It is generally assumed that the analysis of intracranial pressure (ICP) waveforms could be used for the detection of multiple cerebral pathophysiologies. A main obstacle for the analysis of ICP waveforms is given by the large variation of their generating signal, the arterial blood pressure (ABP). Using extended principal component analysis (PCA) we show that it is possible to distinguish between ICP waveforms generated by pathological ABP waveforms, e.g. in the case of a heart failure, without loosing information about the state of the cerebral compliance. We also create a dataset for ICP pulse classification that can be used to train models to distinguish between an intact and diminished cerebral compliance, as a function of the ICP-generating ABP pulse. As a baseline for classification we build a fully convolutional network (FCN) that reaches high performance with relatively few data.

中文翻译：

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用于病理生理学颅内压波形分类的全卷积网络

一般认为颅内压 (ICP) 波形分析可用于检测多种脑病理生理。分析 ICP 波形的一个主要障碍是其生成信号——动脉血压 (ABP) 的巨大变化。使用扩展主成分分析 (PCA)，我们表明可以区分由病理 ABP 波形生成的 ICP 波形，例如在心力衰竭的情况下，而不会丢失有关脑顺应性状态的信息。我们还创建了一个用于 ICP 脉冲分类的数据集，可用于训练模型以区分完整和降低的脑顺应性，作为 ICP 生成 ABP 脉冲的函数。