Medical & Biological Engineering & Computing

, Volume 53, Issue 3, pp 263–273 | Cite as

Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation

  • Ursula Ravens
  • Deniz Katircioglu-Öztürk
  • Erich Wettwer
  • Torsten Christ
  • Dobromir Dobrev
  • Niels Voigt
  • Claire Poulet
  • Simone Loose
  • Jana Simon
  • Agnes Stein
  • Klaus Matschke
  • Michael Knaut
  • Emre Oto
  • Ali Oto
  • H. Altay Güvenir
Original Article


Ex vivo recorded action potentials (APs) in human right atrial tissue from patients in sinus rhythm (SR) or atrial fibrillation (AF) display a characteristic spike-and-dome or triangular shape, respectively, but variability is huge within each rhythm group. The aim of our study was to apply the machine-learning algorithm ranking instances by maximizing the area under the ROC curve (RIMARC) to a large data set of 480 APs combined with retrospectively collected general clinical parameters and to test whether the rules learned by the RIMARC algorithm can be used for accurately classifying the preoperative rhythm status. APs were included from 221 SR and 158 AF patients. During a learning phase, the RIMARC algorithm established a ranking order of 62 features by predictive value for SR or AF. The model was then challenged with an additional test set of features from 28 patients in whom rhythm status was blinded. The accuracy of the risk prediction for AF by the model was very good (0.93) when all features were used. Without the seven AP features, accuracy still reached 0.71. In conclusion, we have shown that training the machine-learning algorithm RIMARC with an experimental and clinical data set allows predicting a classification in a test data set with high accuracy. In a clinical setting, this approach may prove useful for finding hypothesis-generating associations between different parameters.


Atrial fibrillation Risk prediction RIMARC algorithm Human right atrial action potentials Clinical parameters 



Atrial fibrillation


Action potential amplitude (mV)


Action potential duration at 20 % of repolarization (ms)


Action potential duration at 50 % of repolarization (ms)


Action potential duration at 90 % of repolarization (ms)


Maximum rate of depolarization (Vs−1)


Maximum area under ROC curve-based discretization


“Plateau potential” defined as the mean potential (mV) in the time window between 20 % of APD90 plus 5 ms


Ranking instances by maximizing the area under the ROC curve


Resting membrane potential (mV)


Receiver operating characteristics


Sinus rhythm



The authors thank all patients who took part in this study. We gratefully acknowledge the excellent technical assistance of Konstanze Fischer, and the help of Dr. Katrin Ploetze with the logistics of handling patient’s files. The authors are grateful for the generous financial support by the European Union (FP7-Health T2-2010-261057 “EUTRAF”).

Supplementary material

11517_2014_1232_MOESM1_ESM.doc (107 kb)
Supplementary material 1 (DOC 107 kb)
11517_2014_1232_MOESM2_ESM.doc (73 kb)
Supplementary material 2 (DOC 73 kb)
11517_2014_1232_MOESM3_ESM.xlsx (68 kb)
Supplementary material 3 (XLSX 68 kb)


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Copyright information

© International Federation for Medical and Biological Engineering 2014

Authors and Affiliations

  • Ursula Ravens
    • 1
    • 11
  • Deniz Katircioglu-Öztürk
    • 2
    • 3
  • Erich Wettwer
    • 1
  • Torsten Christ
    • 1
    • 4
  • Dobromir Dobrev
    • 1
    • 5
  • Niels Voigt
    • 1
    • 5
  • Claire Poulet
    • 1
    • 6
  • Simone Loose
    • 1
  • Jana Simon
    • 1
  • Agnes Stein
    • 7
  • Klaus Matschke
    • 8
  • Michael Knaut
    • 8
  • Emre Oto
    • 3
  • Ali Oto
    • 9
  • H. Altay Güvenir
    • 10
  1. 1.Department of Pharmacology and Toxicology, Medical Faculty Carl Gustav CarusTU DresdenDresdenGermany
  2. 2.Department of Medical Informatics, Informatics InstituteMiddle East Technical UniversityAnkaraTurkey
  3. 3.MITSAnkaraTurkey
  4. 4.Department of Experimental Pharmacology and ToxicologyUniversity Medical Center Hamburg-EppendorfHamburgGermany
  5. 5.Institute of Pharmacology, Faculty of MedicineUniversity of Duisburg-EssenEssenGermany
  6. 6.Imperial CollegeLondonUK
  7. 7.Department of AnesthesiologyHeart Center DresdenDresdenGermany
  8. 8.Clinic for Cardiac SurgeryHeart Center DresdenDresdenGermany
  9. 9.Department of CardiologyHacettepe University HospitalAnkaraTurkey
  10. 10.Department of Computer EngineeringBilkent UniversityAnkaraTurkey
  11. 11.Institut für Pharmakologie und ToxikologieTU DresdenDresdenGermany

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