Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning

Shen, Yichen; Voisin, Maxime; Aliamiri, Alireza; Avati, Anand; Hannun, Awni; Ng, Andrew Y.

doi:10.1145/3292500.3330657

Cited by 45 publications

(45 citation statements)

References 22 publications

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“…The training data derived were a mixture of healthy individuals and patients who were hospitalized long term or for same day outpatient procedures (i.e., taken from a population with a much higher prevalence of arrhythmia than the general population). When comparing reported evaluation metrics, our algorithm outperforms other deep learning methods that have been proposed so far for AF event detection 8,[17][18][19][20][21] . However, a direct comparison of prior published work is challenging due to the lack of published code or availability of trained models and training data needed for baseline neural network-based comparisons.…”

Section: Discussionmentioning

confidence: 99%

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Multi-task deep learning for cardiac rhythm detection in wearable devices

Soto

Ashley

2020

npj Digit. Med.

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Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements such as step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist, where noise remains an unsolved problem. Here we develop DeepBeat, a multitask deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation. The model is trained on approximately one million simulated unlabeled physiological signals and fine-tuned on a curated dataset of over 500 K labeled signals from over 100 individuals from 3 different wearable devices. We demonstrate that, in comparison with a single-task model, our architecture using unsupervised transfer learning through convolutional denoising autoencoders dramatically improves the performance of atrial fibrillation detection from a F1 score of 0.54 to 0.96. We also include in our evaluation a prospectively derived replication cohort of ambulatory participants where the algorithm performed with high sensitivity (0.98), specificity (0.99), and F1 score (0.93). We show that two-stage training can help address the unbalanced data problem common to biomedical applications, where large-scale well-annotated datasets are hard to generate due to the expense of manual annotation, data acquisition, and participant privacy.

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Section: Discussionmentioning

confidence: 99%

“…Researchers have applied deep CNN to the problem of AF event detection 8,[17][18][19][20][21] . Shashikumar et al 17 developed a blended approach, combining the output of a CNN with other selected features derived from beat-to-beat variability and signal quality.…”

Section: Introductionmentioning

confidence: 99%

Multi-task deep learning for cardiac rhythm detection in wearable devices

Soto

Ashley

2020

npj Digit. Med.

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“…Indeed, we have shown that detecting PAC/PVC can lead to significant false positive reduction during AF detection [ 40 ]. Other recent published reports [ 14 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ] have largely focused on AF without accounting for PAC/PVC and consequently their accuracy of AF detection was suboptimal.…”

Section: Discussionmentioning

confidence: 99%

Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch

Han

Bashar

Mohagheghian

et al. 2020

Sensors

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We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincaré plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC.

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“…20,28,31 Along with traditional machine learning methods, deep learning approaches have been proposed for AF detection from wrist-worn PPG. 21,23,25,28,29,32,36 Shashikumar et al 21,28 extracted first the time series and frequencydomain information as the input for their convolutional neural network. Two approaches have been based on extracted heart rate and activity data.…”

Section: Methods For Af Detection and Ppg Data Quality Assessmentmentioning

confidence: 99%

“…Most of the studies include fewer than 100 volunteers. [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] Six studies had a study population of more than 100 subjects [35][36][37][38][39][40] ; the largest monitored 1617 volunteers in an ambulatory setting. 36 Rhythm characteristics in the study populations varied among studies.…”

Section: Study Populationsmentioning

confidence: 99%

Atrial fibrillation monitoring with wrist-worn photoplethysmography-based wearables: State-of-the-art review

Eerikäinen

Bonomi

Dekker

et al. 2020

Cardiovascular Digital Health Journal

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Early detection and diagnosis of atrial fibrillation (AF) is essential in order to prevent stroke and other severe health consequences. The challenges in diagnosing AF arise from its intermittent and asymptomatic nature. Wrist-worn devices that use monitoring based on photoplethysmography have been proposed recently as a possible solution because of their ability to monitor heart rate and rhythm for long periods of time at low cost. Long-term continuous monitoring with implantable devices has been shown to increase the percentage of detected AF episodes, but the additional value of wrist-worn devices has yet to be determined. In this review, we present the state of the art in AF detection with wrist-worn devices, discuss the potential of the technology and current knowledge gaps, and propose directions for future research. The state-ofthe-art methods show excellent accuracy for AF detection. However, most of the studies were conducted in hospital settings, and more studies showing the accuracy of the technology for ambulatory long-term monitoring are needed. Objective comparison of results and methodologies among different studies currently is difficult due to the lack of adequate public datasets.

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Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning

Cited by 45 publications

References 22 publications

Multi-task deep learning for cardiac rhythm detection in wearable devices

Multi-task deep learning for cardiac rhythm detection in wearable devices

Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch

Atrial fibrillation monitoring with wrist-worn photoplethysmography-based wearables: State-of-the-art review

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