An Algorithm Based on the Continuous Wavelet Transform with Splines for the Automatic Measurement of QT Dispersion: Validation and Application in Chronic Kidney Disease

Abstract

Chronic kidney disease (CKD) is considered a risk factor for the development of car- diovascular disease. QT interval is an electrocardiographic parameter that quantifies the duration of ventricular repolarization. An increase of its spatial variability measured from the selected leads of a standard electrocardiogram (ECG), named QT dispersion (QTd), is considered a risk factor for malign ventricular arrhythmias and sudden death in the CKD. An algorithm for automatic measurement of QTd in the ECG leads DI, aVF and V2 using the continuous wavelet transform with splines is presented. Validation of QRS complex detection has been done on records from MIT-BIH database, and the accuracy is 99.5%. Validation of detection of QRS wave onset and T wave end has been done on records from CSE and QT databases, and the measurements were within the tolerance limits for deviations with respect to the manual measurements defined by the experts. The algorithm was applied in two studies. In the first study, QTd was evaluated in nor mal subjects and patients with CKD. In the second study, QTd was analyzed in patients with CKD before, during and after the hemodialysis treatment. In both studies, the algorithm had a good performance for the QTd analysis. This new algorithm is based on the multilead generalization of a previous algorithm for sin-gle-lead detection of characteristic points of the QRS complex and T wave. It includes the identification of more types of morphologies of these waves, which are common in the analy sis of several ECG leads and heart diseases. To evaluate its performance, ECG recordings of standard annotated databases MIT-BIH, QTDB and CSEDB were used. The results showed that the developed algorithm provides a reliable and accurate QRS detection and delineation of Qi and Te, with standard deviation of the errors within the tolerance limits for variations with respect to the measurements made by different experts. The QTd algorithm was applied in two studies. In the first one, QTd was evaluated as a discrimi nator of patients with CKD from normal subjects. The results showed that QTd was significantly larger in CKD patients than in normal subjects, which agrees with similar studies. In the second study, QTd was analyzed in four patients with CKD before, during and after the HD treatment. The results showed that all the patients have an increase of QTd during HD and post-HD, which has been associated with malign ventricular arrhythmias and sudden death in previous studies. Future applications of this algorithm will focus on to evaluate dispersion in other ECG ventricular activity intervals like JT (from S wave end to T wave end) and Tpe (from T wave peak to T wave end), in order to determine whether they improve the identification of CKD patients with risk of malign ventricular arrhythmias compared with QT dispersion.