA Jg. 21; H. 2; S. 263
Hauptverfasser: Sahoo, Satya S, Jayapandian, Catherine, Garg, Gaurav, Kaffashi, Farhad, Chung, Stephanie, Bozorgi, Alireza, Chen, Chien-Hun, Loparo, Kenneth, Lhatoo, Samden D, Zhang, Guo-Qiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England 01.03.2014
Schlagworte:
Algorithms
Arrhythmias, Cardiac - complications
Arrhythmias, Cardiac - diagnosis
Biomedical Research
Computer Communication Networks - economics
Confidentiality
Cost-Benefit Analysis
Databases, Factual
Death, Sudden
Electrocardiography
Electrophysiologic Techniques, Cardiac
Epilepsy - complications
Epilepsy - physiopathology
Health Insurance Portability and Accountability Act
Humans
Internet
Signal Processing, Computer-Assisted
United States
United States
Epilepsy and Seizure
Ontology
SUDEP
Cloudwave
Electrophsyiological Big Data
MapReduce
ISSN:1527-974X, 1527-974X
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Zusammenfassung: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. 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. 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. 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. The Cloudwave platform is a new approach to leverage of large-scale electrophysiological data for advancing multicenter clinical research.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1527-974X
1527-974X
DOI:10.1136/amiajnl-2013-002156