Dataset Information

Feasibility of a T-Shirt-Type Wearable Electrocardiography Monitor for Detection of Covert Atrial Fibrillation in Young Healthy Adults.

ABSTRACT: Covert atrial fibrillation (AF) accounts for cryptogenic stroke aetiology in elderly patients and in younger populations. However, asymptomatic AF is difficult to diagnose based on a short electrocardiography (ECG) recording. We evaluated the feasibility of a self-applied continuous ECG monitoring device that can record automatically, easily, and noninvasively in a younger population. We investigated community screening for asymptomatic AF using a wireless single-lead ECG with an electrode embedded in a T-shirt. One hundred men with a CHADS2 score ?1 who were free from AF and <65 years of age were enrolled. We instructed the participants to wear ECG monitoring devices for at least 4 days/week over 2 months. The proportion of participants with newly detected AF (NDAF) and the monitoring time were evaluated. The mean CHADS2 score was 1.43?±?0.62. The mean patient age was 52.5?±?5.4 years. The mean monitoring time was 222?±?199?hours. NDAF continuing for >30?seconds was detected in 10 participants (10.0%). AF continuing for >6?minutes was detected in 2 participants (2.0%). The T-shirt-type wearable ECG monitoring system was suitable for continuous, daily long-term use among young people with high physical activity, and it had the distinct capability of identifying covert AF.

SUBMITTER: Fukuma N

PROVIDER: S-EPMC6692346 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Feasibility of a T-Shirt-Type Wearable Electrocardiography Monitor for Detection of Covert Atrial Fibrillation in Young Healthy Adults.

Fukuma Nobuaki N Hasumi Eriko E Fujiu Katsuhito K Waki Kayo K Toyooka Tsuguyoshi T Komuro Issei I Ohe Kazuhiko K

Scientific reports 20190813 1

Covert atrial fibrillation (AF) accounts for cryptogenic stroke aetiology in elderly patients and in younger populations. However, asymptomatic AF is difficult to diagnose based on a short electrocardiography (ECG) recording. We evaluated the feasibility of a self-applied continuous ECG monitoring device that can record automatically, easily, and noninvasively in a younger population. We investigated community screening for asymptomatic AF using a wireless single-lead ECG with an electrode embed ...[more]

PMID: 31409855

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Project description:BackgroundConsumer smartwatches have gained attention as mobile health (mHealth) tools able to detect atrial fibrillation (AF) using photoplethysmography (PPG) or a short strip of electrocardiogram (ECG). PPG has limited accuracy due to the movement artifacts, whereas ECG cannot be used continuously, is usually displayed as a single-lead signal and is limited in asymptomatic cases.ObjectiveDoubleCheck-AF is a validation study of a wrist-worn device dedicated to providing both continuous PPG-based rhythm monitoring and instant 6-lead ECG with no wires. We evaluated its ability to differentiate between AF and sinus rhythm (SR) with particular emphasis on the challenge of frequent premature beats.Methods and resultsWe performed a prospective, non-randomized study of 344 participants including 121 patients in AF. To challenge the specificity of the device two control groups were selected: 95 patients in stable SR and 128 patients in SR with frequent premature ventricular or atrial contractions (PVCs/PACs). All ECG tracings were labeled by two independent diagnosis-blinded cardiologists as "AF," "SR" or "Cannot be concluded." In case of disagreement, a third cardiologist was consulted. A simultaneously recorded ECG of Holter monitor served as a reference. It revealed a high burden of ectopy in the corresponding control group: 6.2 PVCs/PACs per minute, bigeminy/trigeminy episodes in 24.2% (31/128) and runs of ≥3 beats in 9.4% (12/128) of patients. AF detection with PPG-based algorithm, ECG of the wearable and combination of both yielded sensitivity and specificity of 94.2 and 96.9%; 99.2 and 99.1%; 94.2 and 99.6%, respectively. All seven false-positive PPG-based cases were from the frequent PVCs/PACs group compared to none from the stable SR group (P < 0.001). In the majority of these cases (6/7) cardiologists were able to correct the diagnosis to SR with the help of the ECG of the device (P = 0.012).ConclusionsThis is the first wearable combining PPG-based AF detection algorithm for screening of AF together with an instant 6-lead ECG with no wires for manual rhythm confirmation. The system maintained high specificity despite a remarkable amount of frequent single or multiple premature contractions.

