Paper Info
Title
Heart beats in the cloud: distributed analysis of electrophysiological '' using cloud computing for epilepsy clinical research.
Abstract
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.
Year
DOI
Venue
2014
10.1136/amiajnl-2013-002156
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
Keywords
Field
DocType
Electrophsyiological Big Data,Epilepsy and Seizure,Cloudwave,SUDEP,Ontology,MapReduce
Data science,Ontology (information science),Informatics,Data mining,Health Insurance Portability and Accountability Act,Computer science,Visualization,Information privacy,Big data,The Internet,Cloud computing
Journal
Volume
Issue
ISSN
21
2
1067-5027
Citations 
PageRank 
References 
17
1.20
20
Authors
10
Name
Order
Citations
PageRank
Satya Sanket Sahoo114214.92
Catherine Jayapandian2504.77
Gaurav Garg323220.61
Farhad Kaffashi4171.54
Stephanie Chung5171.20
Alireza Bozorgi6503.73
Chien-Hung Chen727036.65
Kenneth A Loparo816222.22
Samden D Lhatoo9708.58
Guo-Qiang Zhang108614.85