J Med Syst - The quantification of the QT-RR interaction in ECG signal using the detrended fluctuationanalysis and ARARX modelling.


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In this paper, the detrended fluctuation analysis DFA is used to investigate and quantify the QT-RR interaction in different pathologic cases in order to distinguish between them. The study is carried out on the ECG signals of MIT-BIH universal database. Different ECG signals related to cardiac pathological cases are concerned with this study. These are: Premature Ventricular Contraction (PVC) (9 cases), Right Bundle Branch Block (RBBB) (4 cases), Left Bundle Branch Block (LBBB) (2 cases), Atrial Premature Beat (APB) (4 cases), Paced Beat (PB) (4 cases), and other pathologic cases with different severity (10 cases). All this cases are compared to the 15 normal cases. The obtained results show that the DFA can identify the healthy subject from the pathologic cases according to the values of the scaling exponent a. The results indicate that a varies between 0.5 and 1 in all cases which means that there is a long range correlation in RR and QT series. The QT and RR series are also modelled using the ARARX model. The parameters of the model are then extracted. The power spectral density (PSD) is estimated by using these parameters in order to provide further information about the causal interactions within the signals and also to determine the power scaling exponent ?. This scaling exponent confirms the relationship between RR and QT intervals in all the studied cases except in APB and PB cases where the behaviour is similar to that of the white noise. The QT variability degrees are calculated and the DFA is applied on it. The obtained results show a long range correlation between RR and QT intervals in all cases and an ambiguity in the APB case. The DFA is compared to the Poincar? method in order to evaluate the algorithm performance using the Fuzzy Sugeno classifier is used for this purpose.

Resumo Limpo

paper detrend fluctuat analysi dfa use investig quantifi qtrr interact differ patholog case order distinguish studi carri ecg signal mitbih univers databas differ ecg signal relat cardiac patholog case concern studi prematur ventricular contract pvc case right bundl branch block rbbb case left bundl branch block lbbb case atrial prematur beat apb case pace beat pb case patholog case differ sever case case compar normal case obtain result show dfa can identifi healthi subject patholog case accord valu scale expon result indic vari case mean long rang correl rr qt seri qt rr seri also model use ararx model paramet model extract power spectral densiti psd estim use paramet order provid inform causal interact within signal also determin power scale expon scale expon confirm relationship rr qt interv studi case except apb pb case behaviour similar white nois qt variabl degre calcul dfa appli obtain result show long rang correl rr qt interv case ambigu apb case dfa compar poincar method order evalu algorithm perform use fuzzi sugeno classifi use purpos

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