Bimodal classification algorithm for atrial fibrillation detection from m-health ECG recordings.

INTRODUCTION: Atrial Fibrillation (AF) is the most common cardiac arrhythmia, presenting a significant independent risk factor for stroke and thromboembolism. With the emergence of m-Health devices, the importance of automatic detection of AF in an off-clinic setting is growing. This study demonstrates the performance of a bimodal classifier for distinguishing AF from sinus rhythm (SR) that could be used for automated detection of AF episodes.

METHODS: Surface recordings from a hand-held research device and standard electrocardiograms (ECG) were collected and analyzed from 68 subjects. An additional 48 subjects from the MIT-BIH Arrythmia Database were also analyzed. All ECGs were blindly reviewed by physicians independently of the bimodal algorithm analysis. The algorithm selects an artifact-free 6-s ECG segment out of a 20-s long recording and computes a spectral Frequency Dispersion Metric (FDM) and a temporal R-R interval variability (VRR) index.

RESULTS: Scatter plots of the VRR and FDM indices revealed two distinct clusters. The bimodal scattering of the indices revealed a linear classification boundary that could be employed to differentiate the SR from AF waveforms. The selected classification boundary was able to correctly differentiate all the subjects from both datasets into either SR or AF groups, except for 3 SR subjects from the MIT-BIH dataset.

CONCLUSION: Our bimodal classification algorithm was demonstrated to successfully acquire, analyze and interpret ECGs for the presence of AF indicating its potential to support m-Health diagnosis, monitoring, and management of therapy in AF patients.