A parametric model approach for quantification of short term QT variability uncorrelated with heart rate variability

In this work we propose to assess the relation between HRV and QTV measured by an automatic delineator. A low order linear autoregressive model on RR versus QT interactions was used to explore short term relations and quantify the fractions of QTV correlated and not correlated with HRV. Power spectral density measures were estimated from the total QTV and from the two separated fractions using the proposed model. Simulated RR and QT series were used to quantify the error bounds associated to the method performance. ECG records of young normal subjects were processed to obtain the RR and QT series. The high QTV fraction not correlated with RR found h these records (over 40% in 98% of the segments) indicates that an important part of QTV can be driven by other factors rather than by heart rate, and may contain complementary information.