Can Less Be More? Benchmarking Lightweight Models Against State-of-the-Art Deep Learning Architectures for Deployable Seizure Detection

Isaiah Essien, Donna-lee Ginsberg, Jesse Thornburg

Keywords: Parsimonious Learning, Mobile Health, Seizure Detection, TensorFlow Lite, Deep Learning, Resource-Constrained Deployment, Global Health Equity

TL;DR: This work presents a 24KB deep learning model that uses consumer smartphones to detect epileptic seizures with 93% sensitivity, demonstrating parsimony in design and application.

Abstract: Over the past decades, emerging research in seizure detection has highlighted the critical need for resource-constrained, deployable models that can operate in low-infrastructure environments. Seizure detection models that achieve high accuracy on benchmarks rarely run on the hardware available in low-resource contexts like developing countries, where epilepsy takes the heaviest toll. This work addresses the fundamental disconnect between model performance and real-world deployability by developing and evaluating parsimonious deep learning architectures for real-time epileptic seizure detection on consumer smartphones. This study systematically develops and compares two lightweight models: a Convolutional Neural Network with Gated Recurrent Units (CNN-GRU) and a 1D Convolutional Network with Multi-Head Attention (1D CNN-MHA). The optimal model is selected for both detection performance and deployment feasibility. The parsimonious 1D CNN-MHA model achieved superior performance with 96% accuracy, 93% sensitivity, and 0.99 AUC, outperforming the CNN-GRU model in both accuracy and sensitivity. Benchmarking against state-of-the-art models reveals a persistent deployment gap: while "lightweight" models in the literature lack deployment evidence, and high-accuracy models are bound to server-grade hardware, the 23.8 KB TensorFlow Lite model bridges this gap by delivering competitive accuracy while running in real-time on mid-range Android devices. Crucially, these results establish deployment feasibility rather than clinical validity: the system demonstrates that seizure-like motion patterns can be reliably discriminated under strict on-device constraints using commodity smartphones. The findings therefore support the principle that carefully designed parsimonious architectures can approach the performance of heavier models while remaining executable in real-world edge environments. This work can be interpreted as a feasibility study of deployability designed to enable subsequent large-scale clinical validation rather than as a population-level diagnostic model.

Submission Number: 67