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How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis
OBJECTIVE: A small electrocardiograph and Holter electrocardiograph can record an electrocardiogram for 24 h or more. We examined whether gender could be verified from such an electrocardiogram and, if possible, how accurate it would be. RESULTS: Ten dimensional statistics were extracted from the he...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9850685/ https://www.ncbi.nlm.nih.gov/pubmed/36658657 http://dx.doi.org/10.1186/s13104-022-06270-2 |
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author | Kaneko, Itaru Hayano, Junichiro Yuda, Emi |
author_facet | Kaneko, Itaru Hayano, Junichiro Yuda, Emi |
author_sort | Kaneko, Itaru |
collection | PubMed |
description | OBJECTIVE: A small electrocardiograph and Holter electrocardiograph can record an electrocardiogram for 24 h or more. We examined whether gender could be verified from such an electrocardiogram and, if possible, how accurate it would be. RESULTS: Ten dimensional statistics were extracted from the heart rate data of more than 420,000 people, and gender identification was performed by various major identification methods. Lasso, linear regression, SVM, random forest, logistic regression, k-means, Elastic Net were compared, for Age < 50 and Age ≥ 50. The best Accuracy was 0.681927 for Random Forest for Age < 50. There are no consistent difference between Age < 50 and Age ≥ 50. Although the discrimination results based on these statistics are statistically significant, it was confirmed that they are not accurate enough to determine the gender of an individual. |
format | Online Article Text |
id | pubmed-9850685 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-98506852023-01-20 How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis Kaneko, Itaru Hayano, Junichiro Yuda, Emi BMC Res Notes Research Note OBJECTIVE: A small electrocardiograph and Holter electrocardiograph can record an electrocardiogram for 24 h or more. We examined whether gender could be verified from such an electrocardiogram and, if possible, how accurate it would be. RESULTS: Ten dimensional statistics were extracted from the heart rate data of more than 420,000 people, and gender identification was performed by various major identification methods. Lasso, linear regression, SVM, random forest, logistic regression, k-means, Elastic Net were compared, for Age < 50 and Age ≥ 50. The best Accuracy was 0.681927 for Random Forest for Age < 50. There are no consistent difference between Age < 50 and Age ≥ 50. Although the discrimination results based on these statistics are statistically significant, it was confirmed that they are not accurate enough to determine the gender of an individual. BioMed Central 2023-01-19 /pmc/articles/PMC9850685/ /pubmed/36658657 http://dx.doi.org/10.1186/s13104-022-06270-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Note Kaneko, Itaru Hayano, Junichiro Yuda, Emi How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis |
title | How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis |
title_full | How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis |
title_fullStr | How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis |
title_full_unstemmed | How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis |
title_short | How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis |
title_sort | how can gender be identified from heart rate data? evaluation using allstar heart rate variability big data analysis |
topic | Research Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9850685/ https://www.ncbi.nlm.nih.gov/pubmed/36658657 http://dx.doi.org/10.1186/s13104-022-06270-2 |
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