ECG Multilead QT Interval Estimation Using Support Vector Machines

Abstract

This work reports a multilead QT interval measurement algorithm for a high-resolution digital electrocardiograph. The software enables off-line ECG processing including QRS detection as well as an accurate multilead QT interval detection algorithm using support vector machines (SVMs). Two fiducial points (Qini and Tend) are estimated using the SVM algorithm on each incoming beat. This enables segmentation of the current beat for obtaining the P, QRS, and T waves. The QT interval is estimated by updating the QT interval on each lead, considering shifting techniques with respect to a valid beat template. The validation of the QT interval measurement algorithm is attained using the Physionet PTB diagnostic ECG database showing a percent error of 2.60±2.25 msec with respect to the database annotations. The usefulness of this software tool is also tested by considering the analysis of the ECG signals for a group of 60 patients acquired using our digital electrocardiograph. In this case, the validation is performed by comparing the estimated QT interval with respect to the estimation obtained using the Cardiosoft software providing a percent error of 2.49±1.99 msec.

Stem Cells for Next Level Toxicity Testing in the 21st Century

“…[301] The QT interval represents the time from the Q wave (first depolarization of the ventricles) to the T wave (total repolarization) and abnormalities in interval lengths are associated with tremendous adverse effects making QT prolongation one of the most common reasons for drug withdrawal. [302,303] The classic hERG assay utilizes patch clamp recording in immortalized human embryonic kidney (HEK) 293 cells heterologously expressing hERG channels. [301] This setup in noncardiac cells exhibits limited predictive power and thus is inferior to novel approaches with hiPSC-derived CM cultures.…”

ECG QT-I nterval Measurement Using Wavelet Transformation

“…Many automatic QT measurement techniques have been developed, including the threshold-based method, the tangent based method, the fitting model method, and the support vector machines method [7]. However, the accuracy of these methods is not satisfactory under the noisy recording conditions caused by electrical interference (AC), movement of chest cavity by respiration, peripheral muscle myogram, and/or atrial arrhythmias such as atrial flutter [8].…”