Improved QT ınterval estimation using conditional generative adversarial networks.

QT interval carries essential information about ventricular depolarization and repolarization. Therefore, in many investigations, it is vital to determine the QT interval accurately and monitor the corresponding QT interval variations. This paper presents a novel implementation of the Generative Adversarial Networks (GAN) to predict the QT waveform interval from the electrocardiogram. The network then estimates the Q and T locations to determine the QT interval. The accuracy of the proposed method in measuring the QT interval was 94.10%. The procedure is evaluated against ground truth performed by expert physicians, and the limits of agreement are used as the measure of performance. The bias in the estimate of the Q locations was 2.92 ms, with 11.48 ms as an upper limit of agreement and − 5.65 ms as the lower limit. The bias in estimating the T location was 1.14 ms with an upper limit of 15.04 ms and a lower limit of − 12.79 ms. Moreover, the bias in the estimate of the QT interval was − 7.10 ms with an upper limit of 50.17 ms and a lower limit of − 64.38 ms. These limits of agreements are clinically acceptable and show that the estimate of the QT interval agrees with the expert's annotations. [ABSTRACT FROM AUTHOR]