title: Computational Electrocardiography: Revisiting Holter ECG Monitoring.
abstract: BACKGROUND
  Since 1942, when Goldberger introduced the 12-lead electrocardiography (ECG), this diagnostic method has not been changed.
  
  
  OBJECTIVE
  After 70 years of technologic developments, we revisit Holter ECG from recording to understanding.
  
  
  METHODS
  A fundamental change is fore-seen towards "computational ECG" (CECG), where continuous monitoring is producing big data volumes that are impossible to be inspected conventionally but require efficient computational methods. We draw parallels between CECG and computational biology, in particular with respect to computed tomography, computed radiology, and computed photography. From that, we identify technology and methodology needed for CECG.
  
  
  RESULTS
  Real-time transfer of raw data into meaningful parameters that are tracked over time will allow prediction of serious events, such as sudden cardiac death. Evolved from Holter's technology, portable smartphones with Bluetooth-connected textile-embedded sensors will capture noisy raw data (recording), process meaningful parameters over time (analysis), and transfer them to cloud services for sharing (handling), predicting serious events, and alarming (understanding). To make this happen, the following fields need more research: i) signal processing, ii) cycle decomposition; iii) cycle normalization, iv) cycle modeling, v) clinical parameter computation, vi) physiological modeling, and vii) event prediction.
  
  
  CONCLUSIONS
  We shall start immediately developing methodology for CECG analysis and understanding.
authors.affiliation: Prof. Dr. Thomas Martin Deserno, Aachen University of Technology (RWTH), Department of Medical Informatics, Pauwelsstraße 30, 52074 Aachen, Germany, E-mail: deserno@ieee.org.
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Deserno
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Marx
mesh_terms: D019295:Computational Biology; D003201:Computers; D015716:Electrocardiography, Ambulatory; D006801:Humans
keywords: Automated ECG analysis; big data; computational biology; mobile health; signal processing