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.
연구 개요
상태
상세 설명
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.
연구 유형
등록 (실제)
단계
- 해당 없음
연락처 및 위치
연구 장소
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Punjab Province
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Lahore, Punjab Province, 파키스탄, 54570
- Tertiary Neurosurgical Referral Hospitals
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참여기준
자격 기준
공부할 수 있는 나이
건강한 자원 봉사자를 받아들입니다
연구 대상 성별
설명
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.
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 치료
- 할당: 무작위
- 중재 모델: 병렬 할당
- 마스킹: 삼루타
무기와 개입
참가자 그룹 / 팔 |
개입 / 치료 |
|---|---|
|
실험적: 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. |
|
활성 비교기: 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. |
연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Monthly Clinical Focal Seizure Frequency
기간: 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)
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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Microchip Pre-Ictal Classification Latency
기간: 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
기간: 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
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Real-Time Microchip Inference Load
기간: 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
기간: Throughout the 8-month trial duration
|
Hardware-related, surgical, or neurological adverse events documented throughout the trial. |
Throughout the 8-month trial duration
|
공동 작업자 및 조사자
수사관
- 수석 연구원: Ammar Khalil, University of Kotli
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (실제)
연구 완료 (실제)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
기타 연구 ID 번호
- UoK1117A
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
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