Reconfirmation algorithms should be the standard of care in automated external defibrillators

Visual data mining with self-organising maps for ventricular fibrillation analysis

Detection of ventricular fibrillation (VF) at an early stage is being deeply studied in order to lower the risk of sudden death and allows the specialist to have greater reaction time to give the patient a good recovering therapy. Some works are focusing on detecting VF based on numerical analysis of time-frequency distributions, but in general the methods used do not provide insight into the problem. However, this study proposes a new methodology in order to obtain information about this problem. This work uses a supervised self-organising map (SOM) to obtain visually information among four important groups of patients: VF (ventricular fibrillation), VT (ventricular tachycardia), HP (healthy patients) and AHR (other anomalous heart rates and noise). A total number of 27 variables were obtained from continuous surface ECG recordings in standard databases (MIT and AHA), providing information in the time, frequency, and time-frequency domains. self-organising maps (SOMs), trained with 11 of the 27 variables, were used to extract knowledge about the variable values for each group of patients. Results show that the SOM technique allows to determine the profile of each group of patients, assisting in gaining a deeper understanding of this clinical problem. Additionally, information about the most relevant variables is given by the SOM analysis.

Automated detection of ventricular fibrillation to guide cardiopulmonary resuscitation

Sudden death due to ventricular fibrillation (VF) is a catastrophic event, especially in out-of-hospital settings. Prompt detection of VF and preparedness to intervene with cardiopulmonary resuscitation (CPR) and especially the delivery of an electrical shock is potentially lifesaving. The reliability and accuracy of automated VF detection by current versions of automated external defibrillators (AEDs) require interruption of CPR because the ECG signal, which is the source of rhythm detection, is corrupted by chest compressions. Significantly better outcomes have been reported if effective chest compression precedes electrical defibrillation and especially if interruptions are minimized. We therefore sought a method by which VF detection could proceed without interrupting chest compressions. A VF detection algorithm was therefore derived based on a method by which continuous wavelet transform is used, together with measurement of morphologic consistency. This method was intended to distinguish between disorganized and organized rhythms. The Fourier-transform-based amplitude spectrum analysis was then used to detect the likelihood that VF was the rhythm prompting the delivery of an electrical shock. The algorithm was validated on 33,095 electrocardiographic segments, including 8840 segments corrupted by compression artifacts from 232 patients after out-of-hospital cardiac arrest. Nine thousand one hundred eighty-seven of 10,042 VF segments and 20,884 of 23,053 non-VF segments were correctly classified, with a sensitivity of 91.5% and a specificity of 90.6%. Although the proposed algorithm has a lesser predictive value for VF detection than the uncorrupted ECGs in clinical settings, it has the major potential for automated rhythm identification to guide defibrillation without repetitive interruptions of CPR.