Explaining deep neural networks for knowledge discovery in electrocardiogram analysis
Abstract Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation...
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2021-05-01
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Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-021-90285-5 |
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doaj-6f83e0ec46bc41759fd87f303ddffe2e2021-05-30T11:38:38ZengNature Publishing GroupScientific Reports2045-23222021-05-0111111110.1038/s41598-021-90285-5Explaining deep neural networks for knowledge discovery in electrocardiogram analysisSteven A. Hicks0Jonas L. Isaksen1Vajira Thambawita2Jonas Ghouse3Gustav Ahlberg4Allan Linneberg5Niels Grarup6Inga Strümke7Christina Ellervik8Morten Salling Olesen9Torben Hansen10Claus Graff11Niels-Henrik Holstein-Rathlou12Pål Halvorsen13Mary M. Maleckar14Michael A. Riegler15Jørgen K. Kanters16SimulaMetUniversity of CopenhagenSimulaMetUniversity of CopenhagenUniversity of CopenhagenUniversity of CopenhagenUniversity of CopenhagenSimulaMetUniversity of CopenhagenUniversity of CopenhagenUniversity of CopenhagenAalborg UniversityUniversity of CopenhagenSimulaMetSimula Research LaboratorySimulaMetUniversity of CopenhagenAbstract Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.https://doi.org/10.1038/s41598-021-90285-5 |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Steven A. Hicks Jonas L. Isaksen Vajira Thambawita Jonas Ghouse Gustav Ahlberg Allan Linneberg Niels Grarup Inga Strümke Christina Ellervik Morten Salling Olesen Torben Hansen Claus Graff Niels-Henrik Holstein-Rathlou Pål Halvorsen Mary M. Maleckar Michael A. Riegler Jørgen K. Kanters |
spellingShingle |
Steven A. Hicks Jonas L. Isaksen Vajira Thambawita Jonas Ghouse Gustav Ahlberg Allan Linneberg Niels Grarup Inga Strümke Christina Ellervik Morten Salling Olesen Torben Hansen Claus Graff Niels-Henrik Holstein-Rathlou Pål Halvorsen Mary M. Maleckar Michael A. Riegler Jørgen K. Kanters Explaining deep neural networks for knowledge discovery in electrocardiogram analysis Scientific Reports |
author_facet |
Steven A. Hicks Jonas L. Isaksen Vajira Thambawita Jonas Ghouse Gustav Ahlberg Allan Linneberg Niels Grarup Inga Strümke Christina Ellervik Morten Salling Olesen Torben Hansen Claus Graff Niels-Henrik Holstein-Rathlou Pål Halvorsen Mary M. Maleckar Michael A. Riegler Jørgen K. Kanters |
author_sort |
Steven A. Hicks |
title |
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis |
title_short |
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis |
title_full |
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis |
title_fullStr |
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis |
title_full_unstemmed |
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis |
title_sort |
explaining deep neural networks for knowledge discovery in electrocardiogram analysis |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2021-05-01 |
description |
Abstract Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features. |
url |
https://doi.org/10.1038/s41598-021-90285-5 |
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