S. Ruiz de Gauna

A Review of Flicker Severity Assessment by the IEC Flickermeter

This paper presents an analysis of the flicker assessment by block 5 of the International Electrotechnical Commission (IEC) flickermeter. We compare the values provided by the IEC flickermeter from field measurements and the complaints from the customers. We also analyze the behavior of the IEC flickermeter when subject to signals containing nonuniform rectangular fluctuations. Finally, as it is not easy to find a consistent relationship between the true annoyance and the flicker severity, we describe some laboratory subjective tests with a small group of people, carried out to assign a true quantitative value of annoyance to a nonuniform light fluctuation from real situations. Block 5 of the IEC flickermeter derives its estimate of the annoyance accurately only for uniform fluctuations. In realistic conditions, when the voltage fluctuations are not uniform but have varying frequencies and amplitudes, the IEC flickermeter does not assess the true flicker annoyance precisely.

Distinction of ventricular fibrillation and ventricular tachycardia using cross correlation

The accurate discrimination of ventricular tachycardia (VT) from ventricular fibrillation (VF) is an important issue in automated external defibrillator (AED) and other cardiac monitoring systems. The correlation function of the ECG signal is an adequate tool for detecting irregularities or noise in signals. Therefore, methods for VF/VT discrimination based on the autocorrelation of the ECG signal and on the cross correlation of the ECG signal with different templates have been proposed. Our work is focused on the use of the cross correlation of the ECG signal with a segment of the same ECG signal, instead of using a given template. The developed algorithm has been tested on a database consisting of 179 human VF and 74 human VT records. The new algorithm classifies accurately 313 VF windows (90.2% sensitivity) and 179 VT windows (96.75% sensitivity). These results improve those obtained from other techniques, considered as a reference, for the same records database.

Feasibility of automated rhythm assessment in chest compression pauses during cardiopulmonary resuscitation

AIM To demonstrate the feasibility of doing a reliable rhythm analysis in the chest compression pauses (e.g. pauses for two ventilations) during cardiopulmonary resuscitation (CPR). METHODS We extracted 110 shockable and 466 nonshockable segments from 235 out-of-hospital cardiac arrest episodes. Pauses in chest compressions were already annotated in the episodes. We classified pauses as ventilation or non-ventilation pause using the transthoracic impedance. A high-temporal resolution shock advice algorithm (SAA) that gives a shock/no-shock decision in 3s was launched once for every pause longer than 3s. The sensitivity and specificity of the SAA for the analyses during the pauses were computed. RESULTS We identified 4476 pauses, 3263 were ventilation pauses and 2183 had two ventilations. The median of the mean duration per segment of all pauses and of pauses with two ventilations were 6.1s (4.9-7.5s) and 5.1s (4.2-6.4s), respectively. A total of 91.8% of the pauses and 95.3% of the pauses with two ventilations were long enough to launch the SAA. The overall sensitivity and specificity were 95.8% (90% low one-sided CI, 94.3%) and 96.8% (CI, 96.2%), respectively. There were no significant differences between the sensitivities (P=0.84) and the specificities (P=0.18) for the ventilation and the non-ventilation pauses. CONCLUSION Chest compression pauses are frequent and of sufficient duration to launch a high-temporal resolution SAA. During these pauses rhythm analysis was reliable. Pre-shock pauses could be minimised by analysing the rhythm during ventilation pauses when CPR is delivered at 30:2 compression:ventilation ratio.

