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Citation: Chernikova D, Mohamed Mohamed MA. The place of digital devices and artificial intelligence in cardiac arrhythmia

management: new advances, practical guides and promising prospects.Jr.med.res.2021; 5(1):7-9. Chernikova et al© All rights are

reserved. https://doi.org/10.32512/jmr.5.1.2022/7.9 Submit your manuscript: www.jmedicalresearch.com

Mini Review

The place of digital devices and artificial intelligence in cardiac arrhythmia management:

new advances, practical guides, and promising prospects.

Chernikova Daryna

**1

, Mohamed Mohamed Mohsen Ahmed

1: Tele-cardiology Working Group,

International Society for Telemedicine

and eHealth, e-Cardiology Working

Group, European Society of Cardiology

2: Cairo University, Egypt

* Corresponding author

** Academic Editor

Correspondence to:

ahmed.mohsen@kasralainy.edu.eg

Publication data:

Submitted: March 24 ,2022

Accepted: May 22 ,2022

Online: June 30, 2022

This article was subject to full peer-review.

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Share: copy and redistribute the material in

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Developing digital devices and remote technologies for the diagnosis and management of arrhythmias

are revolutionizing cardiologist clinical practice and decision-making. Electrocardiogram (ECG) and

plethysmography (PPG)-based devices are widely used to assess cardiac rhythm. Artificial intelligence

(AI) enabled devices may contribute to early detection and monitoring of atrial fibrillation (AF), long

QT syndrome (LQTS), as well as QTc related adverse drug events. Some other devices such as

contactless rhythm monitoring may be useful in ambulatory cardiac arrhythmia mass screening.

Keywords

Digital device ;Arrhythmia ;Atrial fibrillation ;Artificial intelligence ;Review.

Summary

Rapidly developing technologies and increased demand for the use of digital devices over the

past decades contributed to considerable changes in arrhythmias management. A resolution of

the 71

WHO World Health Assembly urged to prioritize the development and greater the use of

digital technologies in health to promote health coverage and advance the sustainable

development goals [1]. According to several surveys conducted with the help of Heart Rhythm

Society communities, mobile smartwatch and non-smartwatch ECG devices were prescribed by

80% of cardiac electrophysiology professionals (EP). In COVID-19 pandemic era, the use of video-

telehealth increased 10-fold [2]. The European Heart Rhythm Association (EHRA) published a

recent practical user guide for digital cardiologic technologies. PPG and ECG-based devices were

introduced for the screening of Atrial fibrillation (AF) and ventricular tachycardia (VT)[3]. The

available data on digital devices including smartwatches, ECG patches, chest straps, mHealth and

smartphones applications contributed considerably to the assessment of cardiac rhythm

abnormalities. These devices may be ECG-based or non-ECG based. The choice depends on the

duration of the symptomatic episodes. Up to date, the 12-lead ECG is the gold standard for the

diagnosis of arrhythmias. However, ECG- or PPG-based device can be useful if 12-lead ECG is not

available. Holter ECG is recommended for recurrent arrhythmia episodes. ECG-patch in turn-

weekly or monthly and implantable loop recorder (ILR) may be of great aid in such clinical

situations. PPG recordings may be of low sensitivity in the assessment short arrhythmias and are

still used to document normal rhythm and normal heart rate [3]. Diagnostic confirmation needs

always a 12-lead ECG or an ECG-based device. Around 37.5 AF cases are diagnosed around the

world. An increase of 33% over the last 20 years was observed. The prevalence may double in

Europe by 2060 [4]. Up to 50 TO 87% of AF patients are initially asymptomatic [5]. Early

detection of AF allows to initiate AF management in the appropriate moments. This may reduce

the mortality related to several associated cardiovascular syndrome [6]. Digital devices may

contribute to stroke risk assessment of, symptoms-rhythm correlation, and management of

concomitant risk factors. This offers a multifunctional management approach for these patients.

The place of digital devices and artificial intelligence in cardiac arrhythmia management: New advances, practical guides and promising prospects

