OBJECTIVE: Prior studies have shown that interruptions of chest compressions could result in high failure rates of resuscitation. Chest compression artifacts force the interruption of compressions before electrocardiographic rhythm analysis. It was the goal of this study to evaluate the accuracy of an automated electrocardiographic rhythm analysis algorithm designed to attenuate compression-induced artifact and minimize uninterrupted chest compressions. DESIGN: Retrospective diagnostic analysis. SETTING: Out-of-hospital cardiopulmonary resuscitation. SUBJECTS: Eight hundred thirty-two patients. INTERVENTIONS: Patients were treated with defibrillation and cardiopulmonary resuscitation. Continuous data were recorded using automated external defibrillators with concurrent measurement of electrocardiographic and sternal motion during chest compressions. MEASUREMENTS AND RESULTS: Human electrocardiographics recorded by automated external defibrillators were annotated and randomly selected to build distinct training and testing databases. The artifact reduction and tolerant filter was applied to the electrocardiographic signal. The algorithm was optimized with the training database (sensitivity, 93.9%; specificity, 91.2%) and tested with the testing database (sensitivity, 92.1%; specificity, 90.5%). Average attenuation of compression-induced artifact was more than 35 dB. CONCLUSIONS: Shockable ventricular arrhythmias can be differentiated from electrocardiographic rhythms not requiring defibrillation in the presence of chest compression-induced artifact with sensitivity and specificity above 90%. With the artifact reduction and tolerant filter, it is possible to effectively eliminate pre- and postshock compression pauses.
OBJECTIVE: Prior studies have shown that interruptions of chest compressions could result in high failure rates of resuscitation. Chest compression artifacts force the interruption of compressions before electrocardiographic rhythm analysis. It was the goal of this study to evaluate the accuracy of an automated electrocardiographic rhythm analysis algorithm designed to attenuate compression-induced artifact and minimize uninterrupted chest compressions. DESIGN: Retrospective diagnostic analysis. SETTING: Out-of-hospital cardiopulmonary resuscitation. SUBJECTS: Eight hundred thirty-two patients. INTERVENTIONS:Patients were treated with defibrillation and cardiopulmonary resuscitation. Continuous data were recorded using automated external defibrillators with concurrent measurement of electrocardiographic and sternal motion during chest compressions. MEASUREMENTS AND RESULTS:Human electrocardiographics recorded by automated external defibrillators were annotated and randomly selected to build distinct training and testing databases. The artifact reduction and tolerant filter was applied to the electrocardiographic signal. The algorithm was optimized with the training database (sensitivity, 93.9%; specificity, 91.2%) and tested with the testing database (sensitivity, 92.1%; specificity, 90.5%). Average attenuation of compression-induced artifact was more than 35 dB. CONCLUSIONS: Shockable ventricular arrhythmias can be differentiated from electrocardiographic rhythms not requiring defibrillation in the presence of chest compression-induced artifact with sensitivity and specificity above 90%. With the artifact reduction and tolerant filter, it is possible to effectively eliminate pre- and postshock compression pauses.
Authors: Theresa M Olasveengen; Mary E Mancini; Gavin D Perkins; Suzanne Avis; Steven Brooks; Maaret Castrén; Sung Phil Chung; Julie Considine; Keith Couper; Raffo Escalante; Tetsuo Hatanaka; Kevin K C Hung; Peter Kudenchuk; Swee Han Lim; Chika Nishiyama; Giuseppe Ristagno; Federico Semeraro; Christopher M Smith; Michael A Smyth; Christian Vaillancourt; Jerry P Nolan; Mary Fran Hazinski; Peter T Morley Journal: Resuscitation Date: 2020-10-21 Impact factor: 5.262
Authors: Sofia Ruiz de Gauna; Unai Irusta; Jesus Ruiz; Unai Ayala; Elisabete Aramendi; Trygve Eftestøl Journal: Biomed Res Int Date: 2014-01-09 Impact factor: 3.411
Authors: U Ayala; U Irusta; J Ruiz; T Eftestøl; J Kramer-Johansen; F Alonso-Atienza; E Alonso; D González-Otero Journal: Biomed Res Int Date: 2014-05-07 Impact factor: 3.411