Edge AI Neurostimulation Controllers Versus Continuous Therapy for Drug-Resistant Focal Epilepsy
Edge Computing Artificial Intelligence Neurostimulation Controllers Versus Standard Continuous Therapy for Drug Resistant Focal Epilepsy: A Randomized Controlled Trial
Drug-resistant focal epilepsy remains a major therapeutic challenge. Conventional neurostimulators rely on static, continuous open-loop stimulation or low-bandwidth reactive loops that suffer from classification latency or high battery consumption. This prospective, double-blind, randomized controlled trial evaluates whether hardware-integrated edge computing artificial intelligence (AI) neurostimulators demonstrate superior seizure reduction, lower processing latency, and reduced battery drain compared to standard continuous neurostimulation therapy over a 6-week intervention period.
Visão geral do estudo
Status
Intervenção / Tratamento
Descrição detalhada
Eighty adult patients with drug-resistant focal epilepsy undergoing intracranial lead implantation across tertiary referral centers were randomized 1:1 post-implantation into two parallel groups: Edge Computing AI Neurostimulation: Received localized on-hardware machine learning feature extraction and pre-ictal spike classification from intracranial EEG, triggering adaptive targeted high-frequency electrical impulse bursts. Standard Continuous Therapy: Received conventional fixed-frequency, open-loop electrical neurostimulation. The trial evaluated clinical seizure frequency tracked via direct intracortical telemetry logs validated by daily patient diaries, as well as operational hardware metrics including microchip classification latency, real-time inference execution loads, and daily milliampere-hour battery consumption over 6 weeks.
Tipo de estudo
Inscrição (Real)
Estágio
- Não aplicável
Contactos e Locais
Locais de estudo
-
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Punjab Province
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Lahore, Punjab Province, Paquistão, 54570
- Tertiary Neurosurgical Referral Hospitals
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Critérios de participação
Critérios de elegibilidade
Idades elegíveis para estudo
Aceita Voluntários Saudáveis
Gêneros Elegíveis para o Estudo
Descrição
Inclusion Criteria:
- Diagnosed with refractory (drug-resistant) focal epilepsy.
- Minimum of four electroclinically documented disabling focal seizures per month despite compliance with two or more appropriately chosen antiseizure medications.
- Scheduled for clinical intracranial lead implantation (subdural grid or depth electrodes).
- Aged 18 to 65 years at the time of enrollment.
Exclusion Criteria:
- Diagnosis of primary generalized epilepsy.
- Progressive neurological disorders.
- Severe cognitive impairment.
- Active psychiatric conditions.
- Previous cranial neurostimulation implants.
- Medical contraindications to surgical implantation or continuous intracortical telemetry.
Plano de estudo
Como o estudo é projetado?
Detalhes do projeto
- Finalidade Principal: Tratamento
- Alocação: Randomizado
- Modelo Intervencional: Atribuição Paralela
- Mascaramento: Triplo
Armas e Intervenções
Grupo de Participantes / Braço |
Intervenção / Tratamento |
|---|---|
|
Experimental: Edge Computing AI Neurostimulation
Participants received an implantable neurostimulator running local edge computing machine learning algorithms to classify pre-ictal spikes from intracortical EEG in real time, delivering adaptive targeted electrical impulse bursts. |
Hardware-integrated neural processing engine executing real-time feature extraction and pre-ictal spike classification from intracranial EEG, triggering adaptive electrical bursts. |
|
Comparador Ativo: Standard Continuous Therapy
Participants received continuous open-loop static neurostimulation delivering fixed-frequency electrical pulses regardless of underlying electrical activity. |
Static, fixed-frequency continuous open-loop electrical pulse stimulation delivered regardless of underlying electrical activity. |
O que o estudo está medindo?
Medidas de resultados primários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
|
Monthly Clinical Focal Seizure Frequency
Prazo: Baseline and Week 6 (completion of intervention)
|
Number of clinical focal seizures per month, recorded continuously via automated direct intracortical telemetry logs and verified against daily patient seizure diaries |
Baseline and Week 6 (completion of intervention)
|
Medidas de resultados secundários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
|
Microchip Pre-Ictal Classification Latency
Prazo: Continuously across the 6-week intervention
|
Local on-hardware signal processing and classification response time measured in milliseconds (ms) from raw intracranial EEG pattern identification to trigger execution. |
Continuously across the 6-week intervention
|
|
Daily Battery Drain Rate
Prazo: Continuously across the 6-week intervention
|
Average daily battery consumption rate measured in milliampere-hours per day (mAh/day) from device telemetry logs. |
Continuously across the 6-week intervention
|
|
Real-Time Microchip Inference Load
Prazo: Continuously across the 6-week intervention
|
Percentage of microchip processing capacity utilized during real-time on-device machine learning execution. |
Continuously across the 6-week intervention
|
|
Number of Participants with Adverse Events
Prazo: Throughout the 8-month trial duration
|
Hardware-related, surgical, or neurological adverse events documented throughout the trial. |
Throughout the 8-month trial duration
|
Colaboradores e Investigadores
Patrocinador
Investigadores
- Investigador principal: Ammar Khalil, University of Kotli
Datas de registro do estudo
Datas Principais do Estudo
Início do estudo (Real)
Conclusão Primária (Real)
Conclusão do estudo (Real)
Datas de inscrição no estudo
Enviado pela primeira vez
Enviado pela primeira vez que atendeu aos critérios de CQ
Primeira postagem (Real)
Atualizações de registro de estudo
Última Atualização Postada (Real)
Última atualização enviada que atendeu aos critérios de controle de qualidade
Última verificação
Mais Informações
Termos relacionados a este estudo
Termos MeSH relevantes adicionais
Outros números de identificação do estudo
- UoK1117A
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