Citation: Chernikova D, Mohamed Mohamed MA. The place of digital devices and artificial intelligence in cardiac arrhythmia

management: new advances, practical guides and promising prospects.Jr.med.res.2021; 5(1):7-9. Chernikova et al© All rights are

reserved. Submit your manuscript: www.jmedicalresearch.com

However, it should be noted that both PPG-based and single-

lead ECG devices may be of low diagnostic sensitivity in

regular tachyarrhythmias. Systematic screening for AF in high

cardiovascular risk population may reduce stroke incidence

[7]. AF is seen in almost 10% of the patients of more than

80 years old. Systematic screening approach should be

implemented also for patients with past stroke history and in

case of multiple associated comorbidity factors. Wearable

devices can be considered first in these cases [8]. New

mHealth approach provides an effective implementation of

digital technologies allowing wait-and-see strategy during

peri-cardioversion [9]. The management of recent AF require

immediate restoration of sinus rhythm by pharmacologic or

electrical cardioversion. However, the results of rate control

versus acute cardioversion ( trial-7 ACWAS) study showed

that spontaneous resolution of recent-onset AF may be

obtained in more than 90% of cases in delayed cardioversion

group which makes the Wait and see strategy and objective

alternative. This does not apply to patients in whom the

duration of atrial fibrillation is unknown. Regardless of

whether a rate or rhythm control strategy is selected, the

patient’s risk for stroke needs always to be estimated and

anticoagulation initiated, if appropriate [10]. According to the

iHEART study, the use of mHealth improved the detection

recurrent atrial arrhythmias after AF ablation [11]. Pilot study

showed that using smartphone ECG with a cloud-based

platform for three months following AF ablation is non inferior

to the standard monitoring plan [12].

AI-enabled mECG device are effective alternative to ECG-

based screening of several other kinds of arrhythmia such as

LQTS. With this monitoring techniques, QTc values are

almost equal to those obtained from a standard 12-lead ECG

[13]. QTc monitoring is useful to prevent QTc-related adverse

drug events. QTc prolonging drugs account for 3% of

prescriptions worldwide and the number of patients

undergoing multiple QTc-prolonging treatments is rapidly

increasing [14]. Moreover, some individuals have potentially

proarrhythmic common genetic variants associated with 8-

fold increased risk of drug-induced LQTS/ torsade de pointe

(TdP) (p.Asp85Asn-KCNE1 variant is present in 1% European

origin individuals and has p.Ser1103Tyr-SCN5A in up to 8%

of African individuals)[15]. The future of QTc monitoring is 6-

lead ECG device that was approved for measurement of QTc

intervals [16] and AI-enabled mECG device with AI-deep

neural network (DNN) that detects QTc values ≥500 ms and

predicts accurately the QTc of a standard 12-lead ECG

[16,17]. According to the available data, the rate of dropout

using health technology is about 44%. The systematic review

of 33 studies showed that the most frequent reasons for

dropout included technical malfunction and difficulties in the

convenience and accessibility. Most of the enrolled patients

preferred standard tools and did not trust the new alternatives

[18]. Other barriers to the implementation of AI-enabled

devices were the cost and insurance reimbursement issues.

Personalization and demonstrating flexibility, as well as clarity

of delivered message may facilitate the implementation of

intelligent remote measurement technologies.

perspectives of digital technology are contactless rhythm

monitoring for the assessment of sudden cardiac arrest risk and

mass AF screening on ambulatory basis.

Video plethysmography correlates with contact PPG . The first

study demonstrated the feasibility of AF detection with a high

accuracy in a group of patients with a single camera [19].

Moreover, there is technology for accurate detecting cardiac

arrest through identifying cardiac arrest-associated agonal

breathing instances using commodity smart devices [20].

Key takeaways

▪ The implementation of new technologies specially AI-enabled devices

determines rapid improvement of the algorithms of automated

interpretations of single-lead ECG and PPG.

▪ AI-enabled mECG device–based QTc monitoring, contactless rhythm

monitoring for mass AF screening and assessment of sudden cardiac

arrest risk are nowadays real eHealth perspectives.

▪ Early detection of AF using digital devices allows early non-invasive

management.

▪ For successful implementation of telecare technologies , raising digital

health literacy among patients is crucial.

▪ Clarifications on legal aspects regarding the collection or processing

of personal data is required for smooth digital health implementation.

▪ Implementing low-cost screening PPG Apps followed by confirmation

with patch ECGs might balance the cost/effectiveness scheme for

these new devices .

Conflict of interest: none

[1] Makri A. Bridging the digital divide in health care. Lancet. 2019;1(5):e204- e205.

[2] Han JK, Al-Khatib SM, Albert CM. Changes in the digital health landscape in cardiac

electrophysiology: A pre-and peri-pandemic COVID-19 era survey. Cardiovasc Digit Health J.

2020;2(1):55-62.

[3] Svennberg E, Tjong F, Goette A, Akoum N, Di Biaise L, Bordachar P, Boriani G et al. How to use

digital devices to detect and manage arrhythmias:an EHRA practical guide. Europace. 2022;00:1-27.

