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

arXiv: https://arxiv.org/abs/2601.07691 · HTML: https://mlsanity.com/paper/2601.07691/

## 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.

## Builds on (most cited first, 16 of 16)

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- [Classification of Time-Series Images Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1710.00886/) (2017)
- [Towards Similarity-Aware Time-Series Classification](https://mlsanity.com/paper/2201.01413/) (2022)
- [RTFN: A Robust Temporal Feature Network for Time Series Classification](https://mlsanity.com/paper/2011.11829/) (2020)
- [How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning](https://mlsanity.com/paper/2407.05664/) (2024)
- [LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification](https://mlsanity.com/paper/2301.04838/) (2023)

## 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.