Details

Autor(en) / Beteiligte

• Isasi, Iraia
• Irusta, Unai
• Aramendi, Elisabete
• Ayala, Unai
• Alonso, Erik
• Kramer-Johansen, Jo
• Eftestol, Trygve

Titel

A Multistage Algorithm for ECG Rhythm Analysis During Piston-Driven Mechanical Chest Compressions

Ist Teil von

• IEEE transactions on biomedical engineering, 2019-01, Vol.66 (1), p.263-272

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2019

Quelle

IEEE

Beschreibungen/Notizen

• Goal: An accurate rhythm analysis during cardiopulmonary resuscitation (CPR) would contribute to increase the survival from out-of-hospital cardiac arrest. Piston-driven mechanical compression devices are frequently used to deliver CPR. The objective of this paper was to design a method to accurately diagnose the rhythm during compressions delivered by a piston-driven device. Methods: Data was gathered from 230 out-of-hospital cardiac arrest patients treated with the LUCAS 2 mechanical CPR device. The dataset comprised 201 shockable and 844 nonshockable ECG segments, whereof 270 were asystole (AS) and 574 organized rhythm (OR). A multistage algorithm (MSA) was designed, which included two artifact filters based on a recursive least squares algorithm, a rhythm analysis algorithm from a commercial defibrillator, and an ECG-slope-based rhythm classifier. Data was partitioned randomly and patient-wise into training (60%) and test (40%) for optimization and validation, and statistically meaningful results were obtained repeating the process 500 times. Results: The mean (standard deviation) sensitivity (SE) for shockable rhythms, specificity (SP) for nonshockable rhythms, and the total accuracy of the MSA solution were: 91.7 (6.0), 98.1 (1.1), and 96.9 (0.9), respectively. The SP for AS and OR were 98.0 (1.7) and 98.1 (1.4), respectively. Conclusions: The SE/SP were above the 90%/95% values recommended by the American Heart Association for shockable and nonshockable rhythms other than sinus rhythm, respectively. Significance: It is possible to accurately diagnose the rhythm during mechanical chest compressions and the results considerably improve those obtained by previous algorithms.