ArticleBiomedical engineering online2020
Optimal length of R-R interval segment window for Lorenz plot detection of paroxysmal atrial fibrillation by machine learning.
Article in Biomedical engineering online, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 12 citations in OpenAlex.
- R-R Interval Histogram-Based Deep Learning for 3-Class Atrial Fibrillation Screening in Garment-Type Wearable Holter Electrocardiogram Monitoring: Algorithm Development and Validation Study.JMIR medical informatics · 2026Article
- Detection of atrial fibrillation using a nonlinear Lorenz Scattergram and deep learning in primary care.BMC primary care · 2024Article
- How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.Cardiovascular research · 2021Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHeartbeat 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.
resultsIn 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.
conclusionsThis 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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