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.

翻译：我们提出了一种通过应用可解释性原理分析颅内压监测数据的新框架。颅内压监测数据收集自约翰霍普金斯医院的60名患者。数据被分割为独立的心动周期。我们训练了一个卷积神经网络，将每个心动周期分类为七种体位之一。通过提取神经网络注意力机制，识别出波形中的关键区域。本文进一步指出了未来探索方向。该框架提供了一种可扩展的方法，以深化对颅内压波形生理学与临床机制的理解，有望提升颅内压监测的诊断能力。