Assessing the accuracy of an automated atrial fibrillation detection algorithm using smartphone technology: the iREAD study
Abstract
Background The Kardia Mobile Cardiac Monitor (KMCM) detects atrial fibrillation (AF) via a handheld cardiac rhythm recorder and AF detection algorithm. The algorithm operates within predefined parameters to provide a "normal" or "possible atrial fibrillation detected" interpretation; outside of these parameters, an "unclassified" rhythm is reported. The system has been increasingly used, but its performance has not been independently tested.Objective The objective of this study was to evaluate whether the KMCM system can accurately detect AF.Methods A single-center, adjudicator-blinded case series of 52 consecutive patients with AF admitted for antiarrhythmic drug initiation were enrolled. Serial 12-lead electrocardiograms (ECGs) and nearly simultaneously acquired KMCM recordings were obtained.Results There were 225 nearly simultaneously acquired KMCM and ECG recordings across 52 enrolled patients (mean age 68 years; 67% male). After exclusion of unclassified recordings, the KMCM automated algorithm interpretation had 96.6% sensitivity and 94.1% specificity for AF detection as compared with physician-interpreted ECGs, with a κ coefficient of 0.89. Physician-interpreted KMCM recordings had 100% sensitivity and 89.2% specificity for AF detection as compared with physician-interpreted ECGs, with a κ coefficient of 0.85. Sixty-two recordings (27.6%) were unclassified by the KMCM algorithm. In these instances, physician interpretation of KMCM recordings had 100% sensitivity and 79.5% specificity for AF detection as compared with 12-lead ECG interpretation, with a κ coefficient of 0.71.Conclusion The KMCM system provides sensitive and specific AF detection relative to 12-lead ECGs when an automated interpretation is provided. Direct physician review of KMCM recordings can enhance diagnostic yield, especially for unclassified recordings.
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The AliveCor automatic diagnostic algorithm has a sensitivity of 96.6% and specificity of 94% for correctly interpreting AF versus normal sinus rhythm.
“the automatic diagnostic algorithm has excellent sensitivity (96.6%) and specificity (94%) for correctly interpreting AF versus normal sinus rhythm [15].”
From Multi-centre Randomised Controlled Trial of a Smartphone-based Event Recorder Alongside Standard Care Versus Standard Care for Patients Presenting to the Emergency Department with Palpitations and Pre-syncope: The IPED (Investigation of Palpitations in the ED) study (2019) · cites this paper as [15]
Cites
2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation
January, C. T., Wann, L. S., Alpert, J. S., Calkins, H. G., Cigarroa, J. E., Cleveland, J. C., CONTI, J. B., Ellinor, P. T., Ezekowitz, M. D., Field, M. E., et al. (2014) · Circulation · doi:10.1161/cir.0000000000000041 · Cited by 7,119 · see citing works
Heart Disease and Stroke Statistics—2015 Update: A Report From the American Heart Association
Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., de Ferranti, S., Després, J.-P., Fullerton, H. J., Howard, V. J., et al. (2015) · Circulation · doi:10.1161/cir.0000000000000152 · Cited by 5,797 · see citing works
Worldwide Epidemiology of Atrial Fibrillation: A Global Burden of Disease 2010 Study
Chugh, S. S., Havmoeller, R., Narayanan, K., Singh, D., Rienstra, M., Benjamin, E. J., Gillum, R. F., Kim, Y.-H., McAnulty, J. H., Zheng, Z.-J., et al. (2014) · Circulation · doi:10.1161/circulationaha.113.005119 · Cited by 4,506 · see citing works
Calkins, H. G., Hindricks, G., Cappato, R., Kim, Y., Saad, E., Aguinaga, L., Akar, J. G., Badhwar, V., Brugada, J., Camm, A. J., et al. (2017) · Heart Rhythm · doi:10.1016/j.hrthm.2017.05.012 · Cited by 2,771 · see citing works
January, C. T., Wann, L. S., Alpert, J. S., Calkins, H., Cigarroa, J. E., Cleveland, J. C., Conti, J. B., Ellinor, P. T., Ezekowitz, M. D., Field, M. E., et al. (2014) · Circulation · doi:10.1161/cir.0000000000000040 · Cited by 2,056 · see citing works
Colilla, S., Crow, A., Petkun, W., Singer, D. E., Simon, T. A., & Liu, X. (2013) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2013.05.063 · Cited by 1,323 · see citing works
Wearable Devices as Facilitators, Not Drivers, of Health Behavior Change
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015) · JAMA · doi:10.1001/jama.2014.14781 · Cited by 808 · see citing works
Halcox, J. P. J., Wareham, K., Cardew, A., Gilmore, M., Barry, J. P., Phillips, C., & Gravenor, M. B. (2017) · Circulation · doi:10.1161/circulationaha.117.030583 · Cited by 580 · see citing works
