title: Real-time automated detection of clonic seizures in newborns.
abstract: OBJECTIVE
  The aim of this study is to apply a real-time algorithm for clonic neonatal seizures detection, based on a low complexity image processing approach extracting the differential average luminance from videotaped body movements.
  
  
  METHODS
  23 video-EEGs from 12 patients containing 78 electrographically confirmed neonatal seizures of clonic type were reviewed and all movements were divided into noise, random movements, clonic seizures or other seizure types. Six video-EEGs from 5 newborns without seizures were also reviewed. Videos were then separately analyzed using either single, double or triple windows (these latter with 50% overlap) each of a 10s duration.
  
  
  RESULTS
  With a decision threshold set at 0.5, we obtained a sensitivity of 71% (corresponding specificity: 69%) with double-window processing for clonic seizures diagnosis. The discriminatory power, indicated by the Area Under the Curve (AUC), is higher with two interlaced windows (AUC=0.796) than with single (AUC=0.788) or triple-window (AUC=0.728). Among subjects without neonatal seizures, our algorithm showed a specificity of 91% with double-window processing.
  
  
  CONCLUSIONS
  Our algorithm reliably detects neonatal clonic seizures and differentiates them from either noise, random movements and other seizure types.
  
  
  CONCLUSIONS
  It could represent a low-cost, low complexity, real-time automated screening tool for clonic neonatal seizures.
authors.affiliation: Child Neuropsychiatric Unit, Neuroscience Department, University of Parma, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Pisani
authors.affiliation: Child Neuropsychiatric Unit, Neuroscience Department, University of Parma, Italy. Electronic address: carlotta.spagnoli@gmail.com.
authors.forename: Carlotta
authors.initials: C
authors.lastname: Spagnoli
authors.affiliation: Child Neuropsychiatric Unit, Neuroscience Department, University of Parma, Italy.
authors.forename: Elena
authors.initials: E
authors.lastname: Pavlidis
authors.affiliation: Child Neuropsychiatric Unit, Neuroscience Department, University of Parma, Italy.
authors.forename: Carlotta
authors.initials: C
authors.lastname: Facini
authors.affiliation: Department of Information Engineering, University of Parma, Italy.
authors.forename: Guy Mathurin
authors.initials: GM
authors.lastname: Kouamou Ntonfo
authors.affiliation: Department of Information Engineering, University of Parma, Italy.
authors.forename: Gianluigi
authors.initials: G
authors.lastname: Ferrari
authors.affiliation: Department of Information Engineering, University of Parma, Italy.
authors.forename: Riccardo
authors.initials: R
authors.lastname: Raheli
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007231:Infant, Newborn; D007232:Infant, Newborn, Diseases / Q000175:diagnosis*; D009068:Movement; D012372:ROC Curve; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014741:Video Recording / Q000379:methods*
keywords: Automated detection; Neonatal seizures; Newborns; Seizures