Efficient Edge-AI: Towards the Future of Implantable and Smart Medical Devices

Abstract

Epilepsy affects over 1% of the global population and imposes substantial clinical, social, and economic burdens. Although pharmacological therapy is the first line of treatment, approximately 30–40% of patients remain drug-resistant, making neuromodulation one of the few viable options. However, current neuromodulation systems are largely open-loop or depend on cloud-based AI, limited by latency, power, privacy, and scalability. These constraints hinder autonomous, personalised, implantable closed-loop neurostimulation.

This thesis investigates neuromorphic computing as a paradigm for next-generation closed-loop neuromodulation, focusing on seizure detection and prediction in epilepsy. It introduces neuromorphic neuromodulation and shows how biologically inspired, on-device intelligence can support self-responsive and personalised therapies. Building on this framework, the thesis develops seizure detection systems using liquid-time constant neurons and dendritic spiking mechanisms with heterogeneous temporal dynamics. These models enable efficient neural-signal representation without expensive feature extraction and show robust out-of-sample generalisation across patients and recording conditions on large-scale clinical EEG datasets.

The thesis also addresses edge learning by proposing a neuromorphic learning rule for on-device adaptation toward patient-specific treatment under strict power and memory constraints. This mechanism can be adopted by the developed models to support personalised, low-latency intelligence at the edge. Finally, learnable activation functions are explored within artificial neural networks to improve training efficiency and interpretability, highlighting a pathway for integration with neuromorphic technology.

Together, these contributions advance neuromorphic neurotechnology toward autonomous, personalised, and continuously learning closed-loop systems, with implications beyond epilepsy to a broader range of neurological disorders.