Project description:Aims:Atrial fibrillation (AF) is a public health problem and its prevalence is increasing worldwide. Electronic cohorts, with large electrocardiogram (ECG) databases linked to mortality data, can be useful in determining prognostic value of ECG abnormalities. Our aim is to evaluate the risk of mortality in patients with AF from Brazil. Methods:This observational retrospective study of primary care patients was developed with the digital ECG database from the Telehealth Network of Minas Gerais, Brazil. ECGs performed from 2010 to 2017 were interpreted by cardiologists and the University of Glasgow automated analysis software. An electronic cohort was obtained linking data from ECG exams and those from a national mortality information system, using standard probabilistic linkage methods. We considered only the first ECG of each patient. Patients under 16 years were excluded. Hazard ratios (HR) for mortality were adjusted for demographic and self-reported clinical factors and estimated with Cox regression. Results:From a dataset of 1,773,689 patients, 1,558,421 were included, mean age 51.6 years; 40.2% male. There were 3.34% deaths from all causes in 3.68 years of median follow up. The prevalence of AF was 1.33%. AF was an independent risk factor for all-cause mortality (HR 2.10, 95%CI 2.03-2.17) and cardiovascular mortality (HR 2.06, 95%CI 1.86-2.29). Females with AF had a higher risk of overall and cardiovascular mortality compared with males (p < 0.001). Conclusions:AF was a strong predictor of cardiovascular and all-cause mortality in a primary care population, with increased risk in women. Condensed abstract:To assess risk of mortality in AF patients, an electronic cohort was obtained linking data from ECG exams of Brazilian primary care patients and a national mortality information system. From 1,558,421 patients, AF (prevalence 1.33%) carried a higher risk of overall and cardiovascular mortality, with increased risk in women. What’s New:This is the first study with a large Brazilian electronic cohort to evaluate the risk of mortality linked to AF in primary care patients.AF patients from a Brazilian primary care population had a higher risk of death for all causes (HR 2.10, 95%CI 2.03-2.17) and cardiovascular mortality (HR 2.06, 95%CI 1.86-2.29).Female patients with AF had an increased risk of overall and cardiovascular mortality compared with male patients (p < 0.001).

Project description:BACKGROUND:Atrial Fibrillation is the most common arrhythmia worldwide with a global age adjusted prevalence of 0.5% in 2010. Anticoagulation treatment using warfarin or direct oral anticoagulants is effective in reducing the risk of AF-related stroke by approximately two-thirds and can provide a 10% reduction in overall mortality. There has been increased interest in detecting AF due to its increased incidence and the possibility to prevent AF-related strokes. Inexpensive consumer devices which measure the ECG may have the potential to accurately detect AF but do not generally incorporate diagnostic algorithms. Machine learning algorithms have the potential to improve patient outcomes particularly where diagnoses are made from large volumes or complex patterns of data such as in AF. METHODS:We designed a novel AF detection algorithm using a de-correlated Lorenz plot of 60 consecutive RR intervals. In order to reduce the volume of data, the resulting images were compressed using a wavelet transformation (JPEG200 algorithm) and the compressed images were used as input data to a Support Vector Machine (SVM) classifier. We used the Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) Atrial Fibrillation database and the MIT-BIH Arrhythmia database as training data and verified the algorithm performance using RR intervals collected using an inexpensive consumer heart rate monitor device (Polar-H7) in a case-control study. RESULTS:The SVM algorithm yielded excellent discrimination in the training data with a sensitivity of 99.2% and a specificity of 99.5% for AF. In the validation data, the SVM algorithm correctly identified AF in 79/79 cases; sensitivity 100% (95% CI 95.4%-100%) and non-AF in 328/336 cases; specificity 97.6% (95% CI 95.4%-99.0%). CONCLUSIONS:An inexpensive wearable heart rate monitor and machine learning algorithm can be used to detect AF with very high accuracy and has the capability to transmit ECG data which could be used to confirm AF. It could potentially be used for intermittent screening or continuously for prolonged periods to detect paroxysmal AF. Further work could lead to cost-effective and accurate estimation of AF burden and improved risk stratification in AF.

Dataset Information

Feasibility of a T-Shirt-Type Wearable Electrocardiography Monitor for Detection of Covert Atrial Fibrillation in Young Healthy Adults.

Publications

Feasibility of a T-Shirt-Type Wearable Electrocardiography Monitor for Detection of Covert Atrial Fibrillation in Young Healthy Adults.

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