Pabel offers an AI‑driven analytics platform that processes raw EEG recordings to automatically detect hidden epileptiform activity such as low‑amplitude spikes and sharp waves. Using deep‑learning models with artifact rejection and visual overlays, the system highlights events on the original waveform and generates concise, exportable reports with timestamps and confidence scores, integrating securely with existing EEG workflows.
Funding
Funding not disclosed
Founders
Product
Problem
Clinicians often miss subtle epileptiform patterns in routine EEG recordings because these events can be low amplitude, brief, or obscured by noise, leading to delayed diagnosis and suboptimal treatment for patients with epilepsy.
Solution
Pabel provides an AI-driven analytics platform that processes raw EEG data to uncover hidden epileptiform activity. The system applies deep‑learning models trained on large, annotated EEG datasets to identify spikes, sharp waves, and other seizure‑related signatures that are difficult to detect visually. Detected events are highlighted on the original waveform and summarized in a concise report that includes timestamps, confidence scores, and suggested clinical relevance. The platform integrates with existing EEG acquisition software via standard file formats (e.g., EDF, BDF) and can be accessed through a secure web interface, allowing neurologists and EEG technologists to review findings alongside their routine analysis.
Target Audience
Primary users are clinical neurophysiologists, epileptologists, and EEG technologists in hospitals, epilepsy centers, and diagnostic labs who require reliable detection of subtle seizure activity.
Features
- Convolutional neural network models optimized for high‑resolution detection of low‑amplitude epileptiform discharges
- Automated artifact rejection and signal preprocessing to improve detection accuracy in noisy recordings
- Visual overlay of identified events on the original EEG trace with adjustable confidence thresholds
- Exportable summary reports compatible with common clinical documentation systems (PDF, CSV, HL7)
- Secure cloud processing with end‑to‑end encryption and compliance with healthcare data standards (HIPAA, GDPR)