New approach for T-wave end detection on electrocardiogram: Performance in noisy conditions

A new approach and algorithm for T-wave end location based on the computation of Trapezium's areas is proposed and validated (in terms of accuracy and repeatability), using signals from the Physionet QT Database. The performance of the proposed algorithm in noisy conditions has been tested and compared with one of the most used approaches for estimating the T-wave end point: the method based on the threshold on the first derivative.The results indicated that the proposed approach based on Trapezium's areas outperformed the baseline method with respect to accuracy and repeatability. Also, the proposed method is more robust to wideband noise.The trapezium-based approach has a good performance in noisy conditions and does not rely on any empirical threshold. It is very adequate for use in scenarios where the levels of broadband noise are significant.The Electrocardiogram (ECG) analysis is the heart diagnostic technique most used in the clinical practice due to its excellent benefit-cost relationship. From the ECG signal, the following features are evaluated: amplitude, morphology and duration of its waves, intervals and segments as well as their appearance sequence.The diagnostic using the ECG signal has numerous approaches. One of them is the beat-to-beat analysis of the time intervals between the Q-wave onset and the T-wave end or interval QT (see Figure 1) during periods of time, typically, from 5 min to 24 hours. Sometimes the interval QT is estimated as the time interval between the peak of the R-wave and the end of the T-wave (RTe). The QT or RT intervals depend on the accuracy with which both points (onset and offset) are determined, especially of the T-wave end (Tend) due to the slow transition in the signal around this point, eventually contaminated by noise and interference on ECG signal.Three variants of studies with QT interval have been done: (a) QT mean duration or QT length (QTL), (b) QT time variability or QT variability (QTV), and (c) spatial variabili