The Comparison of Algorithms for Life-threatening Cardiac Arrhythmias Recognition

Abstract

During the clinical monitoring of the human heart activity the main goal is to detect heart arrhythmias and capture their precursors as early as possible. And we decided to apply 2 seconds gliding window for lifethreatening cardiac arrhythmias recognition. All types of arrhythmias were grouped into six classes depending on their danger to the human life. And these classes were separated in two parts: threatening humans’ life and others. As a classification features Fourier transform with spectrum up to 15 Hz were picked. In this paper we describe the formed dataset of ECG fragments and compare efficiency of different simple classification algorithms for this two-class problem. The following algorithms were tested: k-nearest neighbours, nearest convex hull algorithm, nearest mean and SVMs with different kernels. The results appeared to be sufficiently appropriate.