A Ring-type Wearable Device for Atrial Fibrillation

2019 ◽  
Author(s):  
Keyword(s):  
Atrial Fibrillation ◽  
Wearable Device ◽  
Ring Type

scholarly journals Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study (Preprint)

2019 ◽  
Author(s):  
Soonil Kwon ◽  
Joonki Hong ◽  
Eue-Keun Choi ◽  
Byunghwan Lee ◽  
Changhyun Baik ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Learning Algorithm ◽  
Wearable Device ◽  
Control Group ◽  
Ring Type ◽  
Deep Learning Algorithm ◽  
Learning Analysis

BACKGROUND Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF). OBJECTIVE We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals. METHODS Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR). RESULTS In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR. CONCLUSIONS A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations. CLINICALTRIAL ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188

scholarly journals Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study

10.2196/16443 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e16443
Author(s):  
Soonil Kwon ◽  
Joonki Hong ◽  
Eue-Keun Choi ◽  
Byunghwan Lee ◽  
Changhyun Baik ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Learning Algorithm ◽  
Wearable Device ◽  
Control Group ◽  
Ring Type ◽  
Deep Learning Algorithm ◽  
Learning Analysis

Background Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF). Objective We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals. Methods Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR). Results In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR. Conclusions A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations. Trial Registration ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188

Diagnosis of Atrial Fibrillation Using Machine Learning with Wearable Devices after Cardiac Surgery (Preprint)

2021 ◽  
Author(s):  
Daisuke Hiraoka ◽  
Tomohiko Inui ◽  
Eiryo Kawakami ◽  
Megumi Oya ◽  
Ayumu Tsuji ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Cardiac Surgery ◽  
Early Detection ◽  
Pulse Rate ◽  
Paroxysmal Atrial Fibrillation ◽  
Wearable Device ◽  
Strongly Correlated ◽  
Cross Correlation Analysis ◽  
Data Output

BACKGROUND Some attempts have been made to detect atrial fibrillation with a wearable device equipped with photoelectric volumetric pulse wave technology, and it is expected to be applied under real clinical conditions. OBJECTIVE This study is the second part of a two-phase study aimed at developing a method for immediate detection of paroxysmal atrial fibrillation (AF) using a wearable device with built-in PPG. The objective of this study is to develop an algorithm to immediately diagnose atrial fibrillation by wearing an Apple Watch equipped with a photoplethysmography (PPG) sensor on patients undergoing cardiac surgery and using machine learning of the pulse data output from the device. METHODS A total of 80 subjects who underwent cardiac surgery at a single institution between June 2020 and March 2021 were monitored for postoperative atrial fibrillation using telemetry monitored ECG and Apple Watch. Atrial fibrillation was diagnosed by qualified physicians from telemetry-monitored ECGs and 12-lead ECGs; a diagnostic algorithm was developed using machine learning on pulse rate data output from the Apple Watch. RESULTS One of the 80 patients was excluded from the analysis due to redness of the Apple Watch wearer. 27 (34.2%) of the 79 patients developed AF, and 199 events of AF, including brief AF, were observed. 18 events of AF lasting longer than 1 hour were observed, and Cross-correlation analysis (CCF) showed that pulse rate measured by Apple Watch was strongly correlated (CCF 0.6-0.8) with 8 events and very strongly correlated (CCF >0.8) with 3 events. The diagnostic accuracy by machine learning was 0.7952 (sensitivity 0.6312, specificity 0.8605 at the point closest to the top-left) for the AUC of the ROC curve. CONCLUSIONS We were able to safely monitor pulse rate in patients after cardiac surgery by wearing an Apple Watch. Although the pulse rate from the PPG sensor does not follow the heart rate of the telemetry monitoring ECG in some parts, which may reduce the accuracy of the diagnosis of atrial fibrillation by machine learning, we have shown the possibility of clinical application of early detection of atrial fibrillation using only the pulse rate collected by the PPG sensor. CLINICALTRIAL The use of wristband type continuous pulse measurement device with artificial intelligence for early detection of paroxysmal atrial fibrillation Clinical Research Protocol No. jRCTs032200032 https://jrct.niph.go.jp/latest-detail/jRCTs032200032