Lowres, N., Neubeck, L., Salkeld, G., Krass, I., McLachlan, A. J., Redfern, J., Bennett, A. A., Briffa, T., Bauman, A., Martinez, C., et al. (2014) · Thrombosis and Haemostasis · doi:10.1160/th14-03-0231 · Cited by 458 · see citing works
Lau, J. K., Lowres, N., Neubeck, L., Brieger, D. B., Sy, R. W., Galloway, C. D., Albert, D. E., & Freedman, S. B. (2013) · International Journal of Cardiology · doi:10.1016/j.ijcard.2013.01.220 · Cited by 326 · see citing works
Powell, A. C., Landman, A., & Bates, D. W. (2014) · JAMA · doi:10.1001/jama.2014.2564 · Cited by 236 · see citing works
Tarakji, K. G., Wazni, O. M., Callahan, T. D., Kanj, M. H., Hakim, A. H., Wolski, K., Wilkoff, B. L., Saliba, W. I., & Lindsay, B. D. (2014) · Heart Rhythm · doi:10.1016/j.hrthm.2014.11.015 · Cited by 138 · see citing works
The Outlook of Digital Health for Cardiovascular Medicine
Turakhia, M. P., Desai, S. A., & Harrington, R. A. (2016) · JAMA Cardiology · doi:10.1001/jamacardio.2016.2661 · Cited by 56 · see citing works
Flagged first, then by citations
1 of the 14 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Hindricks, G., Potpara, T., Dagres, N., Arbelo, E., Bax, J., Blomström-Lundqvist, C., Boriani, G., Castellá, M., Dan, G., Dilaveris, P. E., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehaa612 · Cited by 2,605 · see citing works
Smartwatch Performance for the Detection and Quantification of Atrial Fibrillation
Wasserlauf, J., You, C., Patel, R., Valys, A., Albert, D., & Passman, R. (2019) · Circulation: Arrhythmia and Electrophysiology · doi:10.1161/circep.118.006834 · Cited by 245 · see citing works
How useful is the smartwatch ECG?
Isakadze, N., & Martin, S. S. (2019) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2019.10.010 · Cited by 234 · see citing works
Clinical Validation of 5 Direct-to-Consumer Wearable Smart Devices to Detect Atrial Fibrillation
Mannhart, D., Lischer, M., Knecht, S., du Fay de Lavallaz, J., Strebel, I., Serban, T., Vögeli, D., Schaer, B., Osswald, S., Mueller, C., et al. (2023) · JACC: Clinical Electrophysiology · doi:10.1016/j.jacep.2022.09.011 · Cited by 175 · see citing works
Windows into human health through wearables data analytics
Witt, D. R., Kellogg, R., Snyder, M. P., & Dunn, J. (2019) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2019.01.001 · Cited by 160 · see citing works
Multi-task deep learning for cardiac rhythm detection in wearable devices
Soto, J. T., & Ashley, E. A. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00320-4 · Cited by 148 · see citing works
Artificial intelligence in the diagnosis and management of arrhythmias
Nagarajan, V. D., Su-Lin, L., Robertus, J. L., Nienaber, C. A., Trayanova, N. A., & Ernst, S. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab544 · Cited by 139 · see citing works
The Telehealth Ten: A Guide for a Patient-Assisted Virtual Physical Examination
Benziger, C. P., Huffman, M. D., Sweis, R. N., & Stone, N. J. (2020) · The American Journal of Medicine · doi:10.1016/j.amjmed.2020.06.015 · Cited by 128 · see citing works
“Ask if patients can show you the output from an Apple Watch (heart rates and electrocardiogram) or similar smart phone technology, including AliveCor from Kardia.(10,11) This may be displayed via the monitor during the visit or uploaded by the patient to his/her chart prior to the visit.”
BackgroundRajakariar, K., Koshy, A. N., Sajeev, J., Nair, S., Roberts, L., & Teh, A. W. (2020) · Heart · doi:10.1136/heartjnl-2019-316004 · Cited by 110 · see citing works
Wearables in cardiology: Here to stay
Dagher, L., Shi, H., Zhao, Y., & Marrouche, N. F. (2020) · Heart Rhythm · doi:10.1016/j.hrthm.2020.02.023 · Cited by 107 · see citing works
Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review
Giebel, G. D., & Gissel, C. (2019) · JMIR mHealth and uHealth · doi:10.2196/13641 · Cited by 99 · see citing works
“In a study assessing the accuracy of AliveCor in patients with a history of either paroxysmal or persistent atrial fibrillation, William et al [56] found a very high accuracy in the form of sensitivity and specificity.”
BackgroundMethodInfluentialAccuracy of Smartphone Camera Applications for Detecting Atrial Fibrillation
O’Sullivan, J. W., Grigg, S. E., Crawford, W., Turakhia, M. P., Perez, M. V., Ingelsson, E., Wheeler, M. T., Ioannidis, J. P. A., & Ashley, E. A. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.2064 · Cited by 96 · see citing works
Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine
Iqbal, J., Jaimes, D. C. C., Makineni, P., Subramani, S., Hemaida, S., Thugu, T. R., Butt, A. N., Sikto, J. T., Kaur, P., Lak, M. A., et al. (2023) · Cureus · doi:10.7759/cureus.44658 · Cited by 109 · see citing works
“This has been successful, as it has been tested in electronic medical records (EMRs) [6].”
