Compressor-Based Classification for Atrial Fibrillation Detection

Atrial fibrillation (AF) is one of the most common arrhythmias with challenging public health implications. Automatic detection of AF episodes is therefore one of the most important tasks in biomedical engineering. In this paper, we apply the recently introduced method of compressor-based text classification to the task of AF detection (binary classification between heart rhythms). We investigate the normalised compression distance applied to $\Delta$RR and RR-interval sequences, the configuration of the k-Nearest Neighbour classifier, and an optimal window length. We achieve good classification results (avg. sensitivity = 97.1%, avg. specificity = 91.7%, best sensitivity of 99.8%, best specificity of 97.6% with 5-fold cross-validation). Obtained performance is close to the best specialised AF detection algorithms. Our results suggest that gzip classification, originally proposed for texts, is suitable for biomedical data and continuous stochastic sequences in general.