Physics-constrained deep active learning for spatiotemporal modeling of cardiac electrodynamics

Yao, Bing

doi:10.1016/j.compbiomed.2022.105586

Cited by 33 publications

(7 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the past few decades, deep learning or deep neural network (DNN) has emerged as a powerful tool for pattern recognition that can learn the abstracted features from complex data and yield state-of-the-art predictions ( Mousavi et al, 2019 ; Xie and Yao, 2022a ; Xie and Yao, 2022b ; Chen et al, 2022 ; Wang et al, 2022 ). As opposed to traditional machine learning, deep learning presents strong robustness and fault tolerance to uncertain factors, which makes it suitable for beat and rhythm classification from ECGs ( Tutuko et al, 2021 ).…”

Section: Research Backgroundmentioning

confidence: 99%

Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet

Xie,

Stavrakis,

Yao

2024

Front. Physiol.

Self Cite

View full text Add to dashboard Cite

Introduction: Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, which can eventually lead to heart failure, stroke, or even sudden death. Electrocardiography (ECG), which involves acquiring bioelectrical signals from the body surface to reflect heart activity, is a standard procedure for detecting AF. However, the occurrence of AF is often intermittent, costing a significant amount of time and effort from medical doctors to identify AF episodes. Moreover, human error is inevitable, as even experienced medical professionals can overlook or misinterpret subtle signs of AF. As such, it is of critical importance to develop an advanced analytical model that can automatically interpret ECG signals and provide decision support for AF diagnostics.Methods: In this paper, we propose an innovative deep-learning method for automated AF identification using single-lead ECGs. We first extract time-frequency features from ECG signals using continuous wavelet transform (CWT). Second, the convolutional neural networks enhanced with residual learning (ReNet) are employed as the functional approximator to interpret the time-frequency features extracted by CWT. Third, we propose to incorporate a multi-branching structure into the ResNet to address the issue of class imbalance, where normal ECGs significantly outnumber instances of AF in ECG datasets.Results and Discussion: We evaluate the proposed Multi-branching Resnet with CWT (CWT-MB-Resnet) with two ECG datasets, i.e., PhysioNet/CinC challenge 2017 and ECGs obtained from the University of Oklahoma Health Sciences Center (OUHSC). The proposed CWT-MB-Resnet demonstrates robust prediction performance, achieving an F1 score of 0.8865 for the PhysioNet dataset and 0.7369 for the OUHSC dataset. The experimental results signify the model’s superior capability in balancing precision and recall, which is a desired attribute for ensuring reliable medical diagnoses.

show abstract

Section: Research Backgroundmentioning

confidence: 99%

Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet

Xie,

Stavrakis,

Yao

2024

Front. Physiol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The loss function typically used in fully connected neural networks aims to reconstruct simulated iAPs from time and coordinates as inputs, leveraging calculable derivatives during propagation to ensure adherence to governing physical equations [30][31][32] . However, in this case, the input is eAP, and the pseudo-physics loss function is employed to maintain the iAP shape.…”

Section: Incorporating the Alievmentioning

confidence: 99%

Intelligent In-Cell Electrophysiology: Reconstructing Action Potentials from Nanoelectrode Data Using Physics-Informed Deep Learning.

Jahed,

Rahmani,

Foster

et al. 2024

Preprint

View full text Add to dashboard Cite

Intracellular electrophysiology is utilized across different scientific and medical disciplines, including neuroscience, cardiology, and pharmacology, due to its pivotal role in exploring and comprehending the electrical properties of cells within diverse biological systems. Traditional methods for intracellular electrophysiology, such as patch-clamp, are highly precise but suffer from being low-throughput and invasive. In contrast, Nanoelectrode Arrays (NEAs) present a promising alternative, enabling simultaneous intracellular action potential (iAP) and extracellular action potential (eAP) recordings with high throughput. However, accessing intracellular potentials with NEAs remains a challenge. This study introduces a technique for intracellular electrophysiology supported by artificial intelligence (AI) that capitalizes on thousands of synchronous eAP and iAP pairs collected from stem-cell-derived cardiomyocytes on NEAs. Our analysis on this unique dataset uncovered strong correlations between specific features of eAP and iAP waveforms, such as amplitude and spiking velocity, that were not detected before, suggesting that extracellular signals could be reliable indicators of intracellular signals. By developing a physics-informed deep learning model trained on these datasets, we show successful reconstruction of iAP waveforms from extracellular recordings. We then demonstrate our model’s potential for non-invasive, long-term, and high-throughput assessments of drug cardiotoxicity. Although we show one specific application of the AI-based model, it opens the door for future research in electrophysiology, with the promise of expanding the dataset to encompass various electrogenic cell types and drug interactions.

show abstract

“…Instead, this is achieved by the intermediate layers of the network automatically extracting relevant features from the data. DNN-based features have been proved to be more informative and effective than handcrafted features for data-driven disease detection [8], [23], [24], [25], [26], [27]. As a result, DNN models, especially convolutional neural networks (CNNs), have been developed to analyze retinal images for DR prediction with better performances than conventional machine learning methods.…”

Section: Research Background and Literature Reviewmentioning

confidence: 99%

Multi-Branching Temporal Convolutional Network With Tensor Data Completion for Diabetic Retinopathy Prediction

Wang,

Chen,

Liu

et al. 2024

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

Diabetic retinopathy (DR), caused by damage to the blood vessels in the tissue of the retina, is a microvascular complication of diabetes. DR is the leading cause of vision loss among working-aged adults. However, due to the low compliance rate of DR screening and expensive medical devices for ophthalmic exams, many DR patients did not seek proper medical attention until DR develops to irreversible stages (i.e., vision loss). Fortunately, the widely available electronic health record (EHR) databases provide an unprecedented opportunity to develop cost-effective machine-learning tools for DR detection. This paper proposes a Multi-branching Temporal Convolutional Network with Tensor Data Completion (MB-TCN-TC) model to analyze the longitudinal EHRs collected from diabetic patients for DR prediction. Experimental results demonstrate that the proposed MB-TCN-TC model not only effectively copes with the imbalanced data and missing value issues commonly seen in EHR datasets but also captures the temporal correlation and complicated interactions among medical variables in the longitudinal clinical records, yielding superior prediction performance compared to existing methods.

show abstract

Physics-constrained deep active learning for spatiotemporal modeling of cardiac electrodynamics

Cited by 33 publications

References 49 publications

Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet

Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet

Intelligent In-Cell Electrophysiology: Reconstructing Action Potentials from Nanoelectrode Data Using Physics-Informed Deep Learning.

Multi-Branching Temporal Convolutional Network With Tensor Data Completion for Diabetic Retinopathy Prediction

Contact Info

Product

Resources

About