BackgroundMobile health applications for the detection of atrial fibrillation: a systematic review
Lopez Perales, C. R., Van Spall, H. G. C., Maeda, S., Jimenez, A., Laţcu, D. G., Milman, A., Kirakoya-Samadoulougou, F., Mamas, M. A., Muser, D., & Casado Arroyo, R. (2021) · EP Europace · doi:10.1093/europace/euaa139 · Cited by 77 · see citing works
“In one study assessing Kardia Mobile, 28% of recordings were unclassified by the algorithm.(24) Of these unclassified recordings, only 8% were non-interpretable by the physician.”
BackgroundMethodEmerging Technologies for Identifying Atrial Fibrillation
Ding, E., Marcus, G. M., & McManus, D. D. (2020) · Circulation Research · doi:10.1161/circresaha.119.316342 · Cited by 77 · see citing works
Wegner, F. K., Kochhäuser, S., Ellermann, C., Lange, P. S., Frommeyer, G., Leitz, P. R., Eckardt, L., & Dechering, D. G. (2019) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2019.11.018 · Cited by 75 · see citing works
Himmelreich, J. C. L., Karregat, E. P. M., Lucassen, W. A. M., van Weert, H. C. P. M., de Groot, J. R., Handoko, M. L., Nijveldt, R., & Harskamp, R. E. (2019) · The Annals of Family Medicine · doi:10.1370/afm.2438 · Cited by 71 · see citing works
Use of Flecainide for the Treatment of Atrial Fibrillation
Echt, D. S., & Ruskin, J. N. (2020) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2019.12.041 · Cited by 71 · see citing works
Machine learning in the detection and management of atrial fibrillation
Wegner, F. K., Plagwitz, L., Doldi, F., Ellermann, C., Willy, K., Wolfes, J., Sandmann, S., Varghese, J., & Eckardt, L. (2022) · Clinical Research in Cardiology · doi:10.1007/s00392-022-02012-3 · Cited by 67 · see citing works
“While smartphone‑based ECG devices have been devel‑ oped by multiple companies [14], the AliveCor Kardia AF detection algorithm has been evaluated most frequently by scientific studies and was shown to have a high negative pre‑ dictive value for the presence of AF [15–22].”
MethodLai, D., Bu, Y., Su, Y., Zhang, X., & Ma, C. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2980454 · Cited by 65 · see citing works
“sensitivity, and specificity also would strongly demonstrate the efficacy and robustness of the proposed automatic AF detection system, by contrast with the incomplete performance reported in patients [4], [8], [13] and in health subjects [14].”
BackgroundMethodResultInfluentialPrasitlumkum, N., Cheungpasitporn, W., Chokesuwattanaskul, A., Thangjui, S., Thongprayoon, C., Bathini, T. J., Vallabhajosyula, S., Kanitsoraphan, C., Leesutipornchai, T., & Chokesuwattanaskul, R. (2020) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2020.05.015 · Cited by 68 · see citing works
JCS/JHRS 2022 Guideline on Diagnosis and Risk Assessment of Arrhythmia
Takase, B., Ikeda, T., Shimizu, W., Abe, H., Aiba, T., Chinushi, M., Koba, S., KUSANO, K. F., Niwano, S., Takahashi, N., et al. (2023) · Circulation Journal · doi:10.1253/circj.cj-22-0827 · Cited by 67 · see citing works
Wearing Your Heart on Your Sleeve: the Future of Cardiac Rhythm Monitoring
Al‐Alusi, M. A., Ding, E., McManus, D. D., & Lubitz, S. A. (2019) · Current Cardiology Reports · doi:10.1007/s11886-019-1223-8 · Cited by 59 · see citing works
Reed, M. J., Grubb, N. R., Lang, C. C., O'Brien, R., Simpson, K., Padarenga, M., Grant, A., Tuck, S., Keating, L., Coffey, F., et al. (2019) · EClinicalMedicine · doi:10.1016/j.eclinm.2019.02.005 · Cited by 57 · see citing works
“6%) and specificity (94%) for correctly interpreting AF versus normal sinus rhythm [15].”
BackgroundUse of a Smart Watch for Early Detection of Paroxysmal Atrial Fibrillation: Validation Study
Inui, T., Kohno, H., Kawasaki, Y., Matsuura, K., Ueda, H., Tamura, Y., Watanabe, M., Inage, Y., Yakita, Y., Wakabayashi, Y., et al. (2019) · JMIR Cardio · doi:10.2196/14857 · Cited by 60 · see citing works
“By combining those technologies with the rapidly evolving telemedical services and artificial intelligence technology and the emergence of direct oral anticoagulants that do not require routine blood monitoring, public health care may enter a new era encompassing efficiency and efficacy, particularly with regard to st…”
BackgroundIsmail, S. N. M. S., Aziz, N. A. A., & Ibrahim, S. Z. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.04.012 · Cited by 54 · see citing works
Assessment of signal quality measured with a smart 12‐lead ECG acquisition T‐shirt
Fouassier, D., Roy, X., Blanchard, A., & Hulot, J. (2019) · Annals of Noninvasive Electrocardiology · doi:10.1111/anec.12682 · Cited by 52 · see citing works
“…inner electrodes) to identify irregular beats as a potential marker of cardiac arrhythmias and then send the information through wireless trans‐ mitters such as smartphones (Bruce et al., 2016; Gropler, Dalal, Hare, & Silva, 2018; Tabing, Harrell, Romero, & Francisco, 2017; William et al., 2018).”
