Med Biol Eng Comput - Lagged segmented Poincar? plot analysis for risk stratification in patients with dilated cardiomyopathy.

Tópicos

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Resumo

The objectives of this study were to introduce a new type of heart-rate variability analysis improving risk stratification in patients with idiopathic dilated cardiomyopathy (DCM) and to provide additional information about impaired heart beat generation in these patients. Beat-to-beat intervals (BBI) of 30-min ECGs recorded from 91 DCM patients and 21 healthy subjects were analyzed applying the lagged segmented Poincar? plot analysis (LSPPA) method. LSPPA includes the Poincar? plot reconstruction with lags of 1-100, rotating the cloud of points, its normalized segmentation adapted to their standard deviations, and finally, a frequency-dependent clustering. The lags were combined into eight different clusters representing specific frequency bands within 0.012-1.153 Hz. Statistical differences between low- and high-risk DCM could be found within the clusters II-VIII (e.g., cluster IV: 0.033-0.038 Hz; p = 0.0002; sensitivity = 85.7 %; specificity = 71.4 %). The multivariate statistics led to a sensitivity of 92.9 %, specificity of 85.7 % and an area under the curve of 92.1 % discriminating these patient groups. We introduced the LSPPA method to investigate time correlations in BBI time series. We found that LSPPA contributes considerably to risk stratification in DCM and yields the highest discriminant power in the low and very low-frequency bands.

Resumo Limpo

object studi introduc new type heartrat variabl analysi improv risk stratif patient idiopath dilat cardiomyopathi dcm provid addit inform impair heart beat generat patient beattobeat interv bbi min ecg record dcm patient healthi subject analyz appli lag segment poincar plot analysi lsppa method lsppa includ poincar plot reconstruct lag rotat cloud point normal segment adapt standard deviat final frequencydepend cluster lag combin eight differ cluster repres specif frequenc band within hz statist differ low highrisk dcm found within cluster iiviii eg cluster iv hz p sensit specif multivari statist led sensit specif area curv discrimin patient group introduc lsppa method investig time correl bbi time seri found lsppa contribut consider risk stratif dcm yield highest discrimin power low lowfrequ band

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