ECG Multilead Interval Estimation Using Support Vector Machines.
Journal:
Journal of healthcare engineering
Published Date:
Apr 15, 2019
Abstract
This work reports a multilead interval measurement algorithm for a high-resolution digital electrocardiograph. The software enables off-line ECG processing including detection as well as an accurate multilead interval detection algorithm using support vector machines (SVMs). Two fiducial points ( and ) are estimated using the SVM algorithm on each incoming beat. This enables segmentation of the current beat for obtaining the , , and waves. The interval is estimated by updating the interval on each lead, considering shifting techniques with respect to a valid beat template. The validation of the 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 interval with respect to the estimation obtained using the Cardiosoft software providing a percent error of 2.49 ± 1.99 msec.