| アイテムタイプ |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2023-09-28 |
| 書誌情報 |
en : Bioengineering
巻 9,
号 9,
p. 430,
ページ数 13,
発行日 2022-09-01
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| タイトル |
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|
タイトル |
End-to-End Convolutional Neural Network Model to Detect and Localize Myocardial Infarction Using 12-Lead ECG Images without Preprocessing |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
myocardial infarction |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
electrocardiogram |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
12-lead ECG |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
convolutional neural network |
| 資源タイプ |
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|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
内山, 竜之介
岡田, 吉史
蠣崎, 龍也
富岡, 碩人
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| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
In recent years, many studies have proposed automatic detection and localization techniques for myocardial infarction (MI) using the 12-lead electrocardiogram (ECG). Most of them applied preprocessing to the ECG signals, e.g., noise removal, trend removal, beat segmentation, and feature selection, followed by model construction and classification based on machine-learning algorithms. The selection and implementation of preprocessing methods require specialized knowledge and experience to handle ECG data. In this paper, we propose an end-to-end convolutional neural network model that detects and localizes MI without such complicated multistep preprocessing. The proposed model executes comprehensive learning for the waveform features of unpreprocessed raw ECG images captured from 12-lead ECG signals. We evaluated the classification performance of the proposed model in two experimental settings: ten-fold cross-validation where ECG images were split randomly, and two-fold cross-validation where ECG images were split into one patient and the other patients. The experimental results demonstrate that the proposed model obtained MI detection accuracies of 99.82% and 93.93% and MI localization accuracies of 99.28% and 69.27% in the first and second settings, respectively. The performance of the proposed method is higher than or comparable to that of existing state-of-the-art methods. Thus, the proposed model is expected to be an effective MI diagnosis tool that can be used in intensive care units and as wearable technology. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
MDPI |
|
言語 |
en |
| DOI |
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関連タイプ |
isIdenticalTo |
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|
識別子タイプ |
DOI |
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関連識別子 |
10.3390/bioengineering9090430 |
| PMID |
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|
関連タイプ |
isIdenticalTo |
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|
識別子タイプ |
PMID |
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関連識別子 |
36134976 |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2306-5354 |
| 権利 |
|
|
権利情報 |
© 2022 by the authors. Licensee MDPI |
|
言語 |
ja |
| 著者版フラグ |
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|
出版タイプ |
VoR |
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出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
bioengineering-09-00430-v2.pdf (1.3 MB)
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