Bridge time series and complex networks with a frequency-degree mapping algorithm

This paper presents a frequency-degree mapping algorithm to transform time series into complex networks. The results induced from random and periodic time series demonstrate that the method can conserve both amplitude information and temporal information of the original time series. When applied to a practical problem of arrythmia detection, the method is able to distinguish ventricular fibrillation (VF) and monomorphic ventricular tachycardia (MVT) from normal sinus rhythm (NSR) with high accuracy.