Abstract
This paper presents a frequency-degree mapping algorithm which enables network analysis of human electrocardiogram (ECG) time series. Two important topological quantities, the average degree (AD) and the average shortest path length (APL) have been investigated in the associated networks of ECG time series. The results demonstrate that the quantity of AD can serve as an effective and reliable indicator in distinguishing malignant ventricular arrhythmias from normal sinus rhythm and other benignant arrhythmias. Meanwhile, the quantity of APL is shown to be capable of characterizing the heart rate, which may be helpful in detecting shockable rhythms.
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Yang, D., Li, X. (2013). Reliable Detection of Malignant Ventricular Arrhythmias Based on Complex Network Theory. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_25
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DOI: https://doi.org/10.1007/978-3-642-36669-7_25
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