title: Cardiopulmonary Resuscitation Pattern Evaluation Based on Ensemble Empirical Mode Decomposition Filter via Nonlinear Approaches.
abstract: Good quality cardiopulmonary resuscitation (CPR) is the mainstay of treatment for managing patients with out-of-hospital cardiac arrest (OHCA). Assessment of the quality of the CPR delivered is now possible through the electrocardiography (ECG) signal that can be collected by an automated external defibrillator (AED). This study evaluates a nonlinear approximation of the CPR given to the asystole patients. The raw ECG signal is filtered using ensemble empirical mode decomposition (EEMD), and the CPR-related intrinsic mode functions (IMF) are chosen to be evaluated. In addition, sample entropy (SE), complexity index (CI), and detrended fluctuation algorithm (DFA) are collated and statistical analysis is performed using ANOVA. The primary outcome measure assessed is the patient survival rate after two hours. CPR pattern of 951 asystole patients was analyzed for quality of CPR delivered. There was no significant difference observed in the CPR-related IMFs peak-to-peak interval analysis for patients who are younger or older than 60 years of age, similarly to the amplitude difference evaluation for SE and DFA. However, there is a difference noted for the CI (p < 0.05). The results show that patients group younger than 60 years have higher survival rate with high complexity of the CPR-IMFs amplitude differences.
authors.affiliation: Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan.
authors.forename: Muammar
authors.identifier: 0000-0002-2818-242X
authors.initials: M
authors.lastname: Sadrawi
authors.affiliation: Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
authors.forename: Wei-Zen
authors.identifier: 0000-0001-8543-2600
authors.initials: WZ
authors.lastname: Sun
authors.affiliation: Department of Emergency Medicine, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
authors.forename: Matthew Huei-Ming
authors.initials: MH
authors.lastname: Ma
authors.affiliation: Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei 100, Taiwan.
authors.forename: Chun-Yi
authors.identifier: 0000-0002-4131-0502
authors.initials: CY
authors.lastname: Dai
authors.affiliation: Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK.
authors.forename: Maysam F
authors.identifier: 0000-0002-8515-7933
authors.initials: MF
authors.lastname: Abbod
authors.affiliation: Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan.
authors.forename: Jiann-Shing
authors.identifier: 0000-0002-6407-5090
authors.initials: JS
authors.lastname: Shieh
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016887:Cardiopulmonary Resuscitation; D047548:Defibrillators; D004562:Electrocardiography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D058687:Out-of-Hospital Cardiac Arrest / Q000503:physiopathology / Q000628:therapy; D012815:Signal Processing, Computer-Assisted