Screening for Atrial Fibrillation With a Wearable Device

JAMA ◽  
2018 ◽  
Vol 320 (2) ◽  
pp. 139 ◽  
Author(s):  
Benjamin A. Steinberg ◽  
Jonathan P. Piccini
Keyword(s):  
Atrial Fibrillation ◽  
Wearable Device

scholarly journals Framework for Privacy-Preserving Wearable Health Data Analysis: Proof-of-Concept Study for Atrial Fibrillation Detection

2021 ◽  
Vol 11 (19) ◽  
pp. 9049
Author(s):  
Anamaria Vizitiu ◽  
Cosmin-Ioan Nita ◽  
Radu Miron Toev ◽  
Tudor Suditu ◽  
Constantin Suciu ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Data Analytics ◽  
Wearable Devices ◽  
Privacy Preserving ◽  
Health Data ◽  
Wearable Device ◽  
Proof Of Concept ◽  
Early Detection Of Disease ◽  
Encrypted Data ◽  
Concept Study

Medical wearable devices monitor health data and, coupled with data analytics, cloud computing, and artificial intelligence (AI), enable early detection of disease. Privacy issues arise when personal health information is sent or processed outside the device. We propose a framework that ensures the privacy and integrity of personal medical data while performing AI-based homomorphically encrypted data analytics in the cloud. The main contributions are: (i) a privacy-preserving cloud-based machine learning framework for wearable devices, (ii) CipherML—a library for fast implementation and deployment of deep learning-based solutions on homomorphically encrypted data, and (iii) a proof-of-concept study for atrial fibrillation (AF) detection from electrocardiograms recorded on a wearable device. In the context of AF detection, two approaches are considered: a multi-layer perceptron (MLP) which receives as input the ECG features computed and encrypted on the wearable device, and an end-to-end deep convolutional neural network (1D-CNN), which receives as input the encrypted raw ECG data. The CNN model achieves a lower mean F1-score than the hand-crafted feature-based model. This illustrates the benefit of hand-crafted features over deep convolutional neural networks, especially in a setting with a small training data. Compared to state-of-the-art results, the two privacy-preserving approaches lead, with reasonable computational overhead, to slightly lower, but still similar results: the small performance drop is caused by limitations related to the use of homomorphically encrypted data instead of plaintext data. The findings highlight the potential of the proposed framework to enhance the functionality of wearables through privacy-preserving AI, by providing, within a reasonable amount of time, results equivalent to those achieved without privacy enhancing mechanisms. While the chosen homomorphic encryption scheme prioritizes performance and utility, certain security shortcomings remain open for future development.

scholarly journals Automatic detection of Atrial Fibrillation

2021 ◽  
Author(s):  
◽  
Greg Hayes
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Health Sector ◽  
Wearable Device ◽  
P Wave ◽  
Support Vector ◽  
Wave Form ◽  
Discrete Wavelet ◽  
Rr Interval ◽  
The Individual

