title: A new method without reference channels used for ventricular fibrillation detection during cardiopulmonary resuscitation.
abstract: Ventricular fibrillation (VF) is observed as the initial rhythm in the majority of patients suffering from sudden cardiac arrest. It is vitally important to accurately recognize the initial VF rhythm and then implement electrical defibrillation. However, artifacts produced by chest compression during cardiopulmonary resuscitation (CPR) make the VF detection algorithms utilized by current automated external defibrillators (AEDs) unreliable. CPR must be traditionally interrupted for a reliable diagnosis. However, interruptions in chest compression have a deleterious effect on the success of defibrillation. The elimination of the CPR artifacts would enable compressions to continue during AED VF detection and thereby increase the likelihood of resuscitation success. We have estimated a model of this artifact by adaptively incorporating noise-assisted multivariate empirical mode decomposition (NA-MEMD) and least mean squares (LMS) and then removing the artifact from the corrupted ECGs. The simulation experiment indicated that the CPR artifact could be accurately modeled without any reference channels. We constructed a BP neural network to evaluate the results. A total of 372 VF and 645 normal sinus rhythm (SR) ECG samples were included in the analysis, and 24 CPR artifact signals were used to construct corrupted ECGs. The results indicated that at different SNR levels ranging from 0 to -12 dB, the sensitivity and specificity were always above 95 and 80 %, respectively.
authors.affiliation: Institute of Medical Equipment, Academy of Military Medical Science, Tianjin, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Yu
authors.affiliation: Institute of Medical Equipment, Academy of Military Medical Science, Tianjin, China.
authors.forename: Guang
authors.initials: G
authors.lastname: Zhang
authors.affiliation: Institute of Medical Equipment, Academy of Military Medical Science, Tianjin, China.
authors.forename: Taihu
authors.initials: T
authors.lastname: Wu
authors.affiliation: Institute of Medical Equipment, Academy of Military Medical Science, Tianjin, China.
authors.forename: Chao
authors.initials: C
authors.lastname: Li
authors.affiliation: Department of Pharmacology, Logistics University of Chinese People's Armed Police Forces, Tianjin, China.
authors.forename: Zongming
authors.initials: Z
authors.lastname: Wan
authors.affiliation: Institute of Medical Equipment, Academy of Military Medical Science, Tianjin, China.
authors.forename: Liangzhe
authors.initials: L
authors.lastname: Li
authors.affiliation: Institute of Medical Equipment, Academy of Military Medical Science, Tianjin, China.|Instrument Department, The PLA 174 Hospital, Xiamen, China.
authors.forename: Chunfei
authors.initials: C
authors.lastname: Wang
authors.affiliation: Institute of Medical Equipment, Academy of Military Medical Science, Tianjin, China.|Medical Engineering Department, Navy General Hospital of the PLA, Beijing, China.
authors.forename: Yalin
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Institute of Medical Equipment, Academy of Military Medical Science, Tianjin, China.
authors.forename: Hengzhi
authors.initials: H
authors.lastname: Lu
authors.affiliation: Institute of Medical Equipment, Academy of Military Medical Science, Tianjin, China. amms75@outlook.com.
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D016477:Artifacts; D016887:Cardiopulmonary Resuscitation / Q000379:methods* / Q000592:standards*; D004562:Electrocardiography; D006801:Humans; D016571:Neural Networks, Computer; D012372:ROC Curve; D012015:Reference Standards; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D013997:Time Factors; D014693:Ventricular Fibrillation / Q000175:diagnosis*
keywords: Automated external defibrillators (AEDs); BP neural network; Cardiopulmonary resuscitation (CPR); Least mean squares (LMS); Noise-assisted multivariate empirical mode decomposition (NA-MEMD); Ventricular fibrillation (VF)