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A Unified Pipeline for ECG Imaging Testing

Jess Tate, Eelco van Dam, Wilson Good, Jake Bergquist, Peter van Dam, Rob MacLeod
2019 2019 Computing in Cardiology Conference (CinC)  
The Unified ECGI Toolkit (UETK), combined with the EDGAR dataset, allows users to test and validate a vast array of parameters within each stage of the ECGI pipeline.  ...  The Consortium for ECG Imaging (CEI) has formed several collaborative projects to evaluate and improve technical aspects of Electrocardiographic Imaging (ECGI), but these efforts are not yet implemented  ...  Special thanks to the Consortium for ECG Imaging (CEI) for inspiring this work.  ... 
doi:10.22489/cinc.2019.437 pmid:32201705 pmcid:PMC7083590 fatcat:amiwbpvlkvbqvotjhaz6jlqjyi

Teaching a Machine to Diagnose a Heart Disease; Beginning from digitizing scanned ECGs to detecting the Brugada Syndrome (BrS) [article]

Simon Jaxy
2020 arXiv   pre-print
We propose a pipeline that reads in scanned images of ECGs, and transforms the encaptured signals to digital time-voltage data after several processing steps.  ...  The proposed pipeline distinguishes between three major types of ECG images and recreates each recorded lead signal.  ...  Pipeline The available records of ECGs for detecting BrS are limited and diverse in age, source and quality of the image.  ... 
arXiv:2009.01076v1 fatcat:fzoa43iy4jfjfax4bm4v2hbq4e

Robust deep learning pipeline for PVC beats localization

Bohdan Petryshak, Illia Kachko, Mykola Maksymenko, Oles Dobosevych
2021 Technology and Health Care  
We provide a Github1 repository for the reproduction of the results.  ...  The main objective is to address the drawbacks described above in the proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats.  ...  For each fold, we have used a 60/20/20 train/val/test split scheme.  ... 
doi:10.3233/thc-218045 pmid:33682784 pmcid:PMC8150659 fatcat:5qxqwuard5bnzhcuiehgoqlsxi

The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data

Lars Kasper, Steffen Bollmann, Andreea O. Diaconescu, Chloe Hutton, Jakob Heinzle, Sandra Iglesias, Tobias U. Hauser, Miriam Sebold, Zina-Mary Manjaly, Klaas P. Pruessmann, Klaas E. Stephan
2017 Journal of Neuroscience Methods  
SB acknowledges funding from a UQ Postdoctoral Research Fellowship grant and the National Imaging Facility.  ...  First, statistical inference based on the GLM (e.g., t-tests, F-tests) will have a valid account of the degrees of freedom.  ...  More detail on scan parameters and the image preprocessing of each dataset is provided in Table 1 . (1) Siemens 3T (ECG) ECG data were recorded as part of an fMRI study on a Siemens Trio 3 T MRI scanner  ... 
doi:10.1016/j.jneumeth.2016.10.019 pmid:27832957 fatcat:tdf3pj6kzrbtjfdoaqqdxeclo4

Sensor, Signal, and Imaging Informatics

W. Hsu, S. Park, Charles Kahn
2017 IMIA Yearbook of Medical Informatics  
The papers selected here offer a small glimpse of the high-quality scientific work published in 2016 in the domain of sensor, signal, and imaging informatics.  ...  Conclusion: The growing volume of signal and imaging data provides exciting new challenges and opportunities for research in medical informatics.  ...  Holmes for editorial guidance and support. We thank the reviewers for participating in the selection process.  ... 
doi:10.1055/s-0037-1606491 fatcat:oopaffel2nbklprgcc72febsne

Sensor, Signal, and Imaging Informatics in 2017

William Hsu, Thomas Deserno, Charles Kahn
2018 IMIA Yearbook of Medical Informatics  
Conclusion: The growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics.  ...  Objective: To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017.  ...  Holmes for editorial guidance and support. We thank the reviewers for participating in the selection process.  ... 
doi:10.1055/s-0038-1667084 pmid:30157513 fatcat:czexlwnds5c7douwel77cbsz2q

