通过有效和高效的广泛学习模型进行连续无袖血压估计
IEEE journal of biomedical and health informatics
|September 1, 2025
概括
一个新的广泛学习模型 (BLM) 能够使用可穿戴传感器高效地估计血压 (BP). 这种方法显著降低了计算成本,并提高了持续BP监测的培训效率.
科学领域:
- 生物医学工程
- 机器学习
- 心血管健康
背景情况:
- 高血压是心血管疾病和死亡的一个关键风险因素.
- 在高血压管理中,可访问的血压 (BP) 测量非常重要.
- 目前的深度学习模型 (DLM) 面临着计算方面的挑战.
研究的目的:
- 引入一个端到端的广泛学习模型 (BLM) 进行高效的无袖血压估计.
- 与DLM相比,减少计算复杂性和提高培训效率.
- 探索提高记忆效率和灵活性的增量学习模式.
主要方法:
- 开发了一种广泛的学习模型 (BLM),可以增加网络宽度而不是深度.
- 实现了增量学习模式 (IBLM) 以实现横向可扩展性和内存效率.
- 在UCI数据库中对大型数据集 (403.67小时) 进行验证.
主要成果:
- 标准BLM (SBLM) 在动脉血压 (ABP) 波形估计中达到11.72 mmHg的平均绝对误差,与DLM相比.
- 与LSTM和1D-CNN等DLM相比,SBLM的培训效率提高了25.20倍.
- 系统性血压 (SBP) 的MAE值为3. 04mmHg,腹压 (DBP) 的MAE值为2. 57mmHg.
结论:
- BLM提供了一种有效且计算效率高的无袖式BP估计解决方案.
- 增量BLM (IBLM) 提供了一种可扩展的方法,用于减少存储需求的持续监控.
- 拟议的BLM在血压监测中具有个性化实时医疗应用的巨大潜力.
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