Retracted: ECG Signal Detection and Classification of Heart Rhythm Diseases Based on ResNet and LSTM

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Bibliographic Details
Main Author: Advances in Mathematical Physics
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:Advances in Mathematical Physics
Online Access:http://dx.doi.org/10.1155/2024/9890458
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author Advances in Mathematical Physics
author_facet Advances in Mathematical Physics
author_sort Advances in Mathematical Physics
collection DOAJ
format Article
id doaj-art-8a4e4539d82d4ecf86533165d56fb90e
institution OA Journals
issn 1687-9139
language English
publishDate 2024-01-01
publisher Wiley
record_format Article
series Advances in Mathematical Physics
spelling doaj-art-8a4e4539d82d4ecf86533165d56fb90e2025-08-20T02:09:18ZengWileyAdvances in Mathematical Physics1687-91392024-01-01202410.1155/2024/9890458Retracted: ECG Signal Detection and Classification of Heart Rhythm Diseases Based on ResNet and LSTMAdvances in Mathematical Physicshttp://dx.doi.org/10.1155/2024/9890458
spellingShingle Advances in Mathematical Physics
Retracted: ECG Signal Detection and Classification of Heart Rhythm Diseases Based on ResNet and LSTM
Advances in Mathematical Physics
title Retracted: ECG Signal Detection and Classification of Heart Rhythm Diseases Based on ResNet and LSTM
title_full Retracted: ECG Signal Detection and Classification of Heart Rhythm Diseases Based on ResNet and LSTM
title_fullStr Retracted: ECG Signal Detection and Classification of Heart Rhythm Diseases Based on ResNet and LSTM
title_full_unstemmed Retracted: ECG Signal Detection and Classification of Heart Rhythm Diseases Based on ResNet and LSTM
title_short Retracted: ECG Signal Detection and Classification of Heart Rhythm Diseases Based on ResNet and LSTM
title_sort retracted ecg signal detection and classification of heart rhythm diseases based on resnet and lstm
url http://dx.doi.org/10.1155/2024/9890458
work_keys_str_mv AT advancesinmathematicalphysics retractedecgsignaldetectionandclassificationofheartrhythmdiseasesbasedonresnetandlstm