NeuroSense
NeuroSense is developing a personalized epilepsy management platform that combines wearable sensor data, clinical records, and patient-reported information to generate an integrated seizure risk score between physician visits. The system interprets physiological and daily context signals relative to each patient's own baseline, providing a continuous view of health patterns that standard in-clinic assessments miss. An accompanying patient app delivers these personalized risk insights directly to people with epilepsy.
- Artificial Intelligence
- Digital Health
- Hardware
- Healthcare Technology
- Software Only
Funding
Founders
Product
Problem
Epilepsy care relies on intermittent clinic visits that capture only a snapshot of a patient's condition, leaving physicians and patients without visibility into seizure risk and health patterns between appointments. This gap limits personalized treatment adjustments and can delay recognition of concerning changes in a patient's condition.
Solution
NeuroSense offers a platform that integrates wearable physiological signals, clinical context, and patient-reported data into a single, continuous view of a patient's health between visits. The system analyzes changes over time and interprets these patterns relative to each patient's individual baseline, rather than a generic population norm — producing a personalized seizure risk score. A patient-facing app delivers these insights directly to users, helping them recognize patterns and better understand their condition. The platform is designed to support, not replace, clinical decision-making, providing physicians with a richer context for care planning.
Target Audience
The primary users are people with epilepsy who want to track seizure risk between medical visits, along with neurologists and epilepsy care providers who need richer longitudinal context to complement in-clinic assessments.
Features
- Integrated seizure risk score that synthesizes physiologic signals, clinical context, and daily patient-reported data
- Personalized pattern recognition that benchmarks signals against each patient's own baseline rather than group averages
- Continuous monitoring approach that captures health changes occurring between scheduled appointments
- Associated patient app that communicates personalized risk insights and health patterns directly to users
- Multi-layer data architecture combining sensor data with clinical and daily contextual information for a unified view