Abstract:

Ventricular ectopic beats (VEBs) trigger a characteristic response of the heart called heart rate turbulence (HRT). The HRT can be used to predict sudden cardiac death in patients with a history of myocardial infarction. In this work, we present a reliable algorithm to detect and classify ectopic beats. Every electrocardiogram (ECG) is processed with innovative filtering techniques, artifact detection methods, and a robust multichannel analysis to produce accurate annotation results. For the classification task, a support vec- tor machine was used. Furthermore, a new approach to the analysis of HRT is proposed. The HRT is interpreted as the response of a second-order system to an external perturbation. The system theoretical parameters were estimated. The influence of VEB on the morphology of subsequent T waves was also analyzed. A strong influence was detected in the study with 14 patients experiencing frequent VEB. The evolution of the morphology of the T wave with every new beat was studied, and it could be concluded that an exponential shape underlies this dynamic process and was called morphological heart rate turbulence (MHRT). Parameters were defined to quantify the MHRT. The analysis of the MHRT could help to understand the influence of an ectopic beat on the repolarization processes of the heart and more accurately stratify the risk of sudden cardiac death.

Abstract:

In guideline E14, the American Food and Drug Administration (FDA) requests for clinical studies to investigate the prolongation of the heart rate corrected QT-interval (QTc) of the ECG. As drug induced QT-prolongation can be caused by changes in the repolarisation of the ventricles, it is so far a thorough ECG biomarker of risk for ventricular tachyarrhythmias and Torsade de Pointes (TdP). Ventricular repolarisation changes not only change QT but also influence the morphology of the T-wave. In a (400 mg single dose) Moxifloxacin positive control study both, QTc and several descriptors describing the T-wave morphology have been measured. Moxifloxacin is changing two shape dependent descriptors significantly (P<0.05) about 3 to 4 hours after a 400 mg oral single dose of Moxifloxacin.

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:

QT interval correction on measured ECGs is an important issue for pharmaceutical research on the way to new drugs. Pharmaceutical industries have to thoroughly investigate potential effects of their drugs on QT intervals since QT pro- longation is considered as a marker of the proarrhythmia risk. As QT interval depends on RR interval there is an obvious in- terest in modeling the QT-RR relationship. Static formulas to correct QT for RR are well known, but a dynamic dependency is also observed. Two models of dynamic QT-RR relationships are introduced to eliminate the heart rate dependent part out of the QT interval. These models are based on heart cell measure- ments and simulations and are validated by Holter ECG data.

Abstract:

The modelling of the relationship between QT and RR intervals is an important issue for pharmaceuticalresearch on the way to new drugs. Pharmaceutical industries have to thoroughly investigate potential effects of their medicines on QT intervals since QT prolongation is considered as a marker of the proarrhythmia risk. As QT intervals depend on RR intervals there is an obvious interest in modelling the QT-RR relationship. Static formulas to correct QT for RR are well known, but a dynamic dependencyis mainly observed.Two models of dynamic QT-RR relationships are introduced to eliminate the heart rate dependent part out of the QT interval. These models are based on heart cell measurements and simulations and are validated by Holter ECG data.

Abstract:

The analysis of the heart rate turbulence (HRT) can be used to evaluate the risk of sudden cardiac death. For that purpose ectopic beats have to be classified. A method for automatic ectopic beat detection and classification based on a Support-Vector-Machine (SVM) was developed before. In this work an analysis similar to the HRT was carried out on the morphological features of the QRS complex and the T wave of the ECG following a ventricular premature contractions (VPC). A study of 56 subjects, suffering from a various number of ventricular premature contractions was conducted.

Abstract:

Die Analyse der Herz Raten Turbulenz (HRT) dient der Vorhersage eines ploetzlichen Herztodes. Hierzu muessen die ektopen Schlaege im EKG ausgewertet werden. Zur automatischen Detektion und Klassifikation ektoper Schlaege wurde 2010 eine Methode entwickelt, welche auf Basis einer Support-Vector-Machine (SVM) die Schlaege klassifiziert. Artefakte im EKG-Signal fuehren nicht selten zur Fehlklassifizierung, da sie nicht vollstaendig von ektopen Schlaegen unterschieden werden koennen. Um die Genauigkeit die HRT Analyse zu verbessern, wurde ein Algorithmus zur automatischen Unterscheidung von Artefakten und ektopen Schlaegen entwickelt.

Abstract:

Ectopic beats are a common cause of cardiac arrhythmia. In order to automatically detect and classify ectopic beats in the ECG a new method based on a Support Vector Machine (SVM) was developed. The numerical patterns needed for this classification task were obtained from rhythmical and morphological characteristics of the QRS complexes. The SVM was trained and tested using the MIT-BIH Arrythmia Database and the MIT-BIH Supraventricular Arrythmia Database. A sensitivity of 92.46% was achieved.

Abstract:

Biosignal analysis is aiming at analyzing physiological parameters for improved diagnosis and treatment. In this paper, the use of multivariate autoregressive models is proposed as a new method to analyze ECG data and to gain further information about the functionality of the heart. This application is demonstrated on myocarditis patients, where cure and diagnosis was observed. Timeseries of RR and QT intervals are analyzed by an autoregressive (AR) model, whose parameters are found dependent on the condition of the inflammation. The heart muscle inflammation is known as potentially lethal and an invasive biopsy still is the gold standard. Due to the variety of its symptoms, detailled non-invasive diagnosis is rather difficult and thus a highly challenging topic.

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.

Abstract:

The most frequently occurring cardiac arrhythmia in the adulthood is atrial fibrillation. In Germany, the number of sick people is estimated at 800,000. Patients who suffer from atrial fibrillation often do not sense any symptoms of the illness. Through the uncontrolled blood flow in the atrium, a blood clot, which can lead to the blood circulation for an embolism or for a stroke, can form itself. These persons must be recognized, because of their increased stroke risk in order to be able to attend it. In this work, the chest strap CorBelt, developed by the company Corscience GmbH&Co.KG, is equipped with an algorithm for the recognition of atrial fibrillation with the aid of heart rate variability.