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Optimal length of R–R interval segment window for Lorenz plot detection of paroxysmal atrial fibrillation by machine learning

BACKGROUND: Heartbeat interval Lorenz plot (LP) imaging is a promising method for detecting atrial fibrillation (AF) in long-term monitoring, but the optimal segment window length for the LP images is unknown. We examined the performance of AF detection by LP images with different segment window len...

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Autores principales: Kisohara, Masaya, Masuda, Yuto, Yuda, Emi, Ueda, Norihiro, Hayano, Junichiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298777/
https://www.ncbi.nlm.nih.gov/pubmed/32546178
http://dx.doi.org/10.1186/s12938-020-00795-y
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author Kisohara, Masaya
Masuda, Yuto
Yuda, Emi
Ueda, Norihiro
Hayano, Junichiro
author_facet Kisohara, Masaya
Masuda, Yuto
Yuda, Emi
Ueda, Norihiro
Hayano, Junichiro
author_sort Kisohara, Masaya
collection PubMed
description BACKGROUND: Heartbeat interval Lorenz plot (LP) imaging is a promising method for detecting atrial fibrillation (AF) in long-term monitoring, but the optimal segment window length for the LP images is unknown. We examined the performance of AF detection by LP images with different segment window lengths by machine learning with convolutional neural network (CNN). LP images with a 32 × 32-pixel resolution of non-overlapping segments with lengths between 10 and 500 beats were created from R–R intervals of 24-h ECG in 52 patients with chronic AF and 58 non-AF controls as training data and in 53 patients with paroxysmal AF and 52 non-AF controls as test data. For each segment window length, discriminant models were made by fivefold cross-validation subsets of the training data and its classification performance was examined with the test data. RESULTS: In machine learning with the training data, the averages of cross-validation scores were 0.995 and 0.999 for 10 and 20-beat LP images, respectively, and > 0.999 for 50 to 500-beat images. The classification of test data showed good performance for all segment window lengths with an accuracy from 0.970 to 0.988. Positive likelihood ratio for detecting AF segments, however, showed a convex parabolic curve linear relationship to log segment window length and peaked at 85 beats, while negative likelihood ratio showed monotonous increase with increasing segment window length. CONCLUSIONS: This study suggests that the optimal segment window length that maximizes the positive likelihood ratio for detecting paroxysmal AF with 32 × 32-pixel LP image is 85 beats.
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spelling pubmed-72987772020-06-17 Optimal length of R–R interval segment window for Lorenz plot detection of paroxysmal atrial fibrillation by machine learning Kisohara, Masaya Masuda, Yuto Yuda, Emi Ueda, Norihiro Hayano, Junichiro Biomed Eng Online Research BACKGROUND: Heartbeat interval Lorenz plot (LP) imaging is a promising method for detecting atrial fibrillation (AF) in long-term monitoring, but the optimal segment window length for the LP images is unknown. We examined the performance of AF detection by LP images with different segment window lengths by machine learning with convolutional neural network (CNN). LP images with a 32 × 32-pixel resolution of non-overlapping segments with lengths between 10 and 500 beats were created from R–R intervals of 24-h ECG in 52 patients with chronic AF and 58 non-AF controls as training data and in 53 patients with paroxysmal AF and 52 non-AF controls as test data. For each segment window length, discriminant models were made by fivefold cross-validation subsets of the training data and its classification performance was examined with the test data. RESULTS: In machine learning with the training data, the averages of cross-validation scores were 0.995 and 0.999 for 10 and 20-beat LP images, respectively, and > 0.999 for 50 to 500-beat images. The classification of test data showed good performance for all segment window lengths with an accuracy from 0.970 to 0.988. Positive likelihood ratio for detecting AF segments, however, showed a convex parabolic curve linear relationship to log segment window length and peaked at 85 beats, while negative likelihood ratio showed monotonous increase with increasing segment window length. CONCLUSIONS: This study suggests that the optimal segment window length that maximizes the positive likelihood ratio for detecting paroxysmal AF with 32 × 32-pixel LP image is 85 beats. BioMed Central 2020-06-16 /pmc/articles/PMC7298777/ /pubmed/32546178 http://dx.doi.org/10.1186/s12938-020-00795-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Kisohara, Masaya
Masuda, Yuto
Yuda, Emi
Ueda, Norihiro
Hayano, Junichiro
Optimal length of R–R interval segment window for Lorenz plot detection of paroxysmal atrial fibrillation by machine learning
title Optimal length of R–R interval segment window for Lorenz plot detection of paroxysmal atrial fibrillation by machine learning
title_full Optimal length of R–R interval segment window for Lorenz plot detection of paroxysmal atrial fibrillation by machine learning
title_fullStr Optimal length of R–R interval segment window for Lorenz plot detection of paroxysmal atrial fibrillation by machine learning
title_full_unstemmed Optimal length of R–R interval segment window for Lorenz plot detection of paroxysmal atrial fibrillation by machine learning
title_short Optimal length of R–R interval segment window for Lorenz plot detection of paroxysmal atrial fibrillation by machine learning
title_sort optimal length of r–r interval segment window for lorenz plot detection of paroxysmal atrial fibrillation by machine learning
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298777/
https://www.ncbi.nlm.nih.gov/pubmed/32546178
http://dx.doi.org/10.1186/s12938-020-00795-y
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