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Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

Artzai Picon, Unai Irusta, Aitor Álvarez-Gila, Elisabete Aramendi, Felipe Alonso-Atienza, Carlos Figuera, Unai Ayala, Estibaliz Garrote, Lars Wik, Jo Kramer-Johansen, Trygve Eftestøl, Steve Lin
2019 PLoS ONE  
Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task.  ...  Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest.  ...  Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network.  ... 
doi:10.1371/journal.pone.0216756 pmid:31107876 pmcid:PMC6527215 fatcat:bui673jh35bjxlxcgv3tolgmym

Artificial intelligence in the diagnosis and management of arrhythmias

Venkat D Nagarajan, Su-Lin Lee, Jan-Lukas Robertus, Christoph A Nienaber, Natalia A Trayanova, Sabine Ernst
2021 European Heart Journal  
Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP.  ...  In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care.  ...  the detection of shockable and non-shockable rhythms using ML.  ... 
doi:10.1093/eurheartj/ehab544 pmid:34392353 pmcid:PMC8497074 fatcat:n23f4f5x2fhivcccw5cqkejhbi

A Review on Deep Learning Methods for ECG Arrhythmia Classification

Zahra Ebrahimi, Mohammad Loni, Masoud Daneshtalab, Arash Gharehbaghi
2020 Expert Systems with Applications: X  
Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional 515 neural network. Future Generation Computer Systems, 79, 952-959. Acharya, U.  ...  proposed a multiple-feature-branch Convolutional Neural Network (MFB-CNN) for 315 automated myocardial (MI) detection and localization using ECG.  ...  ., 2016) 2015 152 convolutional layers + 3 Increase network depth and provide a method fully-connected layers to prevent gradient saturation.  ... 
doi:10.1016/j.eswax.2020.100033 fatcat:gpdtrhy2ejcl3cqpdgjctworje

Sparsely Activated Networks: A new method for decomposing and compressing data [article]

Paschalis Bizopoulos
2021 arXiv   pre-print
We compare SANs using the five previously defined activation functions on a variety of datasets (Physionet, UCI-epilepsy, MNIST, FMNIST) and show that models that are selected using φ have small description  ...  We lastly present Sparsely Activated Networks (SANs) that consist of kernels with shared weights that, during encoding, are convolved with the input and then passed through a sparse activation function  ...  Automated identification of shockable and non-shockable life-threatening ventricular ar- rhythmias using convolutional neural network. Future Generation Computer Systems, 79:952-959, 2018.  ... 
arXiv:1911.00400v2 fatcat:4gdsy2iywvgitdugodkdfqdzg4

ESICM LIVES 2021: Part 1

2021 Intensive Care Medicine Experimental  
This study showed that a high-protein intake and resistive exercise increase the survival rate and the physical quality of life of critically ill patients.  ...  We used indirect calorimetry to determine energy expenditure and guide caloric provision to the patients randomized to the high protein and early exercise (HPE) group and the control group.  ...  Firstly, to extract hidden patterns in the physiological state of a patient that might reflect the oncoming onset of infection, we trained a convolutional neural network (CNN) using high-resolution signals  ... 
doi:10.1186/s40635-021-00413-8 pmid:34633565 fatcat:ky75zcwr45btlchccmwuwn77nm

Sparsely Activated Networks: A new method for decomposing and compressing data [article]

Paschalis Bizopoulos, National Technological University Of Athens, National Technological University Of Athens
2020
Automated identification of shockable and non-shockable life-threatening ventricular ar- rhythmias using convolutional neural network. Future Generation Computer Systems, 79:952-959, 2018.  ...  Automated detection of arrhythmias using dif- ferent intervals of tachycardia ecg segments with convolutional neural network. Information sciences, 405:81-90, 2017.  ... 
doi:10.26240/heal.ntua.17607 fatcat:afzmycgzbjhcpgkuz56q6oj75m