Clinical Validation of 5 Direct-to-Consumer Wearable Smart Devices to Detect Atrial Fibrillation

Mannhart, Diego; Lischer, Mirko; Knecht, Sven; Lavallaz, J Du Fay De; Strebel, Ivo; Serban, Teodor; Vögeli, David; Schaer, Beat; Oßwald, Stefan; Müeller, Christian; Kühne, Michael; Sticherling, Christian; Badertscher, Patrick

doi:10.1016/j.jacep.2022.09.011

Cited by 45 publications

(36 citation statements)

References 17 publications

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“…Of the physiological sensing devices included in validation studies (e.g., Xiaomi Miband 3, Apple Watch 4, Fitbit Charge 2, Garmin Vivosmart 3) consumer-grade wearables were significantly more accurate HR measurements at rest compared to research-grade wearables (i.e., Empatica E4, Biofotion Everion) when compared to a reference device (MAE: 7.2±5.4 bpm compared to 13.9±7.8 bpm [9]). The Apple Watch showed highest accuracy in several studies (Apple Watch 4 MAE: 4.4±2.7 bpm [9]; Apple Watch 6 ICC: 0.96 [56,59]) compared to other commercial devices. Despite this, we could [9]) in a list of commercially-available weareables.…”

Section: Physiological Measurement Devicesmentioning

confidence: 91%

“…Researchers generally consider consumer devices acceptable to use and prioritize the use of devices based on design and familiarity with the brand, rather than reliability, as well as comfort or ease of use [56]. In table 1, we highlight validation studies of commercially available physiological computing sensor systems and include studies in which measurements were taken from participants at rest.…”

Section: Physiological Measurement Devicesmentioning

confidence: 99%

“…PPG sensors, for instance, are susceptible to interference from natural and artificial light, and can be highly vulnerable to experimental errors, as movement can alter the way light is transmitted [82]. Users may also experience issues putting on wrist devices [56], which can result in light interference. In addition, a study comparing non-obtrusive physiological sensors for monitoring stress levels in computer users found that signal quality may also be susceptible to different pressures from sensors worn on the wrist while users perform typing tasks [2].…”

Section: Signal Quality Assessmentmentioning

confidence: 99%

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PhysioKit: Open-source, Low-cost Physiological Computing Toolkit for Single and Multi-user Studies

Joshi,

Wang,

Cho

2023

Preprint

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The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit consists of (i) a sensor and hardware layer that can be configured in a modular manner per research needs, (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study for photo-plethysmography (PPG) sensor with 15 participants, PhysioKit shows strong agreement with research-grade PPG sensors. Furthermore, we report usability survey results from 10 micro-project teams (44 individual members in total) who use PhysioKit for 4-6 weeks, providing insights into its use cases and research benefits. We conclude by discussing extensibility and potential impact of the toolkit on the research community.

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Section: Physiological Measurement Devicesmentioning

confidence: 91%

Section: Physiological Measurement Devicesmentioning

confidence: 99%

Section: Signal Quality Assessmentmentioning

confidence: 99%

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PhysioKit: Open-source, Low-cost Physiological Computing Toolkit for Single and Multi-user Studies

Joshi,

Wang,

Cho

2023

Preprint

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“…In fact, arrhythmia monitoring is among the most popular applications of wearable devices in medicine. However, wearable devices are limited in their ability to detect arrhythmias other than AF [ 6 , 29 ], particularly ventricular tachycardia or ventricular fibrillation, which is why wearable technologies capable of accurately detecting either ventricular tachycardia or ventricular fibrillation were limited in the literature.…”

Section: Cardiovascular Systemmentioning

confidence: 99%

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Neri

Oberdier

Abeelen

et al. 2023

Sensors

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Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

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“…The ZioPatch device uses an AI-powered algorithm to analyze continuous ECG recordings for up to 14 days and has been shown to improve the detection of arrhythmias and other cardiac events compared to conventional Holter monitoring [77,78]. The comparison for concrete use cases, such as to assess the accuracy of five direct-to-consumer wearable smart devices in identifying atrial fibrillation compared with a physician-interpreted 12-lead electrocardiogram with differences in the number of inconclusive tracings currently, diminishing the sensitivity and specificity of those wearables [79].…”

Section: Monitoring Of Ecg Signalsmentioning

confidence: 99%

Current and Future Use of Artificial Intelligence in Electrocardiography

Sellés

Marina-Breysse

2023

JCDD

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Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.

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Clinical Validation of 5 Direct-to-Consumer Wearable Smart Devices to Detect Atrial Fibrillation

Cited by 45 publications

References 17 publications

PhysioKit: Open-source, Low-cost Physiological Computing Toolkit for Single and Multi-user Studies

PhysioKit: Open-source, Low-cost Physiological Computing Toolkit for Single and Multi-user Studies

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Current and Future Use of Artificial Intelligence in Electrocardiography

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