Deep Learning Formulation of ECGI for Data-Driven Integration of Spatiotemporal Correlations and Imaging Information

Bacoyannis, Tania; Krebs, Julian; Cedilnik, Nicolas; Cochet, Hubert; Sermesant, Maxime

doi:10.1007/978-3-030-21949-9_3

Cited by 12 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In electrocardiographic imaging, recreating the heart’s electrical activity runs into numerical difficulties when using body surface potentials. A method using generative neural nets based on CVAEs have been used to tackle this problem [ 143 ].…”

Section: Applicationsmentioning

confidence: 99%

An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications

Singh

Ogunfunmi

2021

Entropy

View full text Add to dashboard Cite

Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the Latent Space (which retains the knowledge in the input data with reduced dimensionality but preserves maximum information) and the Decoder (which reconstructs the input data from the compressed latent space). Autoencoders have found wide applications in dimensionality reduction, object detection, image classification, and image denoising applications. Variational Autoencoders (VAEs) can be regarded as enhanced Autoencoders where a Bayesian approach is used to learn the probability distribution of the input data. VAEs have found wide applications in generating data for speech, images, and text. In this paper, we present a general comprehensive overview of variational autoencoders. We discuss problems with the VAEs and present several variants of the VAEs that attempt to provide solutions to the problems. We present applications of variational autoencoders for finance (a new and emerging field of application), speech/audio source separation, and biosignal applications. Experimental results are presented for an example of speech source separation to illustrate the powerful application of variants of VAE: VAE, β-VAE, and ITL-AE. We conclude the paper with a summary, and we identify possible areas of research in improving performance of VAEs in particular and deep generative models in general, of which VAEs and generative adversarial networks (GANs) are examples.

show abstract

Section: Applicationsmentioning

confidence: 99%

An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications

Singh

Ogunfunmi

2021

Entropy

View full text Add to dashboard Cite

show abstract

“…Many studies have been published on CVAE including medical applications to predict post-trauma health outcomes [ 10 ]. Another study was done by the authors of [ 11 ], which included using CVAE to automatically detect plant diseases, as well, the authors in [ 12 ] developed CVAE based system for electrocardiographic imaging (ECGI).…”

Section: Related Workmentioning

confidence: 99%

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Al-Mohannadi

Al-Maadeed

Elharrouss

et al. 2021

Sensors

View full text Add to dashboard Cite

Cardiovascular diseases (CVDs) have shown a huge impact on the number of deaths in the world. Thus, common carotid artery (CCA) segmentation and intima-media thickness (IMT) measurements have been significantly implemented to perform early diagnosis of CVDs by analyzing IMT features. Using computer vision algorithms on CCA images is not widely used for this type of diagnosis, due to the complexity and the lack of dataset to do it. The advancement of deep learning techniques has made accurate early diagnosis from images possible. In this paper, a deep-learning-based approach is proposed to apply semantic segmentation for intima-media complex (IMC) and to calculate the cIMT measurement. In order to overcome the lack of large-scale datasets, an encoder-decoder-based model is proposed using multi-image inputs that can help achieve good learning for the model using different features. The obtained results were evaluated using different image segmentation metrics which demonstrate the effectiveness of the proposed architecture. In addition, IMT thickness is computed, and the experiment showed that the proposed model is robust and fully automated compared to the state-of-the-art work.

show abstract

“…Our new numerical method is based on DL and autoencoders in order to solve the whole inverse problem and reconstruct accurate 3D activation maps. A preliminary version of this method in 2D and on a single geometry was presented in [13].…”

Section: Introductionmentioning

confidence: 99%

Deep learning formulation of electrocardiographic imaging integrating image and signal information with data-driven regularization

Bacoyannis

Cedilnik

et al. 2021

EP Europace

Self Cite

View full text Add to dashboard Cite

Aims Electrocardiographic imaging (ECGI) is a promising tool to map the electrical activity of the heart non-invasively using body surface potentials (BSP). However, it is still challenging due to the mathematically ill-posed nature of the inverse problem to solve. Novel approaches leveraging progress in artificial intelligence could alleviate these difficulties. Methods and results We propose a deep learning (DL) formulation of ECGI in order to learn the statistical relation between BSP and cardiac activation. The presented method is based on Conditional Variational AutoEncoders using deep generative neural networks. To quantify the accuracy of this method, we simulated activation maps and BSP data on six cardiac anatomies. We evaluated our model by training it on five different cardiac anatomies (5000 activation maps) and by testing it on a new patient anatomy over 200 activation maps. Due to the probabilistic property of our method, we predicted 10 distinct activation maps for each BSP data. The proposed method is able to generate volumetric activation maps with a good accuracy on the simulated data: the mean absolute error is 9.40 ms with 2.16 ms standard deviation on this testing set. Conclusion The proposed formulation of ECGI enables to naturally include imaging information in the estimation of cardiac electrical activity from BSP. It naturally takes into account all the spatio-temporal correlations present in the data. We believe these features can help improve ECGI results.

show abstract

Deep Learning Formulation of ECGI for Data-Driven Integration of Spatiotemporal Correlations and Imaging Information

Cited by 12 publications

References 10 publications

An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications

An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Deep learning formulation of electrocardiographic imaging integrating image and signal information with data-driven regularization

Contact Info

Product

Resources

About