Shock outcome prediction before and after CPR: a comparative study of manual and automated active compression-decompression CPR.

We report on a study designed to compare the relative efficacy of manual CPR (M-CPR) and automated mechanical CPR (ACD-CPR) provided by an active compression-decompression (ACD) device. The ECG signals of out-of-hospital cardiac arrest patients of cardiac aetiology were analysed just prior to, and immediately after, cardiopulmonary resuscitation (CPR) to assess the likelihood of successful defibrillation at these time points. The cardioversion outcome prediction (COP) measure previously developed by our group was used to quantify the probability of return of spontaneous circulation (ROSC) after counter-shock and was used as a measure of the efficacy of CPR. An initial validation study using COP to predict shock outcome from the patient data set resulted in a performance of 60% specificity achieved at 100% sensitivity on a blind test of the data. This is comparable with previous studies and provided confidence in the robustness of the technique across hardware platforms. Significantly, the COP marker also displayed an ability to stratify according to outcomes: asystole, ventricular fibrillation (VF), pulseless electrical activity (PEA), normal sinus rhythm (NSR). We then used the validated COP marker to analyse the ECG data record just prior to and immediately after the chest compression segments. This was initially performed for 87 CPR segments where VF was both the pre- and post-CPR waveform. An increase in the mean COP values was found for both CPR types. A signed rank sum test found the increase due to manual CPR not to be significant (p>0.05) whereas the automated CPR was found to be significant (p<0.05). This increase was larger for the automated CPR (1.26, p=0.024) than for the manual CPR (0.99, p=0.124). These results indicate that the application of CPR does indeed provide beneficial preparation of the heart prior to defibrillation therapy whether manual or automated CPR is applied. The COP marker shows promise as a definitive, quantitative determinant of the immediate positive effect of both types of CPR regardless of the details of use. In work of a more exploratory nature we then used the validated COP marker to analyse the ECG pre- and post-CPR for all rhythm types (212 traces). We show a significant increase in the COP measure (p<0.001 in both cases) as indicated by a shift in the median COP marker distribution values. This increase was more pronounced for automated ACD-CPR than for manual CPR. However, a detailed statistical analysis carried out between the groups adjusted for pre-CPR value showed no significant difference between the two methods of CPR (p=0.20). Similarly, adjusting for length of CPR showed no significant difference between the groups. Secondary, subgroup analysis of the ECG according to the length of time for which CPR was performed showed that both types of CPR led to an increase in the likelihood of successful defibrillation after increasing durations of CPR, however results were less reliable after longer periods of continuous CPR.

Wavelet transform analysis predicts outcome of DC cardioversion for atrial fibrillation patients.

The aim of this study was to examine whether wavelet transform analysis of the electrocardiogram (ECG) can improve the prediction of the maintenance of sinus rhythm in patients with atrial fibrillation (AF) after external DC cardioversion. We examined a variety of wavelet transform-based statistical markers as potential candidates for the prediction of patient status post-cardioversion. Considering a 'success' as a patient who remains in normal sinus rhythm for one month post cardioversion and 'failure' as a patient who does not, it was shown the proposed non-parametric classification system can achieve 89% specificity at 100% sensitivity using a non-parametric classification method.

Practical issues in the evaluation of methods for the prediction of shock outcome success in out-of-hospital cardiac arrest patients.

There is a need for robust, effective predictors of the outcome from shock for out-of-hospital cardiac arrest patients. Such technology would enable the emergency responder to provide a therapy tailored to the patient's needs. Here we report our most recent findings while dwelling intentionally on the rationale behind the decisions taken during system development. Specifically, we illustrate the need for sensible data selection, fully cross-validated results and the care necessary when evaluating system performance. We analyze 878 pre-shock ECG traces, all of at least 10 s duration from 110 patients with cardiac arrest of cardiac aetiology. The continuous wavelet transform was applied to preshock segments of ECG trace. Time-frequency markers are extracted from the transform and a linear threshold derived from a training set to provide high sensitivity prediction of successful defibrillation. These systems are then evaluated on a withheld test set. All experiments are cross-validated. When compared to popular Fourier-based techniques our wavelet transform method, COP (Cardioversion Outcome Predictor), provides a 10-20% improvement in performance with values of 66 +/- 4 specificity at 95 +/- 4 sensitivity, 61 +/- 4 specificity at 97 +/- 2 sensitivity and 56 +/- 1 specificity at 98 +/- 2 sensitivity achieved for datasets limited to 3, 6, and 9 shocks per patient, respectively. Thus, the assessment of the wavelet marker was associated with a high specificity value at or above 95% sensitivity in comparison to previously reported methods. Therefore, COP could provide an optimal index for the identification of patients for whom shocking would be futile, and for whom an alternative therapy could be considered.

Wavelet analysis of pulse oximeter waveform permits identification of unwell children.

BACKGROUND: Children who are unwell often display signs of circulatory compromise. It has been observed that pronounced changes occur in the appearance of the photoplethysmogram (pulse oximeter tracing) in these children. The aim of the study was to discover if wavelet transforms can identify more subtle changes in the photoplethysmogram of children who are unwell. METHODS: Photoplethysmograms were obtained from children attending a paediatric accident and emergency department with clinical features suggestive of significant bacterial illness or circulatory compromise. Photoplethysmograms were also obtained from a control group of well children. Wavelet transforms were applied to the traces in an attempt to separate the two groups. RESULTS: 20 traces were obtained from unwell children and 12 from controls. Analysis of the entropy of the wavelet transform of the photoplethysmogram allows the differentiation of unwell children from controls (p = 0.00002). CONCLUSIONS: Wavelet transform of the photoplethysmogram offers the possibility of a rapid non-invasive method of screening children for significant illness.

Standard pulse oximeters can be used to monitor respiratory rate.

BACKGROUND: One of the most important limitations of standard pulse oximeters is the inability to detect changes in respiratory rate until oxygenation is affected. This study sought to determine if analysis of the plethysmogram by wavelet transforms would enable the determination of changes in respiratory rate at an earlier stage. METHODS: Ten healthy adult volunteers were monitored, breathing at baseline and predetermined respiratory rates, using a standard pulse oximeter. Photo-plethysmograms captured in an attached lap top computer were then analysed using wavelet transforms. RESULTS: Determination of baseline respiratory rate and subsequent changes including apnoea were easily identified. COMMENT: Wavelet transforms permit the accurate determination of respiratory rate by a standard pulse oximeter.

A novel wavelet transform based analysis reveals hidden structure in ventricular fibrillation.

We report a new method of interrogating the surface ECG signal using techniques developed in the field of wavelet transform analysis. Previously unreported structure within the ECG during ventricular fibrillation (VF) is found using a high-resolution decomposition of the signal employing the continuous wavelet transform. We believe that wavelet transform methods could lead to the development of powerful tools for use in the resuscitation of patients with cardiac arrest.