Detecting Atrial Fibrillation and Atrial Flutter in Daily Life Using Photoplethysmography Data

Eerikäinen, Linda M.; Bonomi, A.; Schipper, Fons; Dekker, Lukas; Morree, Helma M. de; Vullings, Rik; Aarts, Ronald M.

doi:10.1109/jbhi.2019.2950574

Cited by 43 publications

(42 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Han

Bashar

Mohagheghian

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The results showed that the method obtains 95.0% accuracy, 96.2% Sen and 92.8% Spe. Linda et al [14] combined the balance of the PPG characteristics, the interpulse intervals (IPIs) and accelerometer data as input for the RF model. The study categorized AF, AFL and other rhythms.…”

Section: Discussionmentioning

confidence: 99%

“…In 2019, Fallet et al [13] computed the values of multiple parameters, such as the mean, standard deviation, and quartile in PPG, and identified these parameters as inputs to the decision tree model. In the same year, Linda et al [14] combined PPG characteristics, interpulse intervals (IPIs) and accelerometer data in the random forest (RF) model to classify AF, atrial flutter (AFL) and other rhythms. This is a forward-looking study.…”

Section: Introductionmentioning

confidence: 99%

Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning

Cheng

Chen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Atrial fibrillation (AF) is the most common persistent arrhythmia and is likely to cause strokes and damage to heart function in patients. Electrocardiogram (ECG) is the gold standard for detecting AF. However, ECGs have short boards with short monitoring cycles and problems with gathering. It is also difficult to detect a burst AF through ECG. In contrast, photoplethysmography (PPG) is easy to perform and suitable for long-term monitoring. In this study, we propose a method that combines timefrequency analysis with deep learning and identifies AF based on PPG. The advantage of the method is that there is no need for the noise filtering and feature extraction of PPG, and it has a high generalization capability. The data for the experiment came from three publicly accessible databases. The first part of the experimental method uses data augmentation to convert the 10 s PPG segment into a time-frequency chromatograph by means of time-frequency analysis. The second part inputs the chromatograph into a hybrid framework that combines a convolutional neural network (CNN) and long short-term memory (LSTM) for AF/nonAF classification. The experimental results show that the method has a high classification accuracy, sensitivity, specificity, and F1 score, which are equal to 98.21%, 98.00%, 98.07% and 98.13%, respectively. The area under the receiver operating characteristic curve (AUC) is 0.9959. The model we propose not only aids doctors in diagnosing AF but also provides a method for identifying AF through portable wearable devices. INDEX TERMS Atrial fibrillation, photoplethysmography (PPG), time-frequency analysis, convolutional neural networks (CNN), long short-term memory (LSTM).

show abstract

“…As far as AF detection is concerned, two main approaches are used to obtain PPG signals: photodetectors (PD) and cameras [ 37 ]. For PD-based PPG sensors, Bonomi et al and Eerikäinen et al severally used a wrist wearable sensor like a smartwatch with a sampling rate of 128 Hz to collect PPG signals to classify AF and NAF [ 41 , 42 , 43 ]. Barshar et al also used the PD in a smartwatch to collect a PPG signal and detect AF from SR [ 44 ].…”

Section: Instrument/signalmentioning

confidence: 99%

Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey

Jiang

Zhou

Ling

et al. 2021

Sensors

View full text Add to dashboard Cite

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.

show abstract

Detecting Atrial Fibrillation and Atrial Flutter in Daily Life Using Photoplethysmography Data

Cited by 43 publications

References 46 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

Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning

Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey

Contact Info

Product

Resources

About