Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks

Shashikumar, Supreeth P.; Shah, Amit; Clifford, Gari D.; Nemati, Shamim

doi:10.1145/3219819.3219912

Cited by 68 publications

(57 citation statements)

References 46 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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“…2). [32][33][34] Traditionally, prominent features were derived from the tachogram (RR intervals) since it is a reliable measure of heart beats. 35 Realizing that PPG waveforms may carry physiological information beyond heart rate, new features beyond RR intervals were derived.…”

Section: Ppg-based Af Detectionmentioning

confidence: 99%

“…raw plot of the signal, fast Fourier transform spectrum, or wavelet spectrogram-represented in the Fig. 2 in PPG representation part) were used with promising results in the detection of physiological events, 32,37,38 Images for PPG representation in Fig. 2 is a general depiction of the format types of information used by the different algorithms.…”

Section: Ppg-based Af Detectionmentioning

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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“…In addition to the field of AI, DNN has also been utilized in diverse scientific disciplines, including the fields of biomedicine (Liang et al, 2018;Shashikumar et al, 2018), economics (Singh & Srivastava, 2017;Yong et al, 2017), chemistry (Fooshee et al, 2018;Sun et al, 2019), and physics (Bhimji et al, 2018;Sadowski & Baldi, 2018). Despite the numerous successes obtained with DNN, limitations remain concerning the application of DNN in numerous scientific problems due to the following reasons: First, a large amount of data is usually requisite to guarantee model accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep learning of subsurface flow via theory-guided neural network

Wang

Zhang

Chang

et al. 2020

Journal of Hydrology

194

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Active researches are currently being performed to incorporate the wealth of scientific knowledge into data-driven approaches (e.g., neural networks) in order to improve the latter's effectiveness. In this study, the Theory-guided Neural Network (TgNN) is proposed for deep learning of subsurface flow. In the TgNN, as supervised learning, the neural network is trained with available observations or simulation data while being simultaneously guided by theory (e.g., governing equations, other physical constraints, engineering controls, and expert knowledge) of the underlying problem. The TgNN can achieve higher accuracy than the ordinary Artificial Neural Network (ANN) because the former provides physically feasible predictions and can be more readily generalized beyond the regimes covered with the training data. Furthermore, the TgNN model is proposed for subsurface flow with heterogeneous model parameters. Several numerical cases of two-dimensional transient saturated flow are introduced to test the performance of the TgNN. In the learning process, the loss function contains data mismatch, as well as PDE constraint, engineering control, and expert knowledge. After obtaining the parameters of the neural network by minimizing the loss function, a TgNN model is built that not only fits the data, but also adheres to physical/engineering constraints. Predicting the future response can be easily realized by the TgNN model. In addition, the TgNN model is tested in more complicated scenarios, such as prediction with changed boundary conditions, learning from noisy data or outliers, transfer learning, and engineering controls. Numerical results demonstrate that the TgNN model achieves much better predictability, reliability, and generalizability than ANN models due to the physical/engineering constraints in the former.

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Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks

Cited by 68 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

Photoplethysmography based atrial fibrillation detection: a review

Deep learning of subsurface flow via theory-guided neural network

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