Abstract:

Ectopic beats are a common cause for cardiac arrhythmia. The methods presented in this paper deal with the evaluation of the features that are used by an existing classifier to distinguish between normal, supraventricular ectopic and ventricular ectopic beats. In order to classify the beats, a support vector machine (SVM) is used. Since noisy features can confuse the classifier and downgrade its performance, high quality features should be chosen. In the end, the performance should be improved by using only the selected features after the evaluation process. For this purpose, a receiver operating character- istic (ROC) analysis was conducted first. Secondly, the Gini diversity index (GDI) was calculated for every feature which is often used as split criterion in decision trees. As a third evaluation tool, the information gain ratio (IGR) was applied to estimate the quality of the features. To conclude the evaluation part, a fourth analysis was implemented. The ROC was applied again to the beats that are falsely classified in a first run-through. This was a first step into a deeper investigation of the dependency among features. As result of the evaluation process, a feature ranking was built and 36 of the 55 features were chosen to build the new SVM. A training and testing process was conducted using beats of the MIT-BIH-Arrhythmia- Database. A correct rate of 98.574%, a sensitivity of 98.592% and a positive predictive value of 99.062% were achieved.

Abstract:

The heart rate variability (HRV) is a measure which is commonly used to assess sympathetic and parasympathetic auto-nomic function. It is well known, that respiration can have a strong influence on HRV. Especially, a phenomenon called Respiratory Sinus Arrythmia (RSA) modulates the RR intervals and is a major contributor to the HRV. The interpreta-tion of common HRV parameters can be ambiguous due to different respiration rates and patterns. To assess this ambi-guity, the coupling of RSA on HRV was quantified and the HRV parameters were compared during different respirato-ry states.A pilot study with five healthy subjects was performed. A three lead ECG was acquired and the respiration was estimat-ed by measuring the aeration of the lungs using the PulmoVista 500 by Dräger. This device uses Electrical Impedance Tomography (EIT) to monitor impedance changes due to the changing amount of air within the lungs during respira-tion. The subjects were asked to breath at controlled respiration rates of 8, 15 and 24 breaths per minute as well as spon-taneously for 1 min each. In addition, to analyze HRV during apnoic phases without any respiration, the subjects were asked to hold their breath for 40s at end-inspiration and end-expiration. After preprocessing of the ECG and the respiration signal, the coupling between the measured respiration and the RR intervals was quantified using the Granger causality. If significant coupling was present, the HRV was separated from its respiratory influence using an ARMAX model. The measured respiration hereby formed the exogeneous input to the filter. Finally, common HRV parameters were calculated for the original and the decoupled RR intervals.We showed, that coupling strength depends on respiratory rates, which might complicate HRV interpretation. Moreo-ver, the coupling is decreased during spontaneous breathing in comparison to controlled respiration. Additionally we found, that HRV parameters during apnoic phases differ from decoupled HRV parameters during spontaneous or con-trolled respiration.

Abstract:

The post extrasystolic T wave change (PEST) is an electrocardiographic phenomenon in which the morphology of the normal T wave is altered for a short time after a ventricular ectopic beat (VEB). It has been observed in patients with other cardiac pathologies but it has not been proposed as a risk index for cardiac death. Since PEST seems to be potentiated in patients with depression of myocardial contractility, we hypothesize that PEST could be used to predict pump failure death (PFD) in patients with chronic heart failure (CHF). For the purpose of quantifying PEST, the parameters morphological change onset (MCO) and morphological change slope (MCS) were introduced. The MUSIC study was used to test the hypothesis. The patients in the study were separated according to its cause of death and comparisons of each cause against the others (including survivors) were carried out. In addition, the parameters MCO and MCS were divided into subgroups us- ing optimal values obtained from the corresponding ROC curves with the aim of analyzing predictability with respect to PFD. The results showed that no significant differences could be established and the proposed parameters do not seem to be related to any kind of cardiac death.