A new smart wristband equipped with an artificial intelligence algorithm to detect atrial fibrillation

Chen, Erdong; Jiang, Jie; Su, Rui; Gao, Meng; Zhu, Shijun; Jun, Zhou; Huo, Yong

doi:10.1016/j.hrthm.2020.01.034

Cited by 76 publications

(51 citation statements)

References 14 publications

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“…The assessment of bias using the Quality Assessment of Diagnostic Accuracy Studies 2 tool for the included studies is highlighted in Multimedia Appendix 2 [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31].…”

Section: Quality Assessmentmentioning

confidence: 99%

Diagnostic Accuracy of Smartwatches for the Detection of Cardiac Arrhythmia: Systematic Review and Meta-analysis

et al. 2021

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Background Significant morbidity, mortality, and financial burden are associated with cardiac rhythm abnormalities. Conventional investigative tools are often unsuccessful in detecting cardiac arrhythmias because of their episodic nature. Smartwatches have gained popularity in recent years as a health tool for the detection of cardiac rhythms. Objective This study aims to systematically review and meta-analyze the diagnostic accuracy of smartwatches in the detection of cardiac arrhythmias. Methods A systematic literature search of the Embase, MEDLINE, and Cochrane Library databases was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify studies reporting the use of a smartwatch for the detection of cardiac arrhythmia. Summary estimates of sensitivity, specificity, and area under the curve were attempted using a bivariate model for the diagnostic meta-analysis. Studies were examined for quality using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Results A total of 18 studies examining atrial fibrillation detection, bradyarrhythmias and tachyarrhythmias, and premature contractions were analyzed, measuring diagnostic accuracy in 424,371 subjects in total. The signals analyzed by smartwatches were based on photoplethysmography. The overall sensitivity, specificity, and accuracy of smartwatches for detecting cardiac arrhythmias were 100% (95% CI 0.99-1.00), 95% (95% CI 0.93-0.97), and 97% (95% CI 0.96-0.99), respectively. The pooled positive predictive value and negative predictive value for detecting cardiac arrhythmias were 85% (95% CI 0.79-0.90) and 100% (95% CI 1.0-1.0), respectively. Conclusions This review demonstrates the evolving field of digital disease detection. The current diagnostic accuracy of smartwatch technology for the detection of cardiac arrhythmias is high. Although the innovative drive of digital devices in health care will continue to gain momentum toward screening, the process of accurate evidence accrual and regulatory standards ready to accept their introduction is strongly needed. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020213237; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=213237.

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Section: Quality Assessmentmentioning

confidence: 99%

Diagnostic Accuracy of Smartwatches for the Detection of Cardiac Arrhythmia: Systematic Review and Meta-analysis

et al. 2021

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“…We hypothesize that one of the major limitations in measuring OSA with wearables is the detection of sleep hypopnea. Currently, most consumer-grade wearables can measure surrogate markers for breathing and heartbeats or heart rate variability [3,[17][18][19][20]. Although sleep apneas consisting of a complete pause in breathing for ≥10 seconds are relatively easy to assess by analyzing breathing frequency [6,12,[21][22][23], it is challenging to detect sleep hypopnea, which is defined as a ≥30% drop in airflow lasting ≥10 seconds accompanied by either an arousal or a ≥3% drop in peripheral capillary oxygen saturation (SpO 2 ) measured at the fingertip [12].…”

Section: Introductionmentioning

confidence: 99%

The Challenges and Pitfalls of Detecting Sleep Hypopnea Using a Wearable Optical Sensor: Comparative Study

Zhang¹,

Qi²,

Hügli³

et al. 2021

J Med Internet Res

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Background Obstructive sleep apnea (OSA) is the most prevalent respiratory sleep disorder occurring in 9% to 38% of the general population. About 90% of patients with suspected OSA remain undiagnosed due to the lack of sleep laboratories or specialists and the high cost of gold-standard in-lab polysomnography diagnosis, leading to a decreased quality of life and increased health care burden in cardio- and cerebrovascular diseases. Wearable sleep trackers like smartwatches and armbands are booming, creating a hope for cost-efficient at-home OSA diagnosis and assessment of treatment (eg, continuous positive airway pressure [CPAP] therapy) effectiveness. However, such wearables are currently still not available and cannot be used to detect sleep hypopnea. Sleep hypopnea is defined by ≥30% drop in breathing and an at least 3% drop in peripheral capillary oxygen saturation (Spo2) measured at the fingertip. Whether the conventional measures of oxygen desaturation (OD) at the fingertip and at the arm or wrist are identical is essentially unknown. Objective We aimed to compare event-by-event arm OD (arm_OD) with fingertip OD (finger_OD) in sleep hypopneas during both naïve sleep and CPAP therapy. Methods Thirty patients with OSA underwent an incremental, stepwise CPAP titration protocol during all-night in-lab video-polysomnography monitoring (ie, 1-h baseline sleep without CPAP followed by stepwise increments of 1 cmH2O pressure per hour starting from 5 to 8 cmH2O depending on the individual). Arm_OD of the left biceps muscle and finger_OD of the left index fingertip in sleep hypopneas were simultaneously measured by frequency-domain near-infrared spectroscopy and video-polysomnography photoplethysmography, respectively. Bland-Altman plots were used to illustrate the agreements between arm_OD and finger_OD during baseline sleep and under CPAP. We used t tests to determine whether these measurements significantly differed. Results In total, 534 obstructive apneas and 2185 hypopneas were recorded. Of the 2185 hypopneas, 668 (30.57%) were collected during baseline sleep and 1517 (69.43%), during CPAP sleep. The mean difference between finger_OD and arm_OD was 2.86% (95% CI 2.67%-3.06%, t667=28.28; P<.001; 95% limits of agreement [LoA] –2.27%, 8.00%) during baseline sleep and 1.83% (95% CI 1.72%-1.94%, t1516=31.99; P<.001; 95% LoA –2.54%, 6.19%) during CPAP. Using the standard criterion of 3% saturation drop, arm_OD only recognized 16.32% (109/668) and 14.90% (226/1517) of hypopneas at baseline and during CPAP, respectively. Conclusions arm_OD is 2% to 3% lower than standard finger_OD in sleep hypopnea, probably because the measured arm_OD originates physiologically from arterioles, venules, and capillaries; thus, the venous blood adversely affects its value. Our findings demonstrate that the standard criterion of ≥3% OD drop at the arm or wrist is not suitable to define hypopnea because it could provide large false-negative results in diagnosing OSA and assessing CPAP treatment effectiveness.

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“…After a full text review, 102 studies in total were included in the qualitative review. 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 …”

Section: Resultsmentioning

confidence: 99%

“…The studies related to arrhythmias accounted for the largest proportion, at 62 studies 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 ( Supplementary Table 1 , only online). Most studies had AF detection as the main task.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis

et al. 2022

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Purpose Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases. Materials and Methods The searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity. Results A total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983). Conclusion This review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.

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A new smart wristband equipped with an artificial intelligence algorithm to detect atrial fibrillation

Cited by 76 publications

References 14 publications

Diagnostic Accuracy of Smartwatches for the Detection of Cardiac Arrhythmia: Systematic Review and Meta-analysis

Diagnostic Accuracy of Smartwatches for the Detection of Cardiac Arrhythmia: Systematic Review and Meta-analysis

The Challenges and Pitfalls of Detecting Sleep Hypopnea Using a Wearable Optical Sensor: Comparative Study

Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis

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