Performance and feasibility of three different approaches for computer based semi-automated analysis of ventricular arrhythmias in telemetric long-term ECG in cynomolgus monkeys.

Autor: Eiringhaus J; Hannover Heart Rhythm Center, Department of Cardiology & Angiology, Hannover Medical School, Germany. Electronic address: Eiringhaus.Joerg@mh-hannover.de., de Vries AL; Hannover Heart Rhythm Center, Department of Cardiology & Angiology, Hannover Medical School, Germany., Hohmann S; Hannover Heart Rhythm Center, Department of Cardiology & Angiology, Hannover Medical School, Germany. Electronic address: Hohmann.Stephan@mh-hannover.de., Böthig D; Department of Pediatric Cardiology and Pediatric Intensive Care, Hannover Medical School, Germany. Electronic address: Boethig.Dietmar@mh-hannover.de., Müller-Leisse J; Hannover Heart Rhythm Center, Department of Cardiology & Angiology, Hannover Medical School, Germany. Electronic address: Mueller-Leisse.Johanna@mh-hannover.de., Hillmann HAK; Hannover Heart Rhythm Center, Department of Cardiology & Angiology, Hannover Medical School, Germany. Electronic address: Hillmann.Henrike@mh-hannover.de., Martens A; Department of Cardiac, Thoracic, Transplantation, and Vascular Surgery, Hannover Medical School, Germany. Electronic address: martens.andreas@mh-hannover.de., Zweigerdt R; Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Department of Cardiac, Thoracic, Transplantation, and Vascular Surgery, Hannover Medical School, Germany. Electronic address: zweigerdt.robert@mh-hannover.de., Schrod A; German Primate Center, Göttingen, Germany. Electronic address: aschrod@dpz.eu., Martin U; Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Department of Cardiac, Thoracic, Transplantation, and Vascular Surgery, Hannover Medical School, Germany. Electronic address: Martin.Ulrich@mh-hannover.de., Duncker D; Hannover Heart Rhythm Center, Department of Cardiology & Angiology, Hannover Medical School, Germany. Electronic address: Duncker.David@mh-hannover.de., Gruh I; Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Department of Cardiac, Thoracic, Transplantation, and Vascular Surgery, Hannover Medical School, Germany. Electronic address: Gruh.Ina@mh-hannover.de., Veltmann C; Hannover Heart Rhythm Center, Department of Cardiology & Angiology, Hannover Medical School, Germany; Center for Electrophysiology Bremen, Bremen, Germany. Electronic address: c.veltmann@ep-bremen.com.
Jazyk: angličtina
Zdroj: Journal of pharmacological and toxicological methods [J Pharmacol Toxicol Methods] 2023 Nov-Dec; Vol. 124, pp. 107471. Date of Electronic Publication: 2023 Sep 09.
DOI: 10.1016/j.vascn.2023.107471
Abstrakt: Computer-based analysis of long-term electrocardiogram (ECG) monitoring in animal models represents a cost and time-consuming process as manual supervision is often performed to ensure accuracy in arrhythmia detection. Here, we investigate the performance and feasibility of three ECG interval analysis approaches A) attribute-based, B) attribute- and pattern recognition-based and C) combined approach with additional manual beat-to-beat analysis (gold standard) with regard to subsequent detection of ventricular arrhythmias (VA) and time consumption. ECG analysis was performed on ECG raw data of 5 male cynomolgus monkeys (1000 h total, 2 × 100 h per animal). Both approaches A and B overestimated the total number of arrhythmias compared to gold standard (+8.92% vs. +6.47%). With regard to correct classification of detected VA event numbers (accelerated idioventricular rhythms [AIVR], ventricular tachycardia [VT]) approach B revealed higher accuracy compared to approach A. Importantly, VA burden (% of time) was precisely depicted when using approach B (-1.13%), whereas approach A resulted in relevant undersensing of ventricular arrhythmias (-11.76%). Of note, approach A and B could be performed with significant less working time (-95% and - 91% working time) compared to gold standard. In sum, we show that a combination of attribute-based and pattern recognition analysis (approach B) can reproduce VA burden with acceptable accuracy without using manual supervision. Since this approach allowed analyses to be performed with distinct time saving it represents a valuable approach for cost and time efficient analysis of large preclinical ECG datasets.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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