Abstract:

There is a large interest in analysing the QT-interval, as a prolonged QT-interval can cause the development of ventricular tachyarrhythmias such as Torsade de Pointes. One major part of QT-analysis is T-end detection. Three automatic T-end delineation methods based on wavelet fil- terbanks (WAM), correlation (CORM) and Principal Com- ponent Analysis PCA (PCAM) have been developed and applied to Physionet QT database. All algorithms tested on Physionet QT database showed good results, while PCAM produced better results than WAM and CORM achieved best results. Standard de- viation in sampling points (fs=250Hz) have been 33.3 (WAM), 8.0 (PTDM) and 7.8 (CORM). It could be shown that WAM is prone to interference while CORM is the most stable method even under bad conditions. Further- more it was possible to detect significant QT-prolongation caused by Moxifloxacin in Thorough QT Study # 2 us- ing CORM. QT-prolongation is significantly correlated to blood plasma concentration of Moxifloxacin.

Abstract:

Prolongation of the ECG QT-interval is a risk factor as it can cause the development of ventricular tachyarrhythmias such as Torsade de Points and ventricular fibrillation often leading to sudden cardiac death. Thus there is a large interest in analysing the QT-interval in the ECG. One major part of ECG QT-analysis is T-end detection. A method for automatic T-end detection is presented and validated by the Physionet QT-database. The delineation algorithm presented here is based on a correlation method. Results have been compared to hand marked T-waves in the Physionet QT-database. The algorithm produced significantly better results than using the standard wavelet method.

Abstract:

The detection of the QT interval plays an important role in current biomedical engineering research projects as it is a crucial parameter for rating humans‟ health status and the effect of drugs.In this study a new approach for a fully-automated QRS and T wave delineation solution is presented. The algorithm both works for single-lead and for multi-lead data. It bases on techniques such as wavelet transform, principle component analysis and pattern-matching methods. It has been implemented in a Matlab GUI and it works stand-alone without any additional knowledge or user interaction. It features the detection of all relevant ECG fiducial points like Rpeak, Tend, QRS boundaries, Qpeak and Speak. The performance of the Tend detection and the Rpeak detection has been evaluated successfully with reference data from Physionet‟s QT database and Thew database respectively. Finally, the algorithm has been applied for a complete QT study with plausible and promising results.