title: Heart beats in the cloud: distributed analysis of electrophysiological 'Big Data' 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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
  
  
  CONCLUSIONS
  The Cloudwave platform is a new approach to leverage of large-scale electrophysiological data for advancing multicenter clinical research.
authors: Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
authors: Satya S
authors: SS
authors: Sahoo
authors: Catherine
authors: C
authors: Jayapandian
authors: Gaurav
authors: G
authors: Garg
authors: Farhad
authors: F
authors: Kaffashi
authors: Stephanie
authors: S
authors: Chung
authors: Alireza
authors: A
authors: Bozorgi
authors: Chien-Hun
authors: CH
authors: Chen
authors: Kenneth
authors: K
authors: Loparo
authors: Samden D
authors: SD
authors: Lhatoo
authors: Guo-Qiang
authors: GQ
authors: Zhang
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000150:complications / Q000175:diagnosis; D035843:Biomedical Research; D003195:Computer Communication Networks / Q000191:economics; D003219:Confidentiality; D003362:Cost-Benefit Analysis; D016208:Databases, Factual; D003645:Death, Sudden; D004562:Electrocardiography; D022062:Electrophysiologic Techniques, Cardiac; D004827:Epilepsy / Q000150:complications / Q000503:physiopathology*; D020408:Health Insurance Portability and Accountability Act; D006801:Humans; D020407:Internet; D012815:Signal Processing, Computer-Assisted; D014481:United States
keywords: Cloudwave; Electrophsyiological Big Data; Epilepsy and Seizure; MapReduce; Ontology; SUDEP