Photoplethysmography based atrial fibrillation detection: a review

Pereira, Tânia; Tran, Nate; Gadhoumi, Kais; Pelter, Michele M.; H., Duc; Lee, Randall J.; Colorado, René A.; Meisel, Karl; Hu, Xiao

doi:10.1038/s41746-019-0207-9

Cited by 211 publications

(152 citation statements)

References 88 publications

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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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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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“…The PPG algorithm had very high specificity and diagnostic accuracy, but was limited by a high dropout rate owing to insufficient signal quality. Although few comparison studies have been performed, PPG-based analysis of heart rate and rhythm might be less accurate than ECG-based assessment 42 .…”

Section: Photoplethysmographymentioning

confidence: 99%

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

et al. 2020

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Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in patients. Technological advances in computing have led to the introduction of novel physiological biosignals that can increase the frequency at which abnormalities in cardiovascular parameters can be detected, making expert-level, automated diagnosis a reality. However, use of these biosignals for diagnosis also raises numerous concerns related to accuracy and actionability within clinical guidelines, in addition to medico-legal and ethical issues. Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy. T he se tools are increasingly recognized by regulatory a g e n c ies a n d p r o f e s s ional m e d ic al s oc ie ti es, but several technical and ethical issues remain. In this Review, we describe the current state of cardiovascular monitoring along the continuum from biosignal acquisition to the identification of novel biosensors and the development of analytical techniques and ultimately to regulatory and ethical issues. Furthermore, we outline new paradigms for cardiovascular monitoring.

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“…Recently, convolutional neural networks (CNN), a class of artificial neural networks with a strong capability in feature extraction, have achieved great success in computer vision medical tasks [13][14][15][16] . Features are no longer hand-derived but learned by models trained through backpropagation 15 .…”

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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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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Photoplethysmography based atrial fibrillation detection: a review

Cited by 211 publications

References 88 publications

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

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

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

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

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