<p>Atrial Fibrillation is an abnormal arrhythmia of the heart and is a growingconcern in the health sector affecting 1% of the population. The incidenceof atrial fibrillation increases with age and has been found to be more detri-mental to long term cardiac health than previously thought. Sufferers arefive times more likely to experience a stroke than others. Often, atrial fib-rillation is asymptomatic and is frequently discovered only when a patient visits a hospital for other reasons. The detection of paroxysmal atrial fib-rillation can be difficult. Holter monitors are used to record the ECG overlong periods of time, but the resulting recording still needs to be analysed.This can be a time consuming task and one prone to errors. If a miniature,low-power, wearable device could be designed to detect and record whena heart experiences atrial fibrillation, then health professionals would havemore timely information to carry out better, more cost effective courses of treatment. This thesis presents progress towards development of such a device. Atrial fibrillation is characterised by random RR interval, missing Pwave and presence of atrial activity. The detection of the P wave and atrialactivity can be unreliable due to low signal levels and differences in wave-form morphology between subjects. The random RR interval appears tobe a more reliable method of detection. By analysing the ECG signal inboth the frequency and time domains, feature sets can be extracted for thedetection process. In this research, the Discrete Wavelet Transform is used to generate several sub-bands for analysing wave form morphology, and anumber of RR interval metrics are created for analysing the rhythm. All features are further processed and presented to a support vector machine classification stage for the ultimate detection of atrial fibrillation. Forty eight files from the MITDB database of the PhysioNet online ECG reposi-tory were downloaded and processed to form separate training and test-ing data sets. Overall classification accuracy for normal sinus rhythm was93% sensitivity and 95% specificity, and for atrial fibrillation, 95% sensitiv-ity and 93% specificity. These results were found to be sensitive to the ECG morphology of the individual subjects. This means that the system either needs to be trained on a greater number of ECG morphologies or perhaps trained on the morphology of the individual under investigation. Putting this issue aside, the research to date shows that it is reasonable to expect a small, low powered, wearable device, to be capable of automatically detecting whena heart experiences atrial fibrillation.</p>

scholarly journals Automatic detection of Atrial Fibrillation

2021 ◽  
Author(s):  
◽  
Greg Hayes
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Health Sector ◽  
Wearable Device ◽  
P Wave ◽  
Support Vector ◽  
Wave Form ◽  
Discrete Wavelet ◽  
Rr Interval ◽  
The Individual

<p>Atrial Fibrillation is an abnormal arrhythmia of the heart and is a growingconcern in the health sector affecting 1% of the population. The incidenceof atrial fibrillation increases with age and has been found to be more detri-mental to long term cardiac health than previously thought. Sufferers arefive times more likely to experience a stroke than others. Often, atrial fib-rillation is asymptomatic and is frequently discovered only when a patient visits a hospital for other reasons. The detection of paroxysmal atrial fib-rillation can be difficult. Holter monitors are used to record the ECG overlong periods of time, but the resulting recording still needs to be analysed.This can be a time consuming task and one prone to errors. If a miniature,low-power, wearable device could be designed to detect and record whena heart experiences atrial fibrillation, then health professionals would havemore timely information to carry out better, more cost effective courses of treatment. This thesis presents progress towards development of such a device. Atrial fibrillation is characterised by random RR interval, missing Pwave and presence of atrial activity. The detection of the P wave and atrialactivity can be unreliable due to low signal levels and differences in wave-form morphology between subjects. The random RR interval appears tobe a more reliable method of detection. By analysing the ECG signal inboth the frequency and time domains, feature sets can be extracted for thedetection process. In this research, the Discrete Wavelet Transform is used to generate several sub-bands for analysing wave form morphology, and anumber of RR interval metrics are created for analysing the rhythm. All features are further processed and presented to a support vector machine classification stage for the ultimate detection of atrial fibrillation. Forty eight files from the MITDB database of the PhysioNet online ECG reposi-tory were downloaded and processed to form separate training and test-ing data sets. Overall classification accuracy for normal sinus rhythm was93% sensitivity and 95% specificity, and for atrial fibrillation, 95% sensitiv-ity and 93% specificity. These results were found to be sensitive to the ECG morphology of the individual subjects. This means that the system either needs to be trained on a greater number of ECG morphologies or perhaps trained on the morphology of the individual under investigation. Putting this issue aside, the research to date shows that it is reasonable to expect a small, low powered, wearable device, to be capable of automatically detecting whena heart experiences atrial fibrillation.</p>

scholarly journals Association of Wearable Device Use With Pulse Rate and Health Care Use in Adults With Atrial Fibrillation

2021 ◽  
Vol 4 (5) ◽  
pp. e215821
Author(s):  
Libo Wang ◽  
Kyron Nielsen ◽  
Joshua Goldberg ◽  
Jeremiah R. Brown ◽  
John S. Rumsfeld ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Health Care ◽  
Pulse Rate ◽  
Health Care Use ◽  
Wearable Device ◽  
Device Use
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