用注意力机制从颅内压波形中自动发现关键特征，助力临床诊断。
A Framework for Feature Discovery in Intracranial Pressure Monitoring Data Using Neural Network Attention
- 通过卷积网络识别心周期对应的体位，提取注意力权重定位波形关键区
- 基于60名患者的波形数据，成功区分七种体位下的特征模式
- 方法可拓展至其他生理信号分析，适合医学信号处理研究者
我们提出一种新框架，用于分析颅内压监测数据，结合可解释性原则。数据来自约翰霍普金斯医院的60名患者，按心脏周期进行分割。训练一个卷积神经网络，将每个心周期分类为七种体位之一。提取神经网络的注意力权重，识别波形中的感兴趣区域。进一步指明了潜在探索方向。该框架提供了一种可扩展的方法，有助于深入理解颅内压波形的生理与临床基础，可能提升颅内压监测的诊断能力。
原文摘要 · Abstract (English)
We present a novel framework for analyzing intracranial pressure monitoring data by applying interpretability principles. Intracranial pressure monitoring data was collected from 60 patients at Johns Hopkins. The data was segmented into individual cardiac cycles. A convolutional neural network was trained to classify each cardiac cycle into one of seven body positions. Neural network attention was extracted and was used to identify regions of interest in the waveform. Further directions for exploration are identified. This framework provides an extensible method to further understand the physiological and clinical underpinnings of the intracranial pressure waveform, which could lead to better diagnostic capabilities for intracranial pressure monitoring.
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