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Electrophysiological Signal Analysis and Visualization using Cloudwave for Epilepsy Clinical Research
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Electrophysiological Signal Analysis and Visualization using Cloudwave for Epilepsy Clinical Research

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Epilepsy is the most common serious neurological disorder affecting 50-60 million persons worldwide. Electrophysiologi-cal data recordings, such as electroencephalogram (EEG), are the gold standard ...

Epilepsy is the most common serious neurological disorder affecting 50-60 million persons worldwide. Electrophysiologi-cal data recordings, such as electroencephalogram (EEG), are the gold standard for diagnosis and pre-surgical evaluation in epilepsy patients. The increasing trend towards multi-center clinical studies require signal visualization and analysis tools to support real time interaction with signal data in a collabora-tive environment, which are cannot be supported by traditional desktop-based standalone applications. As part of the Preven-tion and Risk Identification of SUDEP Mortality (PRISM) project, we have developed a Web-based electrophysiology data visualization and analysis platform called Cloudwave using highly scalable open source cloud computing infrastruc-ture. Cloudwave is integrated with the PRISM patient cohort identification tool called MEDCIS (Multi-modality Epilepsy Data Capture and Integration System). The Epilepsy and Sei-zure Ontology (EpSO) underpins both Cloudwave and MEDCIS to support query composition and result retrieval. Cloudwave is being used by clinicians and research staff at the University Hospital - Case Medical Center (UH-CMC) Epi-lepsy Monitoring Unit (EMU) and will be progressively de-ployed at four EMUs in the United States and the United Kingdom as part of the PRISM project.

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    Electrophysiological Signal Analysis and Visualization using Cloudwave for Epilepsy Clinical Research Electrophysiological Signal Analysis and Visualization using Cloudwave for Epilepsy Clinical Research Presentation Transcript

    • Catherine  Jayapandian   Case  Western  Reserve  University,  Ohio,  USA    
    •   o  Background:  Electrophysiological  Data   Management   o  Challenges:  Big  Data,  Mul;center  studies   o  Cloudwave  Framework:  Features,  Components   o  Current  Results   o  Future  Direc;ons  
    • o  What  is  Epilepsy?   n  Most  common  neurological  disorder  affec;ng  60   million  worldwide   o  How  is  Epilepsy  detected?   n  Mul;-­‐modal  Electrophysiological  evalua;ons  like   EEG,  EKG,  BP,  O2  and  CO2,  Sleep  data,  video   n  Electroencephalogram  (EEG)  is  the  gold  standard   for  diagnosis  and  pre-­‐surgical  evalua;on  
    • o  Mul;-­‐center  Clinical  Study  for  Preven;on  and   Iden;fica;on  of  Risks  in  SUDEP  Pa;ents     o  Key  Components   n  MEDCIS     Mul$modality  Epilepsy  Data  Capture   and  Integra$on  System   n  OPIC   Online  Pa$ent  Informa$on  Capture   n  EpiDEA   Epilepsy  Data  Extrac$on  and   Annota$on   n  Cloudwave   Electrophysiological  Signal  “Big  Data”   on  the  Cloud  
    • o  Ontology-­‐driven  Web-­‐ based  Electrophysiological   Epilepsy  Signal  Query,   Visualiza;on  and  Analysis   Framework   o  Provides  High   Performance  Cloud   CompuBng  Infrastructure   for  handling   Electrophysiological  “Big   Data”  
    • o  PaBents  Cohorts  are  selected  using  the  MEDCIS  Query   Builder   o  PaBent  ID  is  linked  to  Cloudwave  Signal  Viewer   All  studies  and  the  related  seizure  events  for  the   pa;ent  can  be  viewed  using  Cloudwave  interface  
    • o  SelecBon  of  PaBent  Study,  Montage,  Signal/ Channels  for  display   o  Facilitate  creaBon  of  new  montages   (referenBal  and  bipolar)  
    • o  SelecBon  of  Seizure  Events/AnnotaBons   Mouse  zooming  to  ;me-­‐ range  of  interest   Expor;ng  as  image  and   prin;ng   Visually  navigate  using  scroll  to   select  ;me-­‐range  
    • o  SelecBon  of  Filters  –  SensiBvity,  HF  Filter    and  Time  Constant  
    • o  Electrophysiological  “Big”  Signal  Data  Storage  on   HDFS  by  collec$ng  similar  signals  for  correla$on  and   quan$ta$ve  signal  analysis  using  MapReduce   distributed  processing   n  Cloudwave:  Distributed  Processing  of  “Big  Data”  from   Electrophysiological  Recordings  for  Epilepsy  Clinical  Research  Using   Hadoop,  AMIA  2013  (accepted)   o  Computa;on  of  complex  Signal  Processing  algorithms  –   Cardiac  Arrhythmia,  Respiratory  Arrhythmia  and  related   measurements  for  real-­‐;me  rendering  on  Cloudwave  web   interface  (work  in  progress)  
    • o  PRISM  is  NIH  funded,  mul--­‐disciplinary  and  mul--­‐ center   (4   par;cipa;ng   centers)   –   recrui;ng   1200   pa;ents     o  Cloudwave   establishes   the   capability   for   comprehensive  comparaBve  studies  of  SUDEP  and   near-­‐SUDEP  cases  vs.  cohort  survivors   o  Cloudwave  is  a  key  component  of  PRISM  project–   facilitate  the  management  of  Electrophysiological   “Big”   Data   and   Real   Time   Web   Rendering   of   Mul-modal  signals   o  For  more  details,  please  visit:  hap://prism.case.edu   o  Contact:  Catherine  Jayapandian  (cpj3@case.edu)