A Framework for Feature Discovery in Intracranial Pressure Monitoring Data Using Neural Network Attention
This work addresses the need for better diagnostic capabilities in intracranial pressure monitoring for medical professionals, though it appears incremental as it applies existing interpretability methods to a specific dataset.
The researchers tackled the problem of analyzing intracranial pressure monitoring data by developing a framework that uses neural network attention to identify regions of interest in waveforms, achieving classification of cardiac cycles into seven body positions with data from 60 patients.
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.