The Accuracy of Atrial Fibrillation Detection from Wrist Photoplethysmography. A Study on Post-Operative Patients

Tarniceriu, Adrian; Harju, Jarkko; Yousefi, Zeinab Rezaei; Vehkaoja, Antti; Parák, Jakub; Yli‐Hankala, Arvi; Korhonen, Ilkka

doi:10.1109/embc.2018.8513197

Cited by 15 publications

(10 citation statements)

References 16 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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“…Markov model is another statistical tool that could be used for AF detection. RR-interval time series features are used in this model to define the distributions that best fit the data, and the probability for various rhythms can be drawn from these distributions, 44,49,50 Elastic net is a regularization method for regression and classification models. Elastic net performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the logistic regression model.…”

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%

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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“…Single ectopic beats such as ventricular extra systoles are found based on the morphology of the IBI tachogram signal and marked before the arrhythmia detection step. The method has been described in more detail in [14] and the IBI estimation accuracy in [15,16]. Ectopic beat detection method is described in more detail in [17].…”

Section: Arrhythmia Detectionmentioning

confidence: 99%

Performance of Wrist Photoplethysmography in Monitoring Atrial Fibrillation in Post Cardiac Surgery Patients

Tarniceriu¹,

Vuohelainen²,

Haddad³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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New methods for unobtrusive long-term monitoring of arrhythmias are needed. We evaluated the accuracy of wrist-worn optical heart rate monitor in beat-to-beat interval estimation and performance in classifying the rhythm in sinus or atrial fibrillation with 30 post cardiac surgery patients (69.3 ± 6.9 years) in 24-hour recordings. The recordings were made during the first post-operative days when the patients are still relatively stationary and are in high risk of getting post-surgery atrial fibrillation.The results show 10.04 ms mean absolute error in beatto-beat intervals. Atrial fibrillation was analysed in 5minute segments. 100% sensitivity and 96.1% specificity were obtained. The specificity was increased to 99.74% when the data segments with large number of ventricular and supra ventricular extra systoles were removed. The results show that the performance of optical heart rate monitoring technology is adequate for screening of atrial fibrillation at least in stationary conditions.

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The Accuracy of Atrial Fibrillation Detection from Wrist Photoplethysmography. A Study on Post-Operative Patients

Cited by 15 publications

References 16 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

Photoplethysmography based atrial fibrillation detection: a review

Performance of Wrist Photoplethysmography in Monitoring Atrial Fibrillation in Post Cardiac Surgery Patients

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