Fully automatic rhythm analysis during chest compression pauses

AIM Chest compression artefacts impede a reliable rhythm analysis during cardiopulmonary resuscitation (CPR). These artefacts are not present during ventilations in 30:2 CPR. The aim of this study is to prove that a fully automatic method for rhythm analysis during ventilation pauses in 30:2 CPR is reliable an accurate. METHODS For this study 1414min of 30:2 CPR from 135 out-of-hospital cardiac arrest cases were analysed. The data contained 1942 pauses in compressions longer than 3.5s. An automatic pause detector identified the pauses using the transthoracic impedance, and a shock advice algorithm (SAA) diagnosed the rhythm during the detected pauses. The SAA analysed 3-s of the ECG during each pause for an accurate shock/no-shock decision. RESULTS The sensitivity and PPV of the pause detector were 93.5% and 97.3%, respectively. The sensitivity and specificity of the SAA in the detected pauses were 93.8% (90% low CI, 90.0%) and 95.9% (90% low CI, 94.7%), respectively. Using the method, shocks would have been advanced in 97% of occasions. For patients in nonshockable rhythms, rhythm reassessment pauses would be avoided in 95.2% (95% CI, 91.6-98.8) of occasions, thus increasing the overall chest compression fraction (CCF). CONCLUSION An automatic method could be used to safely analyse the rhythm during ventilation pauses. This would contribute to an early detection of refibrillation, and to increase CCF in patients with nonshockable rhythms.

Ventricular fibrillation detection in ventricular fibrillation signals corrupted by cardiopulmonary resuscitation artifact

This study is focused on the removal of artifacts due to cardiopulmonary resuscitation (CPR) on ventricular fibrillation ECG signals. The aim is to allow a reliable analysis of the cardiac rhythm by an AED or the defibrillation success analysis during CPR episodes. The research is based on a human model for the CPR artifact and the VF ECG signals. The test signals were generated adding the CPR artifact (noise) to the VF (signal), with a known signal-to-noise Ratio (SNR). The results of the adaptive Kalman filtering have been obtained according to three different levels: SNR improvement; sensitivity improvement in the AED algorithm for the detection of shockable rhythm; and variations of the significant frequencies, compared to the values obtained with the original VF signals. In all cases, remarkable results have been achieved regarding to the efficiency in the artifact removal.

Comparative analysis of the parameters affecting AED rhythm analysis algorithm applied to pediatric and adult Ventricular Tachycardia

In this study pediatric and adult Ventricular Tachycardia (VT) are used to test the efficiency of an AED analysis algorithm. Statistical assessment of the four significant parameters that define the shock-noshock classification algorithm has been performed. The following parameters are considered: Pulse Rate (PR), Waveform Power Ratio (WPR), and two morphological parameters, Baseline Content (BC) and Probability Distribution Width (PDW). A set of 76 adult and 55 pediatric shockable VT episodes is considered to measure the sensitivity of the classification algorithm originally developed for adult patients (100% for rapid adult VT). The sensitivity for the whole pediatric set is 96.36 %, but increases to 100% for the 1-8 years of age subgroup.

A pediatric shock advice algorithm based on the regularity of the detected beats

Automated external defibrillators (AED) detect fatal ventricular arrhythmias: ventricular fibrillation (VF) and ventricular tachycardia (VT). We have developed an algorithm based on the regularity of the detected beats to accurately discriminate VF from nonshockable rhythms in pediatric patients.The beat detection method is based on a preprocessing band pass filter (5-35 Hz) followed by a nonlinear energy operator (NEO). The discrimination algorithm uses three parameters: the number of detected beats, the coefficient of variation of the interval between beats and the content around the zero line of the output of NEO. The values of these parameters were used in a decision tree that identified irregular shockable rhythms (VF), and slow and fast regular rhythms, classified as nonshockable. VT was excluded in the design of the algorithm because it is often a regular but shockable rhythm. The algorithm was tested on a database of 1091 records (959 nonshockable, 62 VF and 70 VT) from 650 pediatric patients. The specificity was 99.7% and the VF sensitivity was 96.6%. 33% of the VT windows were identified as shockable, 65.2% as fast nonshockable and 1.8% as slow nonshockable. The regularity of the detected beats can accurately discriminate VF from nonshockable rhythms. However, an additional stage to discriminate fast nonshockable rhythms from fast and regular VT is needed for a shock advice algorithm.