Abstract:

Simultaneous recording of ECG, Atrial Blood Pressure (ABP) and respiration is possible and sometimes done to investigate cardiovascular and respiration coupling. But analysis most often concentrates on Heart Rate Variability (HRV) and Blood Pressure Variability (BPV). Although analysis of HRV and BPV has lead to important clinical information in the past, an investigation of the morphology of the time course of ECG and ABP could reveal additional diagnostic information.To analyse the morphology of the Blood Bressure (PB) wave a detection of characteristic points, outliers and boundaries is necessary. A wavelet based algorithm for blood pressure segmentation with outlier detection is presented in this paper. It is tested on 108 records with durations of 30 minutes each.

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:

Following the ICH E14 clinical evaluation guideline [1], the measurement of QT/QTc interval prolongation has become the standard surrogate biomarker for cardiac drug safety assessment and the faith of a drug development. In Thorough QT (TQT) study, a so-called positive control is employed to assess the ability of this study to detect the endpoint of interest, i.e. the QT prolongation by about five milliseconds. In other words the lower bound of the one- sided 95% confidence interval (CI) must be above 0 [ms]. Fully automated detection of ECG fiducial points and mea- surement of the corresponding intervals including QT in- tervals and RR intervals vary between different computer- ized algorithms. In this work we demonstrate the ability and reliability of Hannover ECG System (HES) to as- sess drug effects by detecting QT/QTc prolongation effects that meet the threshold of regulatory concern as mentioned by using THEW database studies namely TQT studies one and two.