A Wearable Real-time Telemonitoring Electrocardiogram Device Compared with Traditional Holter Monitoring

Overview

Journal J Biomed Res

Specialty General Medicine

Date 2021 Jan 26

PMID 33495426

Citations 3

Authors

Qin Shen

Jianqing Li

Chang Cui

Xingyao Wang

Hongxiang Gao

Chengyu Liu

Minglong Chen

Affiliations

Soon will be listed here.

Abstract

Arrhythmias are very common in the healthy populations as well as patients with cardiovascular diseases. Among them, atrial fibrillation (AF) and malignant ventricular arrhythmias are usually associated with some clinical events. Early diagnosis of arrhythmias, particularly AF and ventricular arrhythmias, is very important for the treatment and prognosis of patients. Holter is a gold standard commonly recommended for noninvasive detection of paroxysmal arrhythmia. However, it has some shortcomings such as fixed detection timings, delayed report and inability of remote real-time detection. To deal with such problems, we designed and applied a new wearable 72-hour triple-lead H3-electrocardiogram (ECG) device with a remote cloud-based ECG platform and an expert-supporting system. In this study, 31 patients were recruited and 24-hour synchronous ECG data by H3-ECG and Holter were recorded. In the H3-ECG group, ECG signals were transmitted using remote real-time modes, and confirmed reports were made by doctors in the remote expert-supporting system, while the traditional modes and detection systems were used in the Holter group. The results showed no significant differences between the two groups in 24-hour total heart rate (HR), averaged HR, maximum HR, minimum HR, premature atrial complexes (PACs) and premature ventricular complexes (PVCs) ( >0.05). The sensitivity and specificity of capture and remote automatic cardiac events detection of PACs, PVCs, and AF by H3-ECG were 93% and 99%, 98% and 99%, 94% and 98%, respectively. Therefore, the long-term limb triple-lead H3-ECG device can be utilized for domiciliary ECG self-monitoring and remote management of patients with common arrhythmia under medical supervision.

Citing Articles

Development and Validation of a Real-Time Service Model for Noise Removal and Arrhythmia Classification Using Electrocardiogram Signals.

Park Y, Park Y, Jeong H, Kim K, Jung J, Kim J Sensors (Basel). 2024; 24(16).

PMID: 39204918 PMC: 11360629. DOI: 10.3390/s24165222.

Cardiorespiratory Sensors and Their Implications for Out-of-Hospital Cardiac Arrest Detection: A Systematic Review.

Lingawi S, Hutton J, Khalili M, Shadgan B, Christenson J, Grunau B Ann Biomed Eng. 2024; 52(5):1136-1158.

PMID: 38358559 DOI: 10.1007/s10439-024-03442-y.

Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis.

Manetas-Stavrakakis N, Sotiropoulou I, Paraskevas T, Maneta Stavrakaki S, Bampatsias D, Xanthopoulos A J Clin Med. 2023; 12(20).

PMID: 37892714 PMC: 10607777. DOI: 10.3390/jcm12206576.

References

Poon K, Okin P, Kligfield P . Diagnostic performance of a computer-based ECG rhythm algorithm. J Electrocardiol. 2005; 38(3):235-8. DOI: 10.1016/j.jelectrocard.2005.01.008. View

Zhou Z, Hu D . An epidemiological study on the prevalence of atrial fibrillation in the Chinese population of mainland China. J Epidemiol. 2008; 18(5):209-16. PMC: 4771592. DOI: 10.2188/jea.je2008021. View

de Asmundis C, Conte G, Sieira J, Chierchia G, Rodriguez-Manero M, di Giovanni G . Comparison of the patient-activated event recording system vs. traditional 24 h Holter electrocardiography in individuals with paroxysmal palpitations or dizziness. Europace. 2014; 16(8):1231-5. DOI: 10.1093/europace/eut411. View

Joshi A, Kowey P, Prystowsky E, Benditt D, Cannom D, Pratt C . First experience with a Mobile Cardiac Outpatient Telemetry (MCOT) system for the diagnosis and management of cardiac arrhythmia. Am J Cardiol. 2005; 95(7):878-81. DOI: 10.1016/j.amjcard.2004.12.015. View

Lowres N, Neubeck L, Salkeld G, Krass I, McLachlan A, Redfern J . Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study. Thromb Haemost. 2014; 111(6):1167-76. DOI: 10.1160/TH14-03-0231. View

