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ReHeartNet: Reconstruct Electrocardiogram From Photoplethysmography by Using Dense Connected Deep Learning Model

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Bibliographic Details
Published in:IEEE Open Journal of Engineering in Medicine and Biology
Format: Online Article RSS Article
Published: 2026
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container_title IEEE Open Journal of Engineering in Medicine and Biology
description
discipline_display Biology
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format Online Article
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genre Journal Article
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institution FRELIP
journal_source_facet IEEE Open Journal of Engineering in Medicine and Biology
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle ReHeartNet: Reconstruct Electrocardiogram From Photoplethysmography by Using Dense Connected Deep Learning Model
Biology
General
Biology
sub_discipline_display General
sub_discipline_facet General
subject_display Biology
General
Biology
Biology
General
Biology
subject_facet Biology
General
Biology
title ReHeartNet: Reconstruct Electrocardiogram From Photoplethysmography by Using Dense Connected Deep Learning Model
title_auth ReHeartNet: Reconstruct Electrocardiogram From Photoplethysmography by Using Dense Connected Deep Learning Model
title_full ReHeartNet: Reconstruct Electrocardiogram From Photoplethysmography by Using Dense Connected Deep Learning Model
title_fullStr ReHeartNet: Reconstruct Electrocardiogram From Photoplethysmography by Using Dense Connected Deep Learning Model
title_full_unstemmed ReHeartNet: Reconstruct Electrocardiogram From Photoplethysmography by Using Dense Connected Deep Learning Model
title_short ReHeartNet: Reconstruct Electrocardiogram From Photoplethysmography by Using Dense Connected Deep Learning Model
title_sort reheartnet: reconstruct electrocardiogram from photoplethysmography by using dense connected deep learning model
topic Biology
General
Biology
url http://ieeexplore.ieee.org/document/11419800