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学术急诊医学档案

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卷 10 编号 1 (2022)

一月 2022

Remote Analysis and Transmission System of Electrocardiogram in Prehospital Setting; a Diagnostic Accuracy Study

  • Elmira Almukhambetova
  • Murat Almukhambetov
  • Abdugani Musayev
  • Ainur Yeshmanova
  • Vildan Indershiyev
  • Zhadira Kalhodzhaeva

学术急诊医学档案, 卷 10 编号 1 (2022), 1 一月 2022 , 第 e5 页
https://doi.org/10.22037/aaem.v10i1.1399 已出版: 2022-01-01

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摘要

Introduction: One of the trends in the development of medical technologies is considered to be telemedicine. This study aimed to evaluate the accuracy of a remote electrocardiogram (ECG) analysis and transmission system in prehospital setting.

Methods: In this cross-sectional study, the data of 19,265 ECGs was gathered from emergency medical service (EMS) database of Almaty city, Kazakhstan, from 2015 to 2019. All ECGs were recorded in the prehospital setting by a paramedic, using "Poly-Spectrum" ECG recording device. Subsequently, all ECGs were sent to the cardiologist for interpretation and the findings were compared between software and cardiologist.

Results: 19,265 ECGs were registered. The average time from taking ECGs to receiving an expert’s conclusion was 9.2 ± 2.5 minutes. The medical teams were called in 17.9% of cases after paramedic ECG recording; however, in the rest of the cases there was no need to call those teams. Using the device reduced the number of visits of specialist teams.

The overall sensitivity, specificity, and accuracy of ECG analysis device in diagnosis of ECG abnormalities were 83.8% (95%CI: 82.6 – 84.9), 95.5% (95%CI: 95.1 – 95.8), and 93.3% (95%CI: 92.9 – 93.7), respectively.

Conclusion: The findings of this study showed the 93.3% accuracy of automatic ECG analysis device in interpretation of ECG abnormalities in prehospital setting compared with the cardiologist interpretations. Using the device causes a decrease in the number of cardiologist visits needed as well as reduction in cost and elapsed time.

关键词:
  • Cardiovascular system
  • cardiovascular diseases
  • diagnosis
  • quality of health care
  • health services administration
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Almukhambetova E, Almukhambetov M, Musayev A, Yeshmanova A, Indershiyev V, Kalhodzhaeva Z. Remote Analysis and Transmission System of Electrocardiogram in Prehospital Setting; a Diagnostic Accuracy Study. Arch Acad Emerg Med [网际网络]. 2022年1月1日 [见引于 2026年7月7日];10(1):e5. 载于: https://journals.sbmu.ac.ir/aaem/index.php/AAEM/article/view/1399
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参考

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Guo S-L, Han L-N, Liu H-W, Si Q-J, Kong D-F, Guo F-S. The future of remote ECG monitoring systems. Journal of geriatric cardiology: JGC. 2016;13(6):528-30.

Bansal A, Kumar S, Bajpai A, Tiwari V, Nayak M, Venkatesan S., et al. Remote health monitoring system for detecting cardiac disorders. IET Systems Biology 2015;9(6):309-14.

Martinez-Tabares F, Costa-Salas Y, Cuesta-Frau D, Castellanos-Dominguez G. Multiobjective Design of Wearable Sensor Systems for Electrocardiogram Monitoring. Journal of Sensors. 2016;2016:15.

Gonzalez E, Peña R, Vargas-Rosales C, Avila A, De Cerio D. Survey of WBSNs for Pre-Hospital Assistance: Trends to Maximize the Network Lifetime and Video Transmission Techniques. Sensors. 2015;15(5):11993-2021.

Yaakob N, Khalil I. A Novel Congestion Avoidance Technique for Simultaneous Real-Time Medical Data Transmission. IEEE Journal of Biomedical and Health Informatics. 2016;20(2):669-81.

Ribeiro AH, Ribeiro MH, Paixão GMM, Oliveira DM, Gomes PR, Canazart JA, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nature Communcations. 2020;11(1):1760.

Aljafar L, Alotaiby T, Al-Yami R, Alshebeili S, Zouhair J, editors. Classification of ECG signals of normal and abnormal subjects using common spatial pattern. 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA); 2016: Institute of Electrical and Electronics Engineers.

Bonomini MP, Ingallina FJ, Barone V, Valentinuzzi ME, Arini PD, editors. Comparison of Electrocardiographic and Vectorcardiographic Planes on a Set of Left Ventricular Hypertrophy Patients. VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014; 2015 2015; Cham: Springer International Publishing.

Gregg R, Zhou S, Babaeizadeh S. Can vectorcardiogram criteria unmask inferior Q-waves in Mason-Likar 12-lead ECG? Journal of Electrocardiology. 2018;51(1):2-3.

Said SA, Bloo R, de Nooijer R, Slootweg A. Cardiac and non-cardiac causes of T-wave inversion in the precordial leads in adult subjects: A Dutch case series and review of the literature. World journal of cardiology. 2015;7(2):86-100.

Jonas DE, Reddy S, Middleton JC, Barclay C, Green J, Baker C, et al. Screening for Cardiovascular Disease Risk With Resting or Exercise Electrocardiography: Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA. 2018;319(22):2315-28.

Steinberg JS, Varma N, Cygankiewicz I, Aziz P, Balsam P, Baranchuk A, et al. 2017 ISHNE-HRS expert consensus statement on ambulatory ECG and external cardiac monitoring/telemetry. Heart Rhythm. 2017;14(7):e55-e96.

Mant J, Fitzmaurice DA, Hobbs FD, Jowett S, Murray ET, Holder R, et al. Accuracy of diagnosing atrial fibrillation on electrocardiogram by primary care practitioners and interpretative diagnostic software: analysis of data from screening for atrial fibrillation in the elderly (SAFE) trial. Bmj. 2007;335(7616):380.

Lyon A, Mincholé A, Martínez JP, Laguna P, Rodriguez B. Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. Journal of the Royal Society, Interface. 2018;15(138):2017.

de Chazal P, O'Dwyer M, Reilly RB. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. Institute of Electrical and Electronics Engineers Transactions on Biomedical Engineering. 2004;51(7):1196-206.

Niwas S I, Selva Kumari RS, Sadasivam V. Artificial neural network based automatic cardiac abnormalities classification: Institute of Electrical and Electronics Engineers; 2005. 41-6 p.

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