[4] Lippi G, Sanchis-Gomar F, Cervellin G. Global epidemiology of atrial fibrillation: An increasing

epidemic and public health challenge. Int J Stroke. 2021;16(2):217-21.

[5] Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomström-Lundqvist C, Boriani G et al. 2020

ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with

the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and

management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the

special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J.

2021;42(5):373-498

[6] Kirchhof P, Camm AJ, Goette A, Brandes A, Eckardt L, Elvan A, Fetsch T et al. Early rhythm-

control therapy in patients with atrial fibrillation. N Engl J Med. 2020;383(14):1305-16.

[7] Svennberg E, Friberg L, Frykman V, Al-Khalili F, Engdahl J, Rosenqvist M. Clinical outcomes in

systematic screening for atrial fibrillation (STROKESTOP): a multicentre, parallel group, unmasked,

randomised controlled trial. Lancet. 2021;398(10310):1498-1506.

[8] Wang YC, Xu X, Hajra A, Apple S, Kharawala A, Duarte G, Liaqat W et al. Current Advancement

in diagnosing atrial fibrillation by utilizing wearable devices and artificial intelligence: A review study.

Diagnostics (Basel). 2022;12(3):689.

[9] Pluymaekers NAHA, van der Velden RMJ, Hermans ANL, Gawalko M, Buskes S, Keijenberg JJHMW,

Vorstermans B et al. On-demand mobile health infrastructure for remote rhythm monitoring within a

wait-and-see strategy for recent-onset Atrial Fibrillation: TeleWAS-AF. Cardiology 2021;146:392-96.

[10] Pluymaekers NAHA, Dudink EAMP, Luermans JGLM, Meeder JG, Lenderink T, Widdershoven J,

Bucx JJJ et al. Early or delayed cardioversion in recent-onset atrial fibrillation. N Engl J Med.

2019;380(16):1499-1508.

References

The place of digital devices and artificial intelligence in cardiac arrhythmia management: New advances, practical guides and promising prospects

Citation: Chernikova D, Mohamed Mohamed MA. The place of digital devices and artificial intelligence in cardiac arrhythmia

reserved. Submit your manuscript: www.jmedicalresearch.com

[11] Hickey KT, Hauser NR, Valente LE, Riga TC, Frulla AP, Masterson Creber R, Whang W et al. A single

center randomized, controlled trial investigating the efficacy of a mHealth ECG technology intervention

to improve the detection of atrial fibrillation: the iHEART study protocol. BMC Cardiovasc Disord.

2016;16:152.

[12] Lambert CT, Patel D, Bumgarner JM, Kanj M, Cantillon D, Saliba W, Hussein A et al. Atrial fibrillation

future clinic. Novel platform to integrate smart device electrocardiogram into clinical practice. Cardiovasc

Digit Health J. 2021;2(2):92-100.

[13] Giudicessi JR, Schram M, Bos JM, Galloway CD, Shreibati JB, Johnson PW, Carter RE et al. Artificial

intelligence-enabled assessment of the heart rate corrected QT interval using a mobile

electrocardiogram device. Circulation. 2021;143(13):1274-86.

[14] Giudicessi JR, Noseworthy PA, Ackerman MJ. The QT Interval. Circulation. 2019;139(24):2711-13.

[15] Giudicessi JR, Roden DM, Wilde AAM, Ackerman MJ. Classification and reporting of potentially

proarrhythmic common genetic variation in long QT syndrome genetic testing. Circulation.

2018;137:619-30.

[16] Strik M, Caillol T, Ramirez FD, Abu-Alrub S, Marchand H, Welte N, Ritter P et al. Validating QT-

interval measurement using the Apple Watch ECG to enable remote monitoring during the COVID-19

pandemic. Circulation. 2020;142(4):416-18.

[17] Kornej J, Börschel CS, Benjamin EJ, Schnabel RB. Epidemiology of atrial fibrillation in the 21st

Century: Novel Methods and New Insights. Circ Res. 2020 Jun 19;127(1):4-20.

[18] Simblett S, Greer B, Matcham F, Curtis H, Polhemus A, Ferrão J, Gamble P et al. Barriers to and

facilitators of engagement with remote measurement technology for managing health: Systematic

review and content analysis of findings. J Med Internet Res. 2018;20(7):e10480.

[19] Yan BP, Lai WHS, Chan CKY, Au ACK, Freedman B, Poh YC, Poh MZ. High-throughput, contact-free

detection of atrial fibrillation from video with deep learning. JAMA Cardiol. 2020;5(1):105-107.

[20] Chan J, Rea T, Gollakota S, Sunshine JE. Contactless cardiac arrest detection using smart devices.

NPJ Digit Med. 2019;2:52.