BackgroundConnected Health Technology for Cardiovascular Disease Prevention and Management
Wongvibulsin, S., Martin, S. S., Steinhubl, S. R., & Muse, E. D. (2019) · Current Treatment Options in Cardiovascular Medicine · doi:10.1007/s11936-019-0729-0 · Cited by 50 · see citing works
Manninger, M., Zweiker, D., Svennberg, E., Chatzikyriakou, S. V., Pavlović, N., Zaman, J. A. B., Kirćanski, B., Lenarczyk, R., Vanduynhoven, P., Kosiuk, J., et al. (2021) · EP Europace · doi:10.1093/europace/euab064 · Cited by 49 · see citing works
“76–95% in single-lead ECG).(2,8,9) However, the use of PPG tracings for diagnosing AF needs more evidence, hence is not recommended in the recent AF guidelines.”
BackgroundInfluentialHernandez, M. F., & Rodríguez, F. (2022) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-022-00711-0 · Cited by 49 · see citing works
Biosensors for Personal Mobile Health: A System Architecture Perspective
Arumugam, S., Colburn, D. A. M., & Sia, S. K. (2019) · Advanced Materials Technologies · doi:10.1002/admt.201900720 · Cited by 49 · see citing works
Wong, K. C., Klimis, H., Lowres, N., Huben, A. V., Marschner, S. L., & Chow, C. K. (2020) · Heart · doi:10.1136/heartjnl-2020-316611 · Cited by 49 · see citing works
Ford, C., Xie, C. X., Low, A., Rajakariar, K., Koshy, A. N., Sajeev, J., Roberts, L., Pathik, B., & Teh, A. W. (2022) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2022.02.013 · Cited by 50 · see citing works
Hall, A., Mitchell, A. R. J., Wood, L., & Holland, C. (2020) · Medicine · doi:10.1097/md.0000000000021388 · Cited by 47 · see citing works
“”([60]) Brasier et al also report a number of “unclassified” ECGs and once removed, resulted in optimal sensitivity and specificity.”
BackgroundResearch on digital technology use in cardiology: bibliometric analysis
Yeung, A. W. K., Kulnik, S. T., Parvanov, E. D., Fassl, A., Eibensteiner, F., Völkl-Kernstock, S., Kletecka-Pulker, M., Crutzen, R., Gutenberg, J., Höppchen, I., et al. (2022) · Journal of Medical Internet Research · doi:10.2196/36086 · Cited by 44 · see citing works
“Moreover, artificial intelligence could interpret ECGs rapidly with human-like performance and even detect signals and patterns largely unrecognizable by humans [105].”
BackgroundInfluentialChang, P., Wen, M., Chou, C., Wang, C., & Hung, K. (2022) · American Heart Journal · doi:10.1016/j.ahj.2022.02.002 · Cited by 46 · see citing works
Manetas-Stavrakakis, N., Sotiropoulou, I. M., Paraskevas, T., Stavrakaki, S. M., Bampatsias, D., Xanthopoulos, A. V., Παπαγεωργίου, Ν., & Briasoulis, Α. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12206576 · Cited by 45 · see citing works
Carrington, M., Providencia, R. A., Chahal, C. A. A., Ricci, F., Epstein, A. E., Gallina, S., Fedorowski, A., Sutton, R., & Khanji, M. Y. (2022) · European Heart Journal Open · doi:10.1093/ehjopen/oeac072 · Cited by 43 · see citing works
“As previously stated, false-negative rates in the diagnosis of AF are negligible.(16) Smartwatches also have PPG sensors incorporated in their case, on the side that is in contact with the wrist.”
Ding, E., Svennberg, E., Wurster, C. J., Duncker, D., Manninger, M., Lubitz, S. A., Dickson, E. L., Fitzgibbons, T. P., AKOUM, N. W., Al‐Khatib, S. M., et al. (2020) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2020.06.002 · Cited by 41 · see citing works
Godin, R., Yeung, C., Baranchuk, A. M., Guerra, P. G., & Healey, J. S. (2019) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2019.03.024 · Cited by 39 · see citing works
Feasibility of Atrial Fibrillation Screening With Mobile Health Technologies at Pharmacies
Zaprutko, T., Zaprutko, J., Baszko, A., Sawicka, D., Szałek-Goralewska, A., Dymecka, M., Telec, W., Kopciuch, D., Ratajczak, P., Michalak, M., et al. (2019) · Journal of Cardiovascular Pharmacology and Therapeutics · doi:10.1177/1074248419879089 · Cited by 36 · see citing works
“1%).(3,15,21) Differentiation of sensitivity values between various studies may result from including or excluding “unreadable” and “unclassified” recordings.”
BackgroundDigital Health and the Care of the Patient With Arrhythmia
Tarakji, K. G., Silva, J. N. A., Chen, L. Y., Turakhia, M. P., Perez, M. V., Attia, Z. I., PASSMAN, R. S., Boissy, A. R., Cho, D. J., Majmudar, M. D., et al. (2020) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.120.007953 · Cited by 34 · see citing works
“15 Even when the results are accurate for diagnosing AF, the management should be tailored and cannot be universal.”
