Designing a Rigorous Clinical Study
A detection model is only as credible as the data it's validated against. Here's how we're approaching that validation — deliberately, and de-identified from day one.
Good sensors and a fast pipeline aren't enough on their own — a detection system earns trust through validation against real clinical data, collected properly. We're designing our study around that principle from the start, in consultation with clinical advisors.
What We Collect — De-Identified
Every participant record is tied to a study ID, never a name. Across seven categories, we capture only what's clinically relevant to detection:
Designing a Balanced Cohort
An unbalanced dataset produces a model that only works for one kind of person, or one kind of seizure. Our target composition is deliberately spread out:
These are recruitment targets for the study design — not a completed or enrolled cohort.
We deliberately track confounders too — exercise, driving, tremor, irregular sleep — the same everyday activities that can be mistaken for a seizure in raw sensor data, so the model learns to tell them apart.
Common Questions
No — LifeLyne is a monitoring and alerting system, not a diagnostic device. The study described here is what building toward clinically validated detection actually looks like.
Every record is tied to a study ID rather than a name, and stored separately from any identifying information — consistent with the encryption and access controls described on our Safety & Privacy page.
A logged, timestamped seizure event — recorded by the participant, a caregiver, or clinical staff — compared against the sensor data from the same window. It's a deliberately manual, conservative baseline before any automated confirmation is trusted.
They directly inform retraining of the detection models, and shape whether a larger, ethics-board-reviewed trial is the right next step.
Interested in the research?
We're always open to conversations with clinical partners, researchers, and investors.
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