Chan P, Wong C, Pun L, Wong Y, Wong M, Chu D . Head-to-Head Comparison of the AliveCor Heart Monitor and Microlife WatchBP Office AFIB for Atrial Fibrillation Screening in a Primary Care Setting. Circulation. 2016; 135(1):110-112. DOI: 10.1161/CIRCULATIONAHA.116.024439. View

Tung C, Su D, Turakhia M, Lansberg M . Diagnostic Yield of Extended Cardiac Patch Monitoring in Patients with Stroke or TIA. Front Neurol. 2015; 5:266. PMC: 4290477. DOI: 10.3389/fneur.2014.00266. View

Liu F, Liu C, Jiang X, Zhang Z, Zhang Y, Li J . Performance Analysis of Ten Common QRS Detectors on Different ECG Application Cases. J Healthc Eng. 2018; 2018:9050812. PMC: 5964584. DOI: 10.1155/2018/9050812. View

Haberman Z, Jahn R, Bose R, Tun H, Shinbane J, Doshi R . Wireless Smartphone ECG Enables Large-Scale Screening in Diverse Populations. J Cardiovasc Electrophysiol. 2015; 26(5):520-6. DOI: 10.1111/jce.12634. View

10.

Xu W, Li M, Chen M, Yang B, Wang D, Kong X . Effect of burden and origin sites of premature ventricular contractions on left ventricular function by 7-day Holter monitor. J Biomed Res. 2015; 29(6):465-74. PMC: 4662208. DOI: 10.7555/JBR.29.20150032. View

11.

Olson J, Fouts A, Padanilam B, Prystowsky E . Utility of mobile cardiac outpatient telemetry for the diagnosis of palpitations, presyncope, syncope, and the assessment of therapy efficacy. J Cardiovasc Electrophysiol. 2007; 18(5):473-7. DOI: 10.1111/j.1540-8167.2007.00779.x. View

12.

Munger T, Wu L, Shen W . Atrial fibrillation. J Biomed Res. 2014; 28(1):1-17. PMC: 3904170. DOI: 10.7555/JBR.28.20130191. View

13.

Sampaio S, Craveiro N, Darrieux F, de Oliveira I, Scanavacca M, Hachul D . Accuracy of the pacemaker event recorder versus Holter-ECG to detect both symptomatic and asymptomatic ventricular arrhythmias. J Cardiovasc Electrophysiol. 2017; 29(1):154-159. DOI: 10.1111/jce.13370. View

14.

Friesen G, Jannett T, Jadallah M, Yates S, Quint S, Nagle H . A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Trans Biomed Eng. 1990; 37(1):85-98. DOI: 10.1109/10.43620. View

15.

Rothman S, Laughlin J, Seltzer J, Walia J, Baman R, Siouffi S . The diagnosis of cardiac arrhythmias: a prospective multi-center randomized study comparing mobile cardiac outpatient telemetry versus standard loop event monitoring. J Cardiovasc Electrophysiol. 2007; 18(3):241-7. DOI: 10.1111/j.1540-8167.2006.00729.x. View

16.

Barrett P, Komatireddy R, Haaser S, Topol S, Sheard J, Encinas J . Comparison of 24-hour Holter monitoring with 14-day novel adhesive patch electrocardiographic monitoring. Am J Med. 2014; 127(1):95.e11-7. PMC: 3882198. DOI: 10.1016/j.amjmed.2013.10.003. View

17.

Reiffel J, Schwarzberg R, Murry M . Comparison of autotriggered memory loop recorders versus standard loop recorders versus 24-hour Holter monitors for arrhythmia detection. Am J Cardiol. 2005; 95(9):1055-9. DOI: 10.1016/j.amjcard.2005.01.025. View

18.

Sobocinski P, Anggardh Rooth E, Kull V, von Arbin M, Wallen H, Rosenqvist M . Improved screening for silent atrial fibrillation after ischaemic stroke. Europace. 2012; 14(8):1112-6. DOI: 10.1093/europace/eur431. View

19.

Bumgarner J, Lambert C, Hussein A, Cantillon D, Baranowski B, Wolski K . Smartwatch Algorithm for Automated Detection of Atrial Fibrillation. J Am Coll Cardiol. 2018; 71(21):2381-2388. DOI: 10.1016/j.jacc.2018.03.003. View

20.

Wang T, Larson M, Levy D, Vasan R, Leip E, Wolf P . Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: the Framingham Heart Study. Circulation. 2003; 107(23):2920-5. DOI: 10.1161/01.CIR.0000072767.89944.6E. View