Smartwatch Algorithm for Automated Detection of Atrial Fibrillation

Bumgarner, Joseph M.; Lambert, Cameron T.; Hussein, Ayman A.; Cantillon, Daniel J.; Baranowski, Bryan; Wolski, Kathy; Lindsay, Bruce D.; Wazni, Oussama; Tarakji, Khaldoun G.

doi:10.1016/j.jacc.2018.03.003

Cited by 384 publications

(300 citation statements)

References 13 publications

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“…Compared with 12 lead electrocardiograms interpreted by electrophysiologists, the automated algorithm was reported to have a sensitivity and specificity of 93% and 84%, respectively, in a small cross sectional study of patients with atrial fibrillation undergoing cardioversion (table 5). 68 It should be noted that about one third of the electrocardiograms were uninterpretable and excluded from this analysis.…”

Section: Newer Detection Methodsmentioning

confidence: 99%

Newer technologies for detection of atrial fibrillation

Zungsontiporn

Link

2018

BMJ

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Atrial fibrillation is a common arrhythmia that is associated with increased risk of stroke, which can be reduced with appropriate anticoagulation treatment. However, it remains underdiagnosed in contemporary clinical practice using conventional detection methods, resulting in missed opportunities to implement appropriate treatment. Newer technologies developed in recent years can potentially enhance the detection of atrial fibrillation and overcome certain limitations of the conventional methods. However, uncertainties remain about their use and the significance of atrial fibrillation detected by some of these newer technologies. This review examines the evidence supporting the use of some of these technologies and evaluates their applications in certain clinical scenarios.

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Section: Newer Detection Methodsmentioning

confidence: 99%

Newer technologies for detection of atrial fibrillation

Zungsontiporn

Link

2018

BMJ

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“…Automated algorithms have demonstrated excellent accuracy in interpreting single‐lead ECG when compared with contemporaneous 12‐lead ECG as the reference standard (Table ). However, between 15 and 33% of the traces were deemed unclassified by the automated algorithm, with baseline artefact being the primary reason for this classification . Clinicians were able to interpret recordings deemed unclassified by the device, with 100% sensitivity and 80% specificity .…”

Section: Sensitivity and Specificity Of Smart Device Platforms (Sdp)mentioning

confidence: 99%

Wearable devices for cardiac arrhythmia detection: a new contender?

Sajeev

Koshy

Teh

2019

Internal Medicine Journal

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There has been increased consumer uptake of smart devices and wearable technology. They facilitate non‐invasive, ambulatory assessment of numerous cardiac indices, including the heart rate and rhythm. Several studies have reported on the utility and deficiencies of these devices in identifying and monitoring cardiac arrhythmias. The rapid uptake of these consumer devices has the potential to generate vast amounts of biometric data. This coupled with gaps in knowledge pertaining to the optimal management of conditions such as sub clinical atrial fibrillation, may result in unnecessary and expensive downstream testing. An improved understanding of this nascent field by the clinician is vital.

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“…AliveCor's atrial fibrillation detection algorithm was shown to have a sensitivity and specificity of 96.6% and 94%, respectively, when used with the KardiaMobile . When used with the KardiaBand, the algorithm demonstrated a sensitivity and specificity of 93% and 84%, respectively . Another ECG example is the QardioCore .…”

Section: Four System Architectures Of Biosensors For Personal Mhealthmentioning

confidence: 99%

Biosensors for Personal Mobile Health: A System Architecture Perspective

Arumugam

Colburn

Sia

2019

Adv Materials Technologies

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Advances in mobile biosensors, integrating developments in materials science and instrumentation, are fueling an expansion in health data being collected and analyzed in decentralized settings. For example, semiconductor‐based sensors are enabling measurement of vital signs, and microfluidic‐based sensors are enabling measurement of biochemical markers. As biosensors for mobile health are becoming increasingly paired with smart devices, it can become critical for researchers to design biosensors—with appropriate functionalities and specifications—to work seamlessly with accompanying connected hardware and software. Here, recent research in biosensors, as well as current mobile health devices in use is described and classified into four distinct system architectures that take into account the biosensing and data processing functions required in personal mobile health devices. The path forward for integrating biosensors into smartphone‐based mobile health devices is also discussed.

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Smartwatch Algorithm for Automated Detection of Atrial Fibrillation

Abstract: The KB algorithm for AF detection supported by physician review can accurately differentiate AF from SR. This technology can help screen patients prior to elective CV and avoid unnecessary procedures.

Cited by 384 publications

References 13 publications

Newer technologies for detection of atrial fibrillation

Newer technologies for detection of atrial fibrillation

Wearable devices for cardiac arrhythmia detection: a new contender?

Biosensors for Personal Mobile Health: A System Architecture Perspective

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