BackgroundThe role of timing in treatment of atrial fibrillation: An AFFIRM substudy
Yang, E., Tang, O., Metkus, T. S., Berger, R. D., SPRAGG, D. D., Calkins, H. G., & Marine, J. E. (2020) · Heart Rhythm · doi:10.1016/j.hrthm.2020.12.025 · Cited by 34 · see citing works
Mariani, M. V., Pierucci, N., Forleo, G. B., Schiavone, M., Bernardini, A., Gasperetti, A., Mitacchione, G., Mei, M., Giunta, G., Piro, A., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12020620 · Cited by 33 · see citing works
Yang, T. Y., Huang, L. T., Malwade, S., Hsu, C., & Chen, Y. (2021) · JMIR mhealth and uhealth · doi:10.2196/26167 · Cited by 33 · see citing works
“regarded atrial flutter as the same disease state as AF, viewing the incident as a positive AF result [16,19,20,24,26-29].”
BackgroundMethodResultInfluentialBorrelli, N., Grimaldi, N., Papaccioli, G., Fusco, F., Palma, M., & Sarubbi, B. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20105775 · Cited by 33 · see citing works
(2023) · Australian Prescriber · doi:10.18773/austprescr · Cited by 30 · see citing works
Necklace‐embedded electrocardiogram for the detection and diagnosis of atrial fibrillation
Santala, O. E., Lipponen, J. A., Jäntti, H., Rissanen, T. T., Halonen, J., Kolk, I., Pohjantähti‐Maaroos, H., Tarvainen, M. P., Väliaho, E., Hartikainen, J. E. K., et al. (2021) · Clinical Cardiology · doi:10.1002/clc.23580 · Cited by 29 · see citing works
“This also corroborates earlier studies using handheld devices; these have reported AF-detection sensitivities ranging from 77% to 100% and specificities from 80% to 96% when interpreted by the physicians and sensitivities of 55%–100% and specificities of 84%–98% when the interpretation was performed using automated al…”
ResultCHARGE-AF: A Useful Score For Atrial Fibrillation Prediction?
Goudis, C. A., Daios, S., Dimitriadis, F., & Liu, T. (2022) · Current Cardiology Reviews · doi:10.2174/1573403x18666220901102557 · Cited by 28 · see citing works
“Kardia Mobile Cardiac Monitor (KMCM) utilizes AF detection algorithms and a handheld cardiac rhythm recorder to detect AF [22].”
BackgroundMethodInfluentialBabar, F., Cheema, A. M., Ahmad, Z., Sarfraz, A., Sarfraz, Z., Ashraff, H., Tayyab, H., Bajwa, A. M., Ali, H., Sarfraz, M., et al. (2023) · Current Cardiology Reports · doi:10.1007/s11886-023-01898-3 · Cited by 31 · see citing works
Hindricks, G., Potpara, T. S., Dagres, N., Arbelo, E., Bax, J. J., Blomström-Lundqvist, C., Boriani, G., Castellá, M., Dan, G., Dilaveris, P. E., et al. (2021) · Revista Española de Cardiología · doi:10.1016/j.recesp.2020.10.022 · Cited by 24 · see citing works
Ambulatory ECG monitoring in the age of smartphones
Sanders, D. J., Ungar, L. D. J., Eskander, M., & Seto, A. H. (2019) · Cleveland Clinic Journal of Medicine · doi:10.3949/ccjm.86a.18123 · Cited by 26 · see citing works
State of the art of mobile health technologies use in clinical arrhythmia care
Sridhar, A. R. M., Cheung, J. W., Lampert, R., Silva, J. N. A., Gopinathannair, R., Sotomonte, J. C., Tarakji, K. G., Fellman, M., Chrispin, J., Varma, N., et al. (2024) · Communications Medicine · doi:10.1038/s43856-024-00618-4 · Cited by 28 · see citing works
Lambert, C. T., Patel, D. R., Bumgarner, J. M., Kanj, M. H., Cantillon, D. J., Saliba, W. I., Hussein, A. A., Baranowski, B. J., Dresing, T., Chung, M. K., et al. (2021) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2021.02.002 · Cited by 25 · see citing works
Zhang, H., Dong, Z., Sun, M., Gu, H., & Wang, Z. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106358 · Cited by 25 · see citing works
Utsha, U. T., & Morshed, B. I. (2024) · Smart Health · doi:10.1016/j.smhl.2024.100446 · Cited by 26 · see citing works
Mannhart, D., Lefebvre, B., Gardella, C., Henry, C., Serban, T., Knecht, S., Kühne, M., Sticherling, C., & Badertscher, P. (2023) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2023.04.003 · Cited by 23 · see citing works
Barrio-López, M. T., Ruiz‐Canela, M., Ramos, P., Tercedor, L., Ibáñez, J. L., Ortiz, M., Goñi, L., Ibáñez-Críado, A., Ruiz, R. M., García‐Bolao, I., et al. (2019) · American Heart Journal · doi:10.1016/j.ahj.2019.10.009 · Cited by 23 · see citing works
The Digital Reconstruction of Health Care
Halamka, J. D., & Cerrato, P. L. (2020) · NEJM Catalyst · doi:10.1056/cat.20.0082 · Cited by 22 · see citing works
Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review
Zepeda-Echavarria, A., van de Leur, R. R., van Sleuwen, M., Hassink, R. J., Wildbergh, T. X., Doevendans, P. A., Jaspers, J., & van Es, R. (2023) · JMIR Cardio · doi:10.2196/44003 · Cited by 24 · see citing works
“Of the 36 articles, 24 (66%) evaluated the devices’ capabilities to diagnose rhythm disorders [15-17,21,24,25,29-35, 37-43,49-56], whereas 7 (20%) reported on capabilities to detect rhythm disorders and other heart conditions, such as cardiomyopathy, conduction disorders, and cardiac ischemia [18,22,23,28,36,45,57].”
