A study of the relationship between ictal falls and ictal-asystole in Drug-Resistant Temporal Lobe Epilepsy based on artificial intelligence

Status

Active, not recruiting

Phases

Early Phase 1

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR2300079319

Enrollment

Unknown

Registered

2023-12-30

Start date

2024-01-01

Completion date

Unknown

Last updated

2024-01-08

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

epilepsy, ictal asystole, artificial intelligence

Interventions

Not accompanied by ictal semiology of falls:long-term video EEG

accompanied by ictal semiology of falls:long-term video EEG

Eligibility

Sex/Gender

All

Age

18 Years to 60 Years

Inclusion criteria

Inclusion criteria: 1) drug-resistant temporal lobe epilepsy, 2) with ictal asystole, 3) accompanied by ictal semiology of fall.

Exclusion criteria

Exclusion criteria: 1) Patients with significant neurological/psychiatric disorders, and severe medical diseases other than epilepsy, which might hamper appropriate understanding and completion of the video EEG, will be excluded. 2) Patients with asystole during nonepileptic events will be excluded.

Design outcomes

Primary

Measure	Time frame
the differences of electroencephalogram (EEG) -electrocardiogram (ECG) relationship during ictal-asystole in patients with refractory temporal lobe epilepsy with or without falls;	—

Secondary

Measure	Time frame
to establish the artificial intelligence automatic analysis of long-term video EEG data model;	—

Countries

China

Contacts

Public ContactWenming

The Third People's Hospital of Chengdu,Affiliated Hospital of Southwest Jiaotong University, The Second Affiliated Chengdu Hospital of Chongqing Medical University

wmcivil@swjtu.edu.cn+86 183 8103 0294

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Mar 2, 2026