Cardiopulmonary Resuscitation Pattern Evaluation Based on Ensemble Empirical Mode Decomposition Filter via Nonlinear...

Area of Science:

• Cardiology
• Biomedical Engineering
• Data Science

Background:

• Effective cardiopulmonary resuscitation (CPR) is critical for out-of-hospital cardiac arrest (OHCA) survival.
• Automated external defibrillators (AEDs) can now assess CPR quality using electrocardiography (ECG) signals.

Purpose of the Study:

• To evaluate nonlinear approximations of CPR quality in asystole patients.
• To correlate CPR signal complexity with patient survival rates.

Main Methods:

• ECG signals were filtered using ensemble empirical mode decomposition (EEMD).
• CPR-related intrinsic mode functions (IMFs) were analyzed using sample entropy (SE), complexity index (CI), and detrended fluctuation analysis (DFA).
• Statistical analysis was performed using ANOVA on data from 951 asystole patients.

Main Results:

• No significant age-related differences in CPR-related IMFs peak-to-peak intervals, SE, or DFA.
• A significant difference was noted for the complexity index (CI) (p < 0.05).
• Patients younger than 60 showed higher survival rates associated with greater complexity in CPR-IMF amplitude differences.

Conclusions:

• CPR signal complexity, particularly amplitude differences in CPR-IMFs, is a potential predictor of survival in OHCA patients.
• Nonlinear analysis methods like CI can differentiate CPR quality relevant to patient outcomes.
• Age is a factor in the relationship between CPR complexity and survival.