BackgroundComparison of Apple Watch vs KardiaMobile: A Tale of Two Devices
Lee, C., Lee, C., Fernando, C., & Chow, C.-M. (2022) · CJC Open · doi:10.1016/j.cjco.2022.07.011 · Cited by 20 · see citing works
Klier, K., Koch, L., Graf, L., Schinköthe, T., & Schmidt, A. (2023) · JMIR Cardio · doi:10.2196/50701 · Cited by 26 · see citing works
“The sensitivity of the detection of atrial fibrillation ranges from 54.5% to 100%, with most studies calculating values of >87% [36,37].”
BackgroundInfluentialUse of virtual visits for the care of the arrhythmia patient
Hu, P. T., Hilow, H., Patel, D. R., Eppich, M., Cantillon, D. J., TCHOU, P. J., Bhargava, M., Kanj, M. H., Baranowski, B. J., Hussein, A. A., et al. (2020) · Heart Rhythm · doi:10.1016/j.hrthm.2020.05.011 · Cited by 19 · see citing works
Technology and cardiovascular diseases in the era of COVID‐19
Harky, A., Adan, A., Mohamed, M., Elmi, A. A., & Theologou, T. (2020) · Journal of Cardiac Surgery · doi:10.1111/jocs.15096 · Cited by 18 · see citing works
“A study which assessed the accuracy of smartphone technology in detecting atrial fibrillation (AF) with physician interpretation, using a kardia mobile cardiac monitor, found a 96.6% sensitivity and 94.1% specificity for AF detection compared with physician interpreted ECGs.24 Due to the nature of AF meaning that it c…”
BackgroundInfluentialReliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions
Jacobsen, M., Dembek, T. A., Ziakos, A. P., Gholamipoor, R., Kobbe, G., Kollmann, M., Blum, C. F., Müller‐Wieland, D., Napp, A., Heinemann, L., et al. (2020) · Sensors · doi:10.3390/s20195517 · Cited by 18 · see citing works
“[19,20] From the data obtained, the percentage of ‘good quality data’ was assessed by aggregating the time periods during which data were available that enabled an automatic IBI analysis.”
MethodReed, M. J., Grubb, N. R., Lang, C. C., O’Brien, R., Simpson, K., Padarenga, M., Grant, A., & Tuck, S. (2018) · Trials · doi:10.1186/s13063-018-3098-1 · Cited by 17 · see citing works
A Review on Atrial Fibrillation Detection From Ambulatory ECG
Ma, C., Xiao, Z., Zhao, L., Biton, S. B., Behar, J. A., Long, X., Vullings, R., Aarts, R. M., Li, J., & Liu, C. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3321792 · Cited by 20 · see citing works
“William et al. [125] monitored 233 patients using the AliveCor Kardia Mobile (KM) and its algorithm.”
MethodJCS / JHRS 2022 Guideline on Diagnosis and Risk Assessment of Arrhythmia
Takase, B., Ikeda, T., Shimizu, W., Abe, H., Aiba, T., Chinushi, M., Koba, S., KUSANO, K. F., Niwano, S., Takahashi, N., et al. (2024) · Journal of Arrhythmia · doi:10.1002/joa3.13052 · Cited by 20 · see citing works
Patel, D. R., Hu, P. T., Hilow, H., Lambert, C. T., Moufawad, M., Poe, S. M., Hussein, A. A., Baranowski, B. J., Bhargava, M., Rickard, J., et al. (2019) · Pacing and Clinical Electrophysiology · doi:10.1111/pace.13850 · Cited by 16 · see citing works
Garikapati, K., Turnbull, S., Bennett, R. G., Campbell, T. G., Kanawati, J., Wong, M. S., Thomas, S. P., Chow, C. K., & Kumar, S. (2022) · Heart Lung and Circulation · doi:10.1016/j.hlc.2022.08.001 · Cited by 17 · see citing works
Senoo, K., Miki, T., Okura, T., Shiraishi, H., Shirayama, T., Inoue, K., Sakatani, T., Kakita, K., Hattori, T., Nakai, K., et al. (2020) · Circulation Reports · doi:10.1253/circrep.cr-20-0032 · Cited by 16 · see citing works
Wearable Technology in Healthcare
Krey, M., Schlatter, U., Mahadevan, D. A., Derungs, K., & Oehler, J. K. (2020) · Journal of Advances in Information Technology · doi:10.12720/jait.11.3.172-180 · Cited by 16 · see citing works
“1% was measured compared to a 12-lead electrocardiogram [32].”
