A Novel Method for Mining Semantics from Patterns over ECG Data

Zhen Qiu, Feifei Li, Shenda Hong, Hongyan Li

Abstract: In intensive care units (ICU), electrocardiogram (ECG) waveforms show diverse variationsunder different patients' physical conditions.In general, physicians can diagnose patients efficientlyby detecting any disorder of heart rate or rhythm and any change in the morphological pattern of ECG data,which contain underlying semantics.To help physicians better analyze ECG data in a fairly short time,it is essential to develop a novel method for mining semantics from ECG patterns.This paper is the very first time to characterize ECG patterns by using Prefix Scalable Pattern Tree (PSP-Tree).Comparing with similar currently existing methods, PSP-Tree can mine significant semantics,such as scalability, temporality and hierarchy over ECG patterns.We conduct extensive experiments on real ECG data set which are obtained from PhysioBank Community and Beijing No.3 People Hospital.The experiment results show that our method performs more feasibly and effectively than other related work.