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)