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.

翻译：心房颤动（AF）是最常见的心律失常之一，具有严峻的公共卫生影响。因此，房颤发作的自动检测是生物医学工程领域最重要的任务之一。本文采用最新提出的基于压缩器的文本分类方法，应用于房颤检测任务（即心电节律的二分类问题）。我们研究了应用于ΔRR和RR间期序列的归一化压缩距离、k近邻分类器的配置以及最优窗口长度。实验取得了良好的分类结果（五折交叉验证下：平均灵敏度97.1%，平均特异度91.7%；最佳灵敏度99.8%，最佳特异度97.6%）。所获性能接近最优化的专用房颤检测算法。研究结果表明，最初为文本提出的gzip分类方法同样适用于生物医学数据及一般的连续随机序列。