MethodA comparison of ECG-based home monitoring devices in adults with CHD
Pengel, L. K. D., Robbers‐Visser, D., Groenink, M., Winter, M. M., Schuuring, M. J., Bouma, B. J., & Bokma, J. P. (2022) · Cardiology in the Young · doi:10.1017/s1047951122002244 · Cited by 15 · see citing works
Wegner, F. K., Kochhäuser, S., Frommeyer, G., Lange, P. S., Ellermann, C., Leitz, P. R., Müller, P., Köbe, J., Eckardt, L., & Dechering, D. G. (2021) · Clinical Research in Cardiology · doi:10.1007/s00392-021-01856-5 · Cited by 15 · see citing works
Pitman, B. M., Chew, S.-H., Wong, C. X. J., Jaghoori, A., Iwai, S., Thomas, G., Chew, A., Sanders, P., & Lau, D. H. (2021) · JMIR mhealth and uhealth · doi:10.2196/24470 · Cited by 14 · see citing works
“Similarly, others have reported the KardiaMobile automated algorithm correctly detected AF with 93% sensitivity and 84% specificity in 100 participants with a history of AF who presented for a scheduled elective electrical cardioversion after excluding a substantial 34% of recordings with unclassified tracings [11].”
Enhancing Healthcare Access–Smartphone Apps in Arrhythmia Screening: Viewpoint
Książczyk, M., Dębska-Kozłowska, A., Warchoł, I., & Lubiński, A. (2021) · JMIR mhealth and uhealth · doi:10.2196/23425 · Cited by 14 · see citing works
Management of patients with arterial hypertension and atrial fibrillation
Chazova, I. E., Голицын, С. П., Zhernakova, Y. V., Zheleznova, E. A., Станиславовна, К. Е., Миронов, Н. Ю., Kostyukevich, M. V., Laiovich, L. I., Utsumueva, M. D., Александровна, Ю. Ю., et al. (2021) · Systemic Hypertension · doi:10.26442/2075082x.2021.3.201077 · Cited by 13 · see citing works
Atrial fibrillation: an update on management
McCallum, C., Raja, D. C., & Pathak, R. K. (2019) · Australian Prescriber · doi:10.18773/austprescr.2019.067 · Cited by 13 · see citing works
Moldt, J.-A., Wietek, T. F., Fuhl, W., Zabel, S., Claassen, M., Wagner, S., Nieselt, K., & Herrmann‐Werner, A. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-096208 · Cited by 11 · see citing works
Rosenfeld, L. E., Amin, A., Hsu, J. C., Oxner, A. Z., Hills, M. T., & Frankel, D. S. (2019) · Heart Rhythm · doi:10.1016/j.hrthm.2019.04.007 · Cited by 12 · see citing works
LUCIA, R. D., Giannini, C., Parollo, M., Barletta, V., Costa, G., Santoro, M. G., Primerano, C., Angelillis, M., Carlo, M. D., Zucchelli, G., et al. (2023) · EP Europace · doi:10.1093/europace/euac285 · Cited by 11 · see citing works
“6% and a specificity of 94% for AF detection, and, if compared with other MCT devices, it has a lower cost, could be easily reused, has a good battery autonomy, and is not burdened with the durability of the adhesives or the need of frequent electrode changes.(23) Finally, peculiar ‘digital’ findings of our novel AeEC…”
BackgroundA Morphology-Preserving Algorithm for Denoising of EMG-Contaminated ECG Signals
Atanasoski, V. A., Petrovic, J. S., Popović‐Maneski, L., Miletić, M., Babić, M., Nikolić, A., Panescu, D., & Ivanović, M. D. (2024) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2024.3380352 · Cited by 11 · see citing works
“However, the underlying physiology of the EMG noise, resulting from the engagement of fingers during measurement by handheld devices [1], [3], does not allow for an obvious mitigation strategy and must be eliminated in postprocessing.”
BackgroundMethodTonko, J., & Wright, M. J. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10173922 · Cited by 12 · see citing works
“However, overall diagnostic accuracy and performance of automated algorithms appear to be acceptable [13].”
BackgroundNath, L. C., Forbes, G., Elliott, A. D., Tomren, V., Ryan, A., & Franklin, S. H. (2021) · Australian Veterinary Journal · doi:10.1111/avj.13137 · Cited by 11 · see citing works
Sattar, Y., Song, D. D., Sarvepalli, D., Zaidi, S. R., Ullah, W., Arshad, J., Mir, T., Zghouzi, M., Elgendy, I. Y., Qureshi, W., et al. (2021) · Journal of Interventional Cardiac Electrophysiology · doi:10.1007/s10840-021-01068-x · Cited by 10 · see citing works
“[42] found very high accuracy in the form of sensitivity and specificity.”
BackgroundPitman, B. M., Chew, S.-H., Wong, C. X. J., Jaghoori, A., Iwai, S., Lyrtzis, E., Lim, M. Y., Chew, R. R.-X., Chew, A., Sanders, P., et al. (2022) · Heart Rhythm O2 · doi:10.1016/j.hroo.2022.09.008 · Cited by 10 · see citing works
“Unspecified 65 (2) Religion Orthodox 786 (26) Protestant 2015 (67) Muslim 129 (4) Other religion or no religion 70 (2) Education Illiterate 160 (5) Primary level school 462 (15) Secondary level school 1028 (34) Certificate, diploma, or above 1324 (44) Unspecified 26 (1) Occupation Unemployed 84 (3) Employed (worker) 4…”
Weidlich, S., Mannhart, D., Kennedy, A., Doggart, P., Serban, T., Knecht, S., de Lavallaz, J. D. F., Kühne, M., Sticherling, C., & Badertscher, P. (2023) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2023.12.003 · Cited by 9 · see citing works
“The results of our study extend and corroborate previous work with smart devices 9,10,17,18 as well as neural networks 19,20 and con fi rm that the amount of inconclusive tracings is higher than initially reported.”
