Du, C. "Prediction of Paroxysmal Atrial Fibrillation (PAF) Onset through Analysis of Inter-beat Intervals (IBI)"

Abstract

PAF is a type of progressive cardiac arrhythmia that poses severe health risks, sometimes leading to ventricular arrhythmia and post-operative mortality. Some of the difficulties with treating PAF include screening for patients with the disorder, detecting episode occurrences, and predicting occurrences. To address these issues, electrocardiogram (ECG) data from the PhysioNet Online Database was used to develop a technique to screen, detect, and predict the onset of PAF. Methodologies explored included Hidden Markov Modeling on inter-beat intervals, entropy, and heart-rate spectrograms. Initial testing indicates the technique to be discriminant between PAF and non-PAF (possibly other cardiac disorder) patients (89% sensitivity and 55% specificity). Even more promising is its ability to discriminate between PAF patients and healthy individuals (89% sensitivity and 81% specificity). Both results are from data not involved in training. The IBI-based algorithm could be incorporated into medical devices with the potential of contributing to new healthcare technology.