Title How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis.
Author Kaneko, Itaru; Hayano, Junichiro; Yuda, Emi
Journal BMC Res Notes Publication Year/Month 2023-Jan
PMID 36658657 PMCID PMC9850685
Affiliation + expend 1.Tohoku University Data-driven Science and Artificial Intelligence, Kawauchi 41 Aoba-Ku, Sendai, 980-8576, Japan.

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.

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    National Institute of Pathogen Biology, CAMS & PUMC, Bejing, China
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