ResultFrancisco, A., Pascoal, C., Lamborne, P., Morais, H., & Gonçalves, M. A. D. A. (2025) · Cureus · doi:10.7759/cureus.77404 · Cited by 12 · see citing works
Consumer wearable technologies to identify and monitor exercise-related arrhythmias in athletes
Sanchis‐Gomar, F., Lavie, C. J., & Perez, M. V. (2020) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000817 · Cited by 9 · see citing works
Utsha, U. T., Tsai, I. H., & Morshed, B. I. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-45878-1_10 · Cited by 9 · see citing works
Stroke Prevention in Atrial Fibrillation
Gao, X., & PASSMAN, R. S. (2022) · Current Cardiology Reports · doi:10.1007/s11886-022-01787-1 · Cited by 8 · see citing works
Validation of diagnostic accuracy of a handheld, smartphone-based rhythm recording device
Paul‐Nordin, A., Carnlöf, C., Insulander, P., Ali, A. M., Jensen‐Urstad, M., Saluveer, O., & Drca, N. (2023) · Expert Review of Medical Devices · doi:10.1080/17434440.2023.2171290 · Cited by 8 · see citing works
Ikeda, T., Ashihara, T., Iwasaki, Y., Ono, M., Kagiyama, N., Kimura, T., KUSANO, K. F., Kohno, R., Saku, K., Sasano, T., et al. (2025) · Circulation Journal · doi:10.1253/circj.cj-24-0960 · Cited by 12 · see citing works
Smartphone and new tools for atrial fibrillation diagnosis: evidence for clinical applicability
PAPACCIOLI, G., BASSI, G., Cecilia, L. U. G. I., Parente, E., D’ANDREA, A., PROIETTI, R., Imbalzano, E., TURKI, A. A., & Russo, V. (2022) · Minerva Cardiology and Angiology · doi:10.23736/s2724-5683.22.05841-0 · Cited by 7 · see citing works
Is machine learning the future for atrial fibrillation screening?
Sivanandarajah, P., Wu, H., Bajaj, N., Khan, S. N., & Ng, F. S. (2022) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2022.04.001 · Cited by 7 · see citing works
Wearables in Sports Cardiology
Beavers, D. L., & Chung, E. H. (2022) · Clinics in Sports Medicine · doi:10.1016/j.csm.2022.02.004 · Cited by 7 · see citing works
Introduction of Wearable Device in Cardiovascular Field for Monitoring Arrhythmia
Kim, N. H., & Ko, J. S. (2021) · Chonnam Medical Journal · doi:10.4068/cmj.2021.57.1.1 · Cited by 7 · see citing works
Krey, M. (2020) · International Conference on Information Management · doi:10.1109/icim49319.2020.244711 · Cited by 7 · see citing works
“A sensitivity of 96.6% and a specificity of 94.1% was measured compared to a 12-lead electrocardiogram [32].”
MethodInfluentialGunawardhana, M., Kulathilaka, A., & Zhao, J.-C. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00324-7 · Cited by 10 · see citing works
Santala, O. E., Lipponen, J. A., Jäntti, H., Rissanen, T. T., Tarvainen, M. P., Väliaho, E., Rantula, O., Naukkarinen, N. S., Hartikainen, J. E. K., Martikainen, T. J., et al. (2023) · Cardiology in Review · doi:10.1097/crd.0000000000000526 · Cited by 8 · see citing works
“52,53 Traditional automatic AF detection algorithms are designed to detect AF during an arrhythmia episode.”
Method
- Authors
- William, A. D., Kanbour, M., Callahan, T., Bhargava, M., Varma, N., Rickard, J., Saliba, W., Wolski, K., Hussein, A., Lindsay, B. D., Wazni, O. M., & Tarakji, K. G.
- Affiliations
- Cleveland Clinic (US)
- Marshall University (US)
- Corresponding author
- Tarakji, Khaldoun G.
- ORCID
- Venue
- Heart Rhythm
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 15
- Issue
- 10
- Pages
- 1561-1565
- ISSN
- 1547-5271 (ISSN-L)
- OpenAlex
- W2888509599
- Cited by
- 154
- References
- 14
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 10 · 2023: 32 · 2022: 19 · 2021: 30 · 2020: 25 · 2019: 23 · 2018: 2
- FWCI
- 15.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Heart Rhythm (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- atrial fibrillation, diagnostic accuracy, sensitivity and specificity, 12-lead ECG
- MeSH terms
- Smartphone, Adult, Aged, Aged, 80 and over, Algorithms, Atrial Fibrillation, Electrocardiography, Female, Heart Conduction System, Humans, Male, Middle Aged, Predictive Value of Tests, Signal Processing, Computer-Assisted, Telemetry, Reproducibility of Results, Single-Blind Method, Mobile Applications
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