Efficient Edge-AI: Towards the Future of Implantable and Smart Medical Devices
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Open Access
Type
ThesisThesis type
Doctor of PhilosophyAuthor/s
Herbozo Contreras, Luis FernandoAbstract
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 ...
See moreEpilepsy 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.
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See moreEpilepsy 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.
See less
Date
2026Rights statement
The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.Faculty/School
Faculty of Engineering, School of Biomedical EngineeringAwarding institution
The University of SydneyShare