Estimation and prediction of drug therapy on the termination of atrial fibrillation by autoregressive model with exogenous inputs

Chin En Kuo, Sheng Fu Liang, Shao Sheng Lu, Tang Ching Kuan, Chih Sheng Lin

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1 Citation (Scopus)


Atrial fibrillation (AF) is the most frequent cardiac arrhythmia seen in clinical practice. Several therapeutical approaches have been developed to terminate the AF and the effects are evaluated by the reduction of the wavelet number after the treatments. Most of the previous studies focus on modeling and analysis of the mechanism, and the characteristic of AF. But no one discusses about the prediction of the result after the drug treatment. This paper is the first study to predict whether the drug treatment for AF is active or not. In this paper, the linear autoregressive model with exogenous inputs (ARX) that models the system output-input relationship by solving linear regression equations with least-squares method was developed and applied to estimate the effects of pharmacological therapy on AF. Recordings (224-site bipolar recordings) of plaque electrode arrays placed on the right and left atria of pigs with sustained AF induced by rapid atrial pacing were used to train and test the ARX models. The cardiac mapping data from 12 pigs treated with intravenous administration of antiarrhythmia drug, propafenone (PPF), or dl-sotalol (STL) were evaluated. The recordings of cardiac activity before the drug treatment were input to the model and the model output reported the estimated wavelet number of atria after the drug treatment. The results show that the predicting accuracy rate corresponding to the PPF and STL treatments was 100% and 92%, respectively. It is expected that the developed ARX model can be further extended to assist the clinical staffs to choose the effective treatments for the AF patients in the future.

Original languageEnglish
Pages (from-to)153-161
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
Publication statusPublished - 2013

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management


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