使用心电图信号改善年龄预测:对生活方式影响的洞察力
Jiahao Wang1, Li Yan2, Yaqi Liu1
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Journal of electrocardiology
|July 27, 2025
概括
这项研究使用深度学习模型和心电图 (ECG) 准确估计心脏年龄. 运动和吸烟等生活方式因素显著影响心脏衰老,为心血管健康提供了新的见解.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 从心电图 (ECG) 估计心脏年龄,可以了解心血管健康.
- 心脏年龄和时间年龄之间的差异可能表明血管衰老和风险增加.
- 提高心脏年龄预测的精度和可解释性是一个持续的挑战.
研究的目的:
- 开发新的预处理技术,以提高基于心电图的心脏年龄预测的准确性.
- 研究生活方式因素对心脏衰老的影响.
- 提高心血管健康评估的深度学习模型的可解释性.
主要方法:
- 使用巴特沃思带通波器和超模触点转换处理的心电图信号.
- 引入了耐损训练机制,以减轻标签噪声.
- 在PTB-XL数据集上训练模型,并分析了PLHDB和AISDB数据库中的生活方式数据.
主要成果:
- 在PTB-XL数据集上,心脏年龄预测的平均绝对误差 (MAE) 为6.76年.
- 定期炼与心脏衰老减少1.31年有关.
- 吸烟和过度饮酒与心脏年龄分别增加3.67岁和1.22岁有关.
结论:
- 基于心电图的深度学习模型提供了精确的心脏年龄估计.
- 证明了这些模型在阐明生活方式对心血管健康的影响中的实用性.
- 突出了个性化心血管风险评估和干预策略的潜力.
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