如何从心率数据中识别性别？使用 ALLSTAR 心率变异性大数据分析进行评估。
How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis.
机构信息
Tohoku University Data-driven Science and Artificial Intelligence, Kawauchi 41 Aoba-Ku, Sendai, 980-8576, Japan.
Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi Mizuho-Cho Mizuho-Ku, Nagoya, 467-8601, Japan.
出版信息
BMC Res Notes. 2023 Jan 19;16(1):5. doi: 10.1186/s13104-022-06270-2.
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.
目的
小型心电图仪和动态心电图仪可记录 24 小时或更长时间的心电图。我们研究了是否可以通过心电图验证性别，如果可以，其准确性如何。
结果
从超过 42 万人的心率数据中提取了 10 个维度的统计数据，并通过各种主要识别方法进行了性别识别。对年龄<50 岁和年龄≥50 岁的人群，比较了 Lasso、线性回归、SVM、随机森林、逻辑回归、k-means、Elastic Net。对于年龄<50 岁的人群，随机森林的最佳准确率为 0.681927。年龄<50 岁和年龄≥50 岁之间没有明显差异。虽然基于这些统计数据的判别结果具有统计学意义，但证实其准确性不足以确定个体的性别。
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