Abstract 265: Analyzing Resuscitation Quality by Reviewing Automatic External Defibrillator Data Using Empirical Mode Decomposition Based Algorithm
Background: High quality cardiopulmonary resuscitation (CPR) improves survival of out-of-hospital cardiac arrests (OHCAs). Our team has developed an empirical mode decomposition (EMD) based algorithm to identify chest compression fluctuations from ECG signals retrieved from automatic external defibrillators (AEDs) in OHCAs presenting with asystole. In this study, we further applied this method on pulseless electric activities (PEAs).
Methods: ECGs of AED from 30 randomly selected OHCAs in Taipei EMS presented with PEA were retrieved. Initial 5 minutes of each ECG strips were divided into 5 one-min segments. EMD decomposed the ECG segments into several intrinsic mode fluctuations (IMFs). The dominant IMFs attributed to chest compressions are identified before fed back to the original corrupted signals as a reference to pursuit optimal compression signal by least linear square method. Each compression can be identified by checking local periodicity of the optimized compression signal. CPR quality indicators, including compression number, no flow time and compression rate, were calculated min-by-min by the algorithm, and compared with those derived from manual reviews of ECG and audio by experienced physicians.
Result: Thirty OHCAs were enrolled and 106 segments, excluding 44 segments with transport movement artifacts, underwent both algorithm and manual review. The results derived from two methods were highly correlated (compression number, r=0.86; no flow time, r=0.81) with good agreements shown in Bland-Altman plot. (Fig.)
Conclusion: The EMD algorithm is capable of extracting CPR signals during PEA and enables potential monitoring for CPR quality.
- © 2013 by American Heart Association, Inc.