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人工智能增强了可穿戴设备的发作检测

Shuang Yu¹, Rima El Atrache², Jianbin Tang¹

¹IBM Australia, Melbourne, Victoria, Australia.

|September 16, 2023

概括

可穿戴传感器可以使用深度学习模型检测广泛的发作类型,为脑电图 (EEG) 监测提供一种不那么侵入性的替代方案. 这项技术为改善病护理提供了准确,时间敏感的数据.

科学领域:

生物医学工程生物医学工程
神经科学是一个神经科学.
数字健康数字健康

背景情况:

的监测传统上依赖于电脑电图 (EEG),这可能是侵入性的.
可穿戴式传感器为连续发作检测提供了一个不那么侵入性的替代方案.

研究的目的:

开发和评估使用可穿戴生物传感器数据检测各种发作类型的深度学习模型.
评估不同传感器模式和模型架构用于发作检测的性能.

主要方法:

从166名患者的手腕/脚传感器收集了皮电活动,加速度计 (ACC) 和血液体积脉冲 (BVP).
应用卷积神经网络 (CNN) 和CNN-long短期记忆 (LSTM) 模型来检测发作.
评估了28种发作类型的模型,使用灵敏度,错误阳性率和检测延迟.

主要成果:

使用合并ACC和BVP数据的CNN-LSTM模型实现了83.9%的灵敏度和35.3%的错误阳性率.
在28种发作类型中,有19种可以通过至少一种数据模式检测到,曲线下的面积> 0.8.8.
穿戴式传感器数据融合在发作检测方面表现优于个体模式.

结论:

深度学习模型有效地使用非侵入性可穿戴传感器检测各种发作类型.
这种方法代表了向更容易获得和以患者为中心的监测的范式转变.
结合了可泛化的算法与个性化的发作检测,以提高临床效用.

关键词:

深度学习是一种深度学习.是一种.发作检测检测发作检测发作发作这些可穿戴设备可以穿戴.

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