Deep-learning-assisted detection and termination of spiral and broken-spiral waves in mathematical models for cardiac tissue

Mulimani, Mahesh Kumar; Alageshan, Jaya Kumar; Pandit, Rahul

doi:10.1103/physrevresearch.2.023155

Cited by 11 publications

(3 citation statements)

References 49 publications

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“…The working mechanism of this approach is quite general, so its application is not restricted to temperature-driven transitions investigated here. Moreover, noticing that NNs can easily deal with not only the data generated from numerical simulations but also experimental data [82], including both the real optical imaging [83] and preprocessed physical configurations such as snapshots of the many-body density matrix [84], hence LFRU is expected to be available for analyzing actual experiments as well. In addition, various possible phase transitions in a wide range of nonequilibrium complex many-body systems [3,[85][86][87][88], such as the active matter systems described by the Vicsek model [89] consisting of self-propelled particles, can likewise be investigated via this approach [90].…”

Section: Discussionmentioning

confidence: 99%

Learning phase transitions from regression uncertainty: a new regression-based machine learning approach for automated detection of phases of matter

Guo,

2023

New J. Phys.

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For performing regression tasks involved in various physics problems, enhancing the precision or equivalently reducing the uncertainty of regression results is undoubtedly one of the central goals. Here, somewhat surprisingly, the unfavorable regression uncertainty in performing the regression tasks of inverse statistical problems is found to contain hidden information concerning the phase transitions of the system under consideration. By utilizing this hidden information, a new unsupervised machine learning approach was developed in this work for automated detection of phases of matter, dubbed learning from regression uncertainty. This is achieved by revealing an intrinsic connection between regression uncertainty and response properties of the system, thus making the outputs of this machine learning approach directly interpretable via conventional notions of physics. It is demonstrated by identifying the critical points of the ferromagnetic Ising model and the three-state clock model, and revealing the existence of the intermediate phase in the six-state and seven-state clock models. Comparing to the widely-used classification-based approaches developed so far, although successful, their recognized classes of patterns are essentially abstract, which hinders their straightforward relation to conventional notions of physics. These challenges persist even when one employs the state-of-the-art deep neural networks that excel at classification tasks. In contrast, with the core working horse being a neural network performing regression tasks, our new approach is not only practically more efficient, but also paves the way towards intriguing possibilities for unveiling new physics via machine learning in a physically interpretable manner.

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Section: Discussionmentioning

confidence: 99%

Learning phase transitions from regression uncertainty: a new regression-based machine learning approach for automated detection of phases of matter

Guo,

2023

New J. Phys.

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“…Vandersickel et al (2019) proposed to use graph theory to detect rotors and focal patterns from arbitrarily positioned measurement sites. Mulimani et al (2020) used CNNs to detect the core regions of simulated spiral waves using a CNN-based classification approach and discriminating sub-regions containing spiral wave tips from areas exhibiting other dynamics, and consequently generated low-resolution heat maps indicating the likely and approximate core regions of spiral waves. Very similarly, Alhusseini et al (2020) used CNNs to classify and discriminate rotational and non-rotational tiles in maps of AF acquired with basket catheter electrode mapping.…”

Section: Phase Mapping and Phase Singularity Detection Techniquesmentioning

confidence: 99%

Rotor Localization and Phase Mapping of Cardiac Excitation Waves Using Deep Neural Networks

et al. 2021

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The analysis of electrical impulse phenomena in cardiac muscle tissue is important for the diagnosis of heart rhythm disorders and other cardiac pathophysiology. Cardiac mapping techniques acquire local temporal measurements and combine them to visualize the spread of electrophysiological wave phenomena across the heart surface. However, low spatial resolution, sparse measurement locations, noise and other artifacts make it challenging to accurately visualize spatio-temporal activity. For instance, electro-anatomical catheter mapping is severely limited by the sparsity of the measurements, and optical mapping is prone to noise and motion artifacts. In the past, several approaches have been proposed to create more reliable maps from noisy or sparse mapping data. Here, we demonstrate that deep learning can be used to compute phase maps and detect phase singularities in optical mapping videos of ventricular fibrillation, as well as in very noisy, low-resolution and extremely sparse simulated data of reentrant wave chaos mimicking catheter mapping data. The self-supervised deep learning approach is fundamentally different from classical phase mapping techniques. Rather than encoding a phase signal from time-series data, a deep neural network instead learns to directly associate phase maps and the positions of phase singularities with short spatio-temporal sequences of electrical data. We tested several neural network architectures, based on a convolutional neural network (CNN) with an encoding and decoding structure, to predict phase maps or rotor core positions either directly or indirectly via the prediction of phase maps and a subsequent classical calculation of phase singularities. Predictions can be performed across different data, with models being trained on one species and then successfully applied to another, or being trained solely on simulated data and then applied to experimental data. Neural networks provide a promising alternative to conventional phase mapping and rotor core localization methods. Future uses may include the analysis of optical mapping studies in basic cardiovascular research, as well as the mapping of atrial fibrillation in the clinical setting.

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“…The restricted Boltzmann machine is an unsupervised method, which can uncover the information underlying the data by learning the joint distribution with Bayesian theory 15 yet it is difficult to train the system without a fine partition-function solution 16 . Other neural network(NN)s like convolutional NN 17 or recurrent NN 18 are more universal in extracting local features of data and learning the relationship between elements with high accuracy 19,20 , despite they may face the problem of gradient exploding or gradient vanishing due to the complexity of dynamics. Therefore a single type of neutral network becomes incapable of solving complex population dynamics.…”

Section: Introductionmentioning

confidence: 99%

Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network

Li,

Xu,

Qian

et al. 2021

Preprint

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Solving physical problems by deep learning is accurate and efficient mainly accounting for the use of an elaborate neural network. We propose a novel hybrid network which integrates two different kinds of neural networks: LSTM and ResNet, in order to overcome the difficulty met in solving strongly-oscillating dynamics of the system's time evolution. By taking the double-well model as an example we show that our new method can benefit from a pre-learning and verification of the periodicity of frequency by using the LSTM network, simultaneously making a high-fidelity prediction about the whole dynamics of system with ResNet, which is impossibly achieved in the case of single network. Such a hybrid network can be applied for solving cooperative dynamics in a system with fast spatial or temporal modulations, promising for realistic oscillation calculations under experimental conditions.

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Deep-learning-assisted detection and termination of spiral and broken-spiral waves in mathematical models for cardiac tissue

Cited by 11 publications

References 49 publications

Learning phase transitions from regression uncertainty: a new regression-based machine learning approach for automated detection of phases of matter

Learning phase transitions from regression uncertainty: a new regression-based machine learning approach for automated detection of phases of matter

Rotor Localization and Phase Mapping of Cardiac Excitation Waves Using Deep Neural Networks

Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network

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