Filtering approach based on empirical mode decomposition improves the assessment of short scale complexity in long QT syndrome type 1 population.

Vlasta Bari,Andrea Marchi,Giulia Girardengo,Alfred L George,Paul A Brink,Sergio Cerutti,Lia Crotti,Peter J Schwartz,Alberto Porta

EMBC（2014）

引用 0|浏览6

暂无评分

摘要

This study assesses the complexity of heart period (HP) and QT variability series through sample entropy (SampEn) in long QT syndrome type 1 individuals. In order to improve signal-to-noise ratio SampEn was evaluated over the original series (SampEn0) and over the residual computed by subtracting the first oscillatory mode identified by empirical mode decomposition (SampEn(EMD1R)). HP and QT interval were continuously extracted during daytime (2:00-6:00 PM) from 24 hour Holter recordings in 14 non mutation carriers (NMCs) and 34 mutation carriers (MCs) subdivided in 11 asymptomatic (ASYMP) and 23 symptomatic (SYMP). Both NMCs and MCs belonged to the same family line. While SampEn0 did not show differences among the three groups, Samp(EnEMD1R) assessed over the QT series significantly decreased in ASYMP subjects. SampEn(EMD1R) identified a possible factor (i.e. the lower short scale QT complexity) that might contribute to the different risk profile of the ASYMP group.

查看译文

关键词

continuous qt interval extraction,electrocardiography,time 4 hour,filtering method,medical disorders,oscillatory mode identification,qt variability series,continuous hp interval extraction,symptomatic patients,genetics,nonmutation carriers,short scale complexity assessment,medical signal processing,time 24 hour,risk analysis,computational complexity,short scale qt complexity,feature extraction,long qt syndrome type 1 population,sample entropy,heart period complexity assessment,empirical mode decomposition,filters,asymptomatic patient risk profile,signal classification,transforms,holter recordings,entropy,hp complexity assessment,residual computation,signal-to-noise ratio

AI 理解论文

溯源树

样例

生成溯源树，研究论文发展脉络

Chat Paper

正在生成论文摘要