mlsanityData to 14 Oct 2026

A Framework for Feature Discovery in Intracranial Pressure Monitoring Data Using Neural Network Attention

arXiv 2601.07691 ↗First posted 12 Jan 2026q-bio.QM cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 12 Jan 2026.

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References · 16

  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
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4 more
  1. 2022Towards Similarity-Aware Time-Series Classificationcited by 13
  2. 2020RTFN: A Robust Temporal Feature Network for Time Series Classificationcited by 11
  3. 2024How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learningcited by 6
  4. 2023LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classificationcited by 3

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