Biomedical subjects

G von Wagner

Publications and source records attributed to G von Wagner.

4 recordsLinked to original sources

PubMed2002

Simulation methods for the online extraction of ECG parameters under Matlab/Simulink.

The classification of cardiac pathologies in the human ECG greatly depends on the reliable extraction of characteristic features. This work presents a complete simulation environment for testing ECG classification algorithms under Matlab/Simulink. Evaluation of algorithm performance is undertaken in full compliance with the ANSI/AAMI standards EC38 and EC57, and ranges from beat-to-beat analysis to the comparison of episode markers (e.g., for VT/VF detection algorithms). For testing the quality of waveform boundary detection, our own testing methods have been implemented in compliance with existing literature.

Algorithms↗

PubMed2002

[Hardware implementation in VT/VF detection algorithms for AED (automatic external defibrillators)].

The automatic external defibrillator (AED) should carry out the diagnosis of the patient in a cardiac emergency when medical professionals are not present. The decision about defibrillation should be made without the help of first responder. To fulfill these requirements special algorithms are needed. In this article the structure of such an algorithm and also hardware implementation problems are described.

Algorithms↗

PubMed2002

Mobile patient simulator for resuscitation training with automatic external defibrillators.

Resuscitation training has to be performed under most realistic conditions. This includes both usual CPR measures (breathing and chest compressions) and advanced measures, e.g. the usage of an Automatic External Defibrillator (AED). Almost all currently available simulators for ECG signals used in such trainings have a rather limited variety of available ECG signals. The trainer also has to change between different rhythms manually, resulting in a less realistic training environment. The development will result in mobile ECG simulator which can automatically react to events in the resuscitation process according to pre-programmed scenarios. It also has potential to simulate other physiological parameters like thorax impedance in the future.

Cardiopulmonary Resuscitation↗

PubMed2002

Parameter extraction of ECG signals in real-time.

In order for a mobile ECG recorder to be able to classify a heart rhythm online, the significant parameters must be extracted. The relevant parameters are the beginning, peak and end of the QRS-complex, the P- and T-waves, the ST-segment and other significant intervals, such as the RR-interval. The aim of the development was, firstly, stable, real-time-capable QRS detection, which finally achieved values for sensitivity of 98.9% and a positive predictivity of 99.9% on standard ECG databases. Also, a filter-based detection of P- and T-waves was implemented, which can also be performed in real-time on a microcontroller platform.

Artifacts↗