Mental State Assessment and Validation Using Personalized Physiological Biometrics

Aashish N. Patel, Michael D. Howard, Shane M. Roach, Aaron P. Jones, Natalie B. Bryant, Charles S. H. Robinson, Vincent P. Clark, Praveen K. Pilly
2018 Frontiers in Human Neuroscience  
The paper not only provides a unified pipeline for extracting a comprehensive mental state evaluation from a parsimonious set of sensors (only EEG and ECG), but also demonstrates the use of validation  ...  of k-fold cross-validation for discrete classification and regression testing for continuous prediction.  ...  DISCUSSION The paper not only provides a unified pipeline for extracting a comprehensive mental state evaluation from a parsimonious set FIGURE 8 | Threat detection task biometric evaluation.  ... 
doi:10.3389/fnhum.2018.00221 pmid:29910717 pmcid:PMC5992431 fatcat:zs5ipwmjsva6ndex47j4ltjcyi

NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search [article]

Renbo Tu, Mikhail Khodak, Nicholas Roberts, Ameet Talwalkar
2021 arXiv   pre-print
These results demonstrate the need for a benchmark such as NAS-Bench-360 to help develop NAS approaches that work well on a variety of tasks, a crucial component of a truly robust and automated pipeline  ...  Our experiments show that a modern NAS procedure designed for image classification can indeed find good architectures for tasks with other dimensionalities and learning objectives; however, the same method  ...  Acknowledgements We thank Maria-Florina Balcan for providing useful feedback. We also thank the Determined AI open-source community for its support.  ... 
arXiv:2110.05668v3 fatcat:re3dljjtifc3zpamcwiayb43xu

The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data

Gautier Koscielny, Gagarine Yaikhom, Vivek Iyer, Terrence F. Meehan, Hugh Morgan, Julian Atienza-Herrero, Andrew Blake, Chao-Kung Chen, Richard Easty, Armida Di Fenza, Tanja Fiegel, Mark Grifiths (+21 others)
2013 Nucleic Acids Research  
They thank Mary Todd Bergman and Spencer Phillips of the EBI for their assistance with figures for this article and Hayley Protheroe and the WTSI Team 109 for providing mouse images.  ...  their user feedback and participation in user experience sessions in which prototype interfaces were tested and improved.  ...  A stock image of an abnormal ear is provided for reference. a well-defined set of SOPs.  ... 
doi:10.1093/nar/gkt977 pmid:24194600 pmcid:PMC3964955 fatcat:67adsckyo5c3jo65sc7ecercrm

State‐of‐the‐Art Machine Learning Techniques Aiming to Improve Patient Outcomes Pertaining to the Cardiovascular System

Rahul Kumar Sevakula, Wan‐Tai M. Au‐Yeung, Jagmeet P. Singh, E. Kevin Heist, Eric M. Isselbacher, Antonis A. Armoundas
2020 Journal of the American Heart Association : Cardiovascular and Cerebrovascular Disease  
Satija et al 88 proposed a novel unified framework for automatic detection, localization, and classification of single and combined ECG noise.  ...  To train and test the ECG data, the R-T segments of the preprocessed ECG beats, each one having 200 samples, were scaled as images of 2569256, and then reproduced as an RGB image, before being used as  ... 
doi:10.1161/jaha.119.013924 pmid:32067584 pmcid:PMC7070211 fatcat:ynyv3rswfvgi7bxsmdbyapvrw4

SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis

Jose Ignacio Melero-Alegria, Manuel Cascon, Alfonso Romero, Pedro Pablo Vara, Manuel Barreiro-Perez, Victor Vicente-Palacios, Fernando Perez-Escanilla, Jesus Hernandez-Hernandez, Beatriz Garde, Sara Cascon, Ana Martin-Garcia, Elena Diaz-Pelaez (+12 others)
2019 BMJ Open  
For the first time, a detailed cardiovascular map showing the spatial distribution and a predictive machine learning system of different structural heart diseases and associated risk factors will be created  ...  learning methods that, although known to geography and statistics, need to become used for healthcare research and for political commitment to obtain resources and support effective public health programme  ...  We thank Philips Iberica and Obra Social 'La Caixa' for their support. We especially thank participants in the study and apologise for any inconvenience we could have caused.  ... 
doi:10.1136/bmjopen-2018-024605 pmid:30765403 pmcid:PMC6398793 fatcat:efps2df5sbhppeplkgmowafqci

Towards Personalized Cardiology: Multi-Scale Modeling of the Failing Heart

Elham Kayvanpour, Tommaso Mansi, Farbod Sedaghat-Hamedani, Ali Amr, Dominik Neumann, Bogdan Georgescu, Philipp Seegerer, Ali Kamen, Jan Haas, Karen S. Frese, Maria Irawati, Emil Wirsz (+9 others)
2015 PLoS ONE  
Novel multi-scale parameters derived from the model of all cases were analyzed and compared against clinical parameters, cardiac imaging, lab tests and survival scores to evaluate the explicative power  ...  of the model and its potential for better patient stratification.  ...  We show for the first time that estimated model parameters from imaging are very consistent with cardiac imaging, lab tests and prognosis scores.  ... 
doi:10.1371/journal.pone.0134869 pmid:26230546 pmcid:PMC4521877 fatcat:hk2rcknxofhj5jmkuwg3p4q7am

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry

Stefan Frässle, Eduardo A. Aponte, Saskia Bollmann, Kay H. Brodersen, Cao T. Do, Olivia K. Harrison, Samuel J. Harrison, Jakob Heinzle, Sandra Iglesias, Lars Kasper, Ekaterina I. Lomakina, Christoph Mathys (+9 others)
2021 Frontiers in Psychiatry  
pipelines for predictions about individual patients.  ...  While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use.  ...  Furthermore, unlike generic QC pipelines, the customization afforded by UniQC facilitates testing whether any given dataset shows a functional response relevant for the research question.  ... 
doi:10.3389/fpsyt.2021.680811 pmid:34149484 pmcid:PMC8206497 fatcat:ichfqltpdbdfflh2ozfk4lduda

Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation [article]

Ilkay Oksuz, James R. Clough, Bram Ruijsink, Esther Puyol Anton, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Andrew P. King, Julia A. Schnabel
2020 arXiv   pre-print
Segmenting anatomical structures in medical images has been successfully addressed with deep learning methods for a range of applications.  ...  In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity  ...  The first acquisition was a breath-hold image, cardiactriggered using the volunteer's ECG signal, which results in a high quality image.  ... 
arXiv:1910.05370v4 fatcat:bq6pftuktvhufnycpepolrz2u4

Overcoming Barriers to Quantification and Comparison of Electrocardiographic Imaging Methods: a Community-Based Approach

Jwala Dhamala, Jaume Coll-Font, Jess Tate, Maria de la Salud Guillem S�nchez, Dana Brooks, Linwei Wang, Rob MacLeod, Sandesh Ghimire
2017 2017 Computing in Cardiology Conference (CinC)  
This paper describes initial results of a project to address this challenge via a community-based approach organized by the Consortium for Electrocardiographic Imaging (CEI).  ...  There has been a recent upsurge in the development of electrocardiographic imaging (ECGI) methods, along with a significant increase in clinical application.  ...  Introduction Electrocardiographic imaging (ECGI) involves the development of methods to non-invasively image the electrical activity of the heart from electrocardiographic (ECG) data.  ... 
doi:10.22489/cinc.2017.370-289 pmid:29930953 pmcid:PMC6007992 dblp:conf/cinc/DhamalaCTSBWMG17 fatcat:yjhz7poprbfbljkgj2t6dtp3te
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