Detection of inferior myocardial infarction using shallow convolutional neural networks

2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) ◽

10.1109/r10-htc.2017.8289058 ◽

2017 ◽

Author(s):

Tahsin Reasat ◽

Celia Shahnaz

Keyword(s):

Myocardial Infarction ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Inferior Myocardial Infarction

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SLC-GAN: An Automated Myocardial Infarction Detection Model Based on Generative Adversarial Networks and Convolutional Neural Networks with Single-Lead Electrocardiogram Synthesis

Information Sciences ◽

10.1016/j.ins.2021.12.083 ◽

2022 ◽

Author(s):

Wenqiang Li ◽

Yuk Ming Tang ◽

Kai Ming Yu ◽

Suet To

Keyword(s):

Myocardial Infarction ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Generative Adversarial Networks ◽

Detection Model ◽

Model Based ◽

Adversarial Networks

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Neural networks for ECG diagnosis of inferior myocardial infarction

Proceedings of Computers in Cardiology Conference ◽

10.1109/cic.1993.378433 ◽

2002 ◽

Author(s):

B. Heden ◽

L. Edenbrandt ◽

W.K. Haisty ◽

O. Pahlm

Keyword(s):

Myocardial Infarction ◽

Neural Networks ◽

Inferior Myocardial Infarction

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Classifying Microscopic Acute and Old Myocardial Infarction Using Convolutional Neural Networks

American Journal of Forensic Medicine & Pathology ◽

10.1097/paf.0000000000000672 ◽

2021 ◽

Vol Publish Ahead of Print ◽

Author(s):

Jack Garland ◽

Mindy Hu ◽

Michael Duffy ◽

Kilak Kesha ◽

Charley Glenn ◽

...

Keyword(s):

Myocardial Infarction ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Old Myocardial Infarction

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Detecting and interpreting myocardial infarction using fully convolutional neural networks

Physiological Measurement ◽

10.1088/1361-6579/aaf34d ◽

2019 ◽

Vol 40 (1) ◽

pp. 015001 ◽

Author(s):

Nils Strodthoff ◽

Claas Strodthoff

Keyword(s):

Myocardial Infarction ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Fully Convolutional Neural Networks

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Use of artificial neural networks within deterministic logic for the computer ECG diagnosis of inferior myocardial infarction

Journal of Electrocardiology ◽

10.1016/s0022-0736(94)80090-1 ◽

1994 ◽

Vol 27 ◽

pp. 188-193 ◽

Author(s):

Ten-Fang Yang ◽

Brian Devine ◽

Peter W. Macfarlane

Keyword(s):

Myocardial Infarction ◽

Neural Networks ◽

Artificial Neural Networks ◽

Inferior Myocardial Infarction ◽

Artificial Neural

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Convolutional Neural Networks Based Diagnosis of Myocardial Infarction in Electrocardiograms

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) ◽

10.1109/icccis51004.2021.9397193 ◽

2021 ◽

Author(s):

Samir S. Yadav ◽

Sitaram B. More ◽

Shivajirao M. Jadhav ◽

Sanjay R. Sutar

Keyword(s):

Myocardial Infarction ◽

Neural Networks ◽

Convolutional Neural Networks

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MFB-LANN: A lightweight and updatable myocardial infarction diagnosis system based on convolutional neural networks and active learning

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2021.106379 ◽

2021 ◽

pp. 106379

Author(s):

Ziyang He ◽

Zhiyong Yuan ◽

Panfeng An ◽

Jianhui Zhao ◽

Bo Du

Keyword(s):

Myocardial Infarction ◽

Neural Networks ◽

Active Learning ◽

Convolutional Neural Networks ◽

Diagnosis System

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Right bundle branch block as a cause of false-negative ECG classification of inferior myocardial infarction*1

Journal of Cardiothoracic and Vascular Anesthesia ◽

10.1016/s1053-0770(99)90069-1 ◽

1999 ◽

Vol 32 (3) ◽

pp. 279-284

Author(s):

I GUSSAK

Keyword(s):

Myocardial Infarction ◽

Right Bundle Branch Block ◽

False Negative ◽

Inferior Myocardial Infarction ◽

Bundle Branch Block

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Evaluation von Bildparametern und eines convolutional neural networks in der FDG-PET/MR/CT zur Prädiktion des Gesamtüberlebens (OS) und des Therapieansprechens bei Patienten mit Melanom unter CIT.

10.1055/s-0040-1703340 ◽

2020 ◽

Author(s):

F Seith ◽

J Vogel ◽

C la Fougère ◽

T Küstner ◽

K Nikolaou ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

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Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

10.33107/ubt-ic.2018.75 ◽

2018 ◽

Author(s):

George Symeonidis ◽

Peter P. Groumpos ◽

Evangelos Dermatas

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Traffic Lights ◽

Adverse Conditions

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