Can one detect atrial fibrillation using a wrist-type photoplethysmographic device?

Fallet, Sibylle; Lemay, Mathieu; Renevey, Philippe; Leupi, Celestin; Pruvot, Étienne; Vesin, Jean‐Marc

doi:10.1007/s11517-018-1886-0

Cited by 48 publications

(36 citation statements)

References 22 publications

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“…Features commonly extracted from PPG time series are morphological descriptors, time domain statistics, frequency domain statistics, nonlinear measures, wavelet based measures, and cross-correlation measures. [53][54][55][56][57][58][59][60] There were generally three main ML approaches used in the reviewed studies: k-nearest neighbors (KNN), support vector machine (SVM), and decision trees (DT). KNN classification is a relatively simple clustering technique where a sample is classified by a plurality vote of its neighbors and assigned to the class based on the most common class among its k closest neighbors.…”

Section: Ppg Representationsmentioning

confidence: 99%

“…65,73 The most common approach to deal with this issue is to simply discard the corrupted segments and use only the clean parts of PPG signals. 49,50,58,60,64,74 Some of the works followed a two-step approach: first, to identify motion artifacts by using accelerometer data, or by performing PPG signal quality assessment; and second, to perform AF detections with only good quality signals. 51,75 This often implies loss, and in some cases, a huge part of the signals acquired.…”

Section: Other Cardiac Arrhythmiasmentioning

confidence: 99%

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Photoplethysmography based atrial fibrillation detection: a review

et al. 2020

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Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations-a technology known as photoplethysmography (PPG)-from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.npj Digital Medicine (2020) 3:3 ; https://doi.org/10.1038/s41746-019-0207-9 PHOTOPLETHYSMOGRAPHY PPG signal PPG waveform is generated during a cardiac cycle and typically measured at a peripheral site. Therefore, it is essentially a pulse pressure waveform that originates from the heart contraction and propagates through the vascular tree. As blood flow is controlled

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Section: Ppg Representationsmentioning

confidence: 99%

Section: Other Cardiac Arrhythmiasmentioning

confidence: 99%

Photoplethysmography based atrial fibrillation detection: a review

et al. 2020

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“…Signal quality assessment and suppression of ectopic beats, bigeminy along with respiratory sinus arrhythmia are described in [ 14 ] for more accurate AF detection; however, no detection of PACs or PVCs are reported. In [ 15 ], the case of ventricular arrhythmia (VA) was considered in AF detection from wrist PPG signal, however, only accuracy of AF detection was reported, and no independent VA detection was performed. Recently, deep learning method is used to classify AF and NSR in [ 16 ] which mentioned that high PAC burden would lower AF detection accuracy.…”

Section: Introductionmentioning

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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“…33,35 In addition to time-domain analysis of IBIs, other features that could characterize the rhythm have been extracted from the signal. These include mainly features from the frequency domain, such as spectral entropy, 31 spectral purity index, 31 and wavelet power spectrum. 28 Signal quality metrics have been combined with rhythm information, and the quality metrics have been derived from either the PPG signal itself or the accelerometer data characterizing movement that often is measured with PPG at the wrist.…”

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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Can one detect atrial fibrillation using a wrist-type photoplethysmographic device?

Cited by 48 publications

References 22 publications

Photoplethysmography based atrial fibrillation detection: a review

Photoplethysmography based atrial fibrillation detection: a review

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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