在消费者智能手表上自动丢失脉冲检测
Kamal Shah1, Anran Wang1, Yiwen Chen1
1Google Research, Mountain View, CA, USA.
Nature
|February 26, 2025
概括
智能手表的算法可以检测心脏骤停的关键信号. 这项技术旨在通过使紧急医疗响应更快,同时尽量减少错误警报来提高生存率.
科学领域:
- 生物医学工程
- 心脏病学
- 人工智能
背景情况:
- 医院外心脏骤停是紧急情况, 迅速干预对于生存至关重要.
- 突发性心脏骤停的存活率很低, 这强调了及时检测和医疗援助的必要性.
- 自动检测系统必须平衡灵敏度,尽量减少假阳性,以避免压倒紧急服务.
研究的目的:
- 开发和验证智能手表的机器学习算法,
- 在适合社会部署的规模上评估算法的性能.
- 评估可穿戴生物传感器在改善心脏骤停生存能力方面的潜力.
主要方法:
- 利用智能手表的光电显微镜 (PPG) 信号来识别类似于心脏骤停 (心室动) 和诱导动脉封闭的模式.
- 使用外围无脉动和现实世界 (自由生活) 条件的数据开发和验证了脉动检测算法.
- 在模拟和现实环境中对端到端算法进行了前性评估.
主要成果:
- 开发的算法表明,诱导性无脉动时的可穿戴PPG信号与心室动时的信号相似.
- 预期评估显示,每21.67个使用年有1个意外紧急呼叫率.
- 在前性心脏骤停模拟模型中,该算法获得了67.23%的灵敏度.
结论:
- 在智能手表中集成的多模式机器学习算法,
- 这项技术有可能在社会范围内部署, 提供改善心脏骤停的机会.
- 该系统在检测关键事件和尽量减少虚假警报的社会成本之间取得了平衡.
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