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.
Panoramica dello studio
Stato
Condizioni
Intervento / Trattamento
Descrizione dettagliata
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 di studio
Iscrizione (Effettivo)
Fase
- Non applicabile
Contatti e Sedi
Luoghi di studio
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Punjab Province
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Lahore, Punjab Province, Pakistan, 54570
- Tertiary Neurosurgical Referral Hospitals
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Criteri di partecipazione
Criteri di ammissibilità
Età idonea allo studio
Accetta volontari sani
Sessi ammissibili allo studio
Descrizione
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.
Piano di studio
Come è strutturato lo studio?
Dettagli di progettazione
- Scopo principale: Trattamento
- Assegnazione: Randomizzato
- Modello interventistico: Assegnazione parallela
- Mascheramento: Triplicare
Armi e interventi
Gruppo di partecipanti / Arm |
Intervento / Trattamento |
|---|---|
|
Sperimentale: 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. |
|
Comparatore attivo: 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. |
Cosa sta misurando lo studio?
Misure di risultato primarie
Misura del risultato |
Misura Descrizione |
Lasso di tempo |
|---|---|---|
|
Monthly Clinical Focal Seizure Frequency
Lasso di tempo: Baseline and Week 6 (completion of intervention)
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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)
|
Misure di risultato secondarie
Misura del risultato |
Misura Descrizione |
Lasso di tempo |
|---|---|---|
|
Microchip Pre-Ictal Classification Latency
Lasso di tempo: 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
Lasso di tempo: 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
Lasso di tempo: 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
|
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Number of Participants with Adverse Events
Lasso di tempo: Throughout the 8-month trial duration
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Hardware-related, surgical, or neurological adverse events documented throughout the trial. |
Throughout the 8-month trial duration
|
Collaboratori e investigatori
Investigatori
- Investigatore principale: Ammar Khalil, University of Kotli
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Inizio studio (Effettivo)
Completamento primario (Effettivo)
Completamento dello studio (Effettivo)
Date di iscrizione allo studio
Primo inviato
Primo inviato che soddisfa i criteri di controllo qualità
Primo Inserito (Effettivo)
Aggiornamenti dei record di studio
Ultimo aggiornamento pubblicato (Effettivo)
Ultimo aggiornamento inviato che soddisfa i criteri QC
Ultimo verificato
Maggiori informazioni
Termini relativi a questo studio
Termini MeSH pertinenti aggiuntivi
Altri numeri di identificazione dello studio
- UoK1117A
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