通过机器学习和可穿戴心电图监测预测恐慌发作患者的恐慌发作:模型开发和验证研究
Hayoung Oh1, Hunmin Do2, Chaehyun Maeng3
1Department of Artificial Intelligence Convergence, Sungkyunkwan University, 25-2, Seonggyun-gwan-ro, Jongno-gu, Seoul, 03063, Republic of Korea, 82 10-5389-5996.
Journal of medical Internet research
|October 15, 2025
概括
这项研究开发了一种使用可穿戴式心电图和心理数据的多式深度学习模型,用于准确预测恐慌发作. 人工智能系统对早期预警系统和数字心理健康干预非常有希望.
科学领域:
- 数字精神病学数字精神病学
- 情感计算是一种情感计算.
- 人工智能在心理健康中的作用
背景情况:
- 由于可变的生理反应和主观评估,预测恐慌发作具有挑战性.
- 目前的方法在识别恐慌发作时缺乏准确性.
- 需要客观的实时监控解决方案.
研究的目的:
- 为增强恐慌发作预测开发多式联络深度学习框架.
- 将实时生理信号 (ECG) 与心理数据相结合.
- 提高恐慌发作检测系统的准确性和可靠性.
主要方法:
- 适应了ConvNetQuake架构,用于抽取时间ECG图案.
- 在广泛的ECG数据集上预先训练和微调模型.
- 包括心理评估 (DSM-IV,PDSS) 作为辅助输入.
- 通过使用标准绩效指标评估多式联运框架.
主要成果:
- 该模型实现了71.43%的准确性,83.72%的精度,70.59%的回忆率和76.60%的F1得分.
- 检测到与恐慌发作相关的心率变化异常.
- 多模式集成在预测可靠性方面显著优于单模式方法.
结论:
- 基于可穿戴设备的恐慌发作早期预警系统是可行的.
- 该方法支持即时数字干预.
- 可穿戴的人工智能对推进数字精神病学和情感计算具有重大潜力.
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