Energy-Efficiency-Optimized Convolutional Spiking Neural Networks for Patient-Specific Seizure Detection

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dc.contributor.advisorKassiri, Hossein
dc.contributor.authorMuneeb, Abdul
dc.date.accessioned2025-04-10T10:41:57Z
dc.date.available2025-04-10T10:41:57Z
dc.date.copyright2024-08-27
dc.date.issued2025-04-10
dc.date.updated2025-04-10T10:41:57Z
dc.degree.disciplineElectrical and Computer Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractThis thesis presents the design, development, and hardware implementation of neuromorphic spiking convolutional neural networks (SNNs) for patient-specific seizure detection. Leveraging the energy-efficient, event-driven nature of SNNs, the work achieves a low-power, low-latency, high-accuracy processing unit for multi-channel implantable brain-machine interfaces. Initial validation on publicly available datasets using spiking convolutional neural networks (SCNNs) demonstrates average sensitivities and specificities of 83.02% and 86.31%, respectively, with a false positive rate of 0.69/hour. The 1-bit Integer-Net SCNN achieves sensitivity comparable to 32-bit CNNs while improving memory efficiency by 27× and energy efficiency by 98.6%, consuming just 1.28 μJ/classification. Further optimization tailored to patient-specific seizure detection on STM32H747 microcontrollers and Intel Loihi simulators achieves sensitivities of 92.2% and specificities of 97.3%. By optimizing quantization resolution, encoding schemes, and implementing SCNNs in HDL, this thesis advances energy-efficient seizure detection, contributing to adaptive brain interfaces and the broader field of neuromorphic computing.
dc.identifier.urihttps://hdl.handle.net/10315/42747
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subject.keywordsSpiking neural networks
dc.subject.keywordsEpilepsy
dc.subject.keywordsConvolutional neural networks
dc.subject.keywordsHardware
dc.subject.keywordsIntegrated Circuits
dc.subject.keywordsOn-chip
dc.subject.keywordsNeuroscience
dc.subject.keywordsFPGA
dc.subject.keywordsVerilog
dc.titleEnergy-Efficiency-Optimized Convolutional Spiking Neural Networks for Patient-Specific Seizure Detection
dc.typeElectronic Thesis or Dissertation

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