Biomedical subjects

K Rheinberger

Publications and source records attributed to K Rheinberger.

3 recordsLinked to original sources

PubMed2003

[Analysis of ventricular fibrillation signals for the evaluation of defibrillation success in the treatment of ventricular fibrillation].

OBJECTIVE: Precise detection of ventricular fibrillation (VF), reliable prediction of defibrillation success and adjustment of the discharge waveform to the patient's transthoracic impedance may contribute to a reduction of electricity-associated myocardial injury caused by unnecessary counter shocks. Specifically, asystole thresholds distinguish between VF and asystole, and thus prevent unnecessary defibrillation attempts. We reviewed various studies and manufacturer characteristics regarding the parameters and algorithms for analyzing arrhythmia ECG signals. METHODS: Asystole threshold values of several defibrillator manufacturers were collected and a literature review was performed including the following parameters: amplitude, frequency, bispectral analysis, amplitude spectrum area, wavelets, nonlinear dynamics, N(alpha)histograms, and combinations of various parameters. RESULTS: The manufacturer dependent asystole thresholds vary substantially. We show ways to optimize an ECG-based analysis for the next technological generation of defibrillators. During advanced cardiac life support (ACLS) the probability of defibrillation success should be estimated. Optimal defibrillation waveform, depending on transthoracic resistance, should be individually determined. In case of prolonged VF with a low ECG amplitude defibrillation should not be attempted unless coronary perfusion has been improved by further measures of ACLS. The combined evaluation of VF amplitude and frequency is effective in predicting defibrillation success. Estimation of further parameters is potentially useful for guiding optimal timing of defibrillation. At present, the implementation of most parameters in out-of-hospital cardiopulmonary resuscitation (CPR) is limited by the lack of technical feasibility of online computing. CONCLUSION: Analysis of VF ECG signals should allow adequate VF detection as well as prediction of defibrillation success. Suitable asystole thresholds for analysis of ECG signals have to be determined, and the adverse effects of CPR associated artefacts on data analysis have to be reduced. Analysis of VF ECG signals is a precondition of individually optimized defibrillation and may contribute substantially to an increased quality of CPR.

Algorithms↗

PubMed2001

Waveform analysis of biphasic external defibrillators.

BACKGROUND AND OBJECTIVE: All internal defibrillators and some external defibrillators use biphasic waveforms. The study analysed the discharged waveform pulses of two manual and two semi-automated biphasic external defibrillators. METHODS AND RESULTS: The defibrillators were discharged into resistive loads of 25, 50 and 100 Omega simulating the patient's transthoracic impedance. The tested biphasic defibrillators differed in initial current as well as initial voltage, varying from 10.9 to 73.3 A and from 482.8 to 2140.0 V, respectively. The energies of the manual defibrillators set at 100, 150 and 200 J deviated by up to +19.1 or -28.9% from the selected energy. Impedance-normalised delivered energy varied from 1.0 to 12.5 J/Omega. Delivered energy, shock duration and charge flow were examined with respect to the total pulse, its splitting into positive and negative phases and their impedance dependence. For three defibrillators pulse duration increased with the resistive load, whereas one defibrillator always required 9.9 ms. All tested defibrillators showed a higher charge flow in the positive phase. Defibrillator capacitance varied between approximately 200 and 100 mu F and internal resistance varied from 2.0 to 7.6 Omega. Defibrillator waveform tilt ranged from -13.1 to 61.4%. CONCLUSIONS: The tested defibrillators showed remarkable differences in their waveform design and their varying dependence on transthoracic impedance.

Calibration↗

PubMed2001

Algorithms to analyze ventricular fibrillation signals.

Prediction of the success of defibrillation to avoid myocardial injury and performance feedback during CPR requires algorithms to analyze ventricular fibrillation signals. This report reviews investigations on different parameters of ventricular fibrillation electrocardiographic signals, including amplitude, frequency, bispectral analysis, amplitude spectrum area, wavelets, nonlinear dynamics, N(alpha) histograms, and combinations of several of these parameters. To date, no satisfactory methods have been found that cope with CPR artifacts and show adequate predictive power of successful defibrillation. The usual limitations of the studies are the small number of subjects, which precludes separation into training and test data. Because many investigations are animal studies of untreated short ventricular fibrillation, the results may be different for prolonged ventricular fibrillation in humans. The universality of threshold values has to be examined, and promising new parameters have to be monitored over longer time periods and analyzed for the effects of chest compressions, ventilation, and concomitant vasopressor therapy.

Algorithms↗