Year: 2026
Type: conference-paper
Abstract: Bladder pressure monitoring systems are increasingly vital in diagnosing and managing urinary tract dysfunction. Existing solutions rely heavily on handcrafted features and shallow classifiers, limiting their adaptability to complex signal dynamics. We propose a one-layer streaming transformer model for real-time classification of bladder pressure states, operating on wavelet-transformed representations of raw time-series data. Our model incorporates temporal multi-head self-attention and state caching, enabling efficient online inference with high adaptability. Trained on a dataset of 91 patients with 20,000-80,000 samples each, our method demonstrates improved accuracy, higher energy- and latency-efficiency. Implementation considerations for edge deployment on low-power hardware, such as edge graphical processing units (GPU) and micro-controllers, are also discussed.
Authors: Chengwei Zhou, Steve Majerus, Gourav Datta
Institution: Case Western Reserve University
Language: English
Subfield: Urology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4415336068
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