~n0Neural Zero
A predictive biological surveillance system using computer vision and a Multi-Agent AI Swarm to predict cardiac failure in paralyzed patients before it happens.
We read the invisible autonomic warning signs straight off their face using a standard $50 webcam.
Modern ICU monitors are failing the most vulnerable patients on earth.
In-Hospital Cardiac Arrest Burden
About 292,000 adult in-hospital cardiac arrests occur in the U.S. each year, making deterioration inside the hospital a large and ongoing surveillance problem.
Survival After Arrest Is Still Low
Only about 25.8% survive to hospital discharge after in-hospital cardiac arrest, which means most patients still lose even when the event happens inside a monitored setting.
Alarm Fatigue Crisis
Roughly 72% to 99% of clinical alarms are false or nonactionable, which is exactly why the answer cannot be more noise. It has to be better filtering and earlier escalation.
Why ~n0 matters: About 170,000 U.S. heart attacks each year are silent, many in-hospital cardiac arrests are retrospectively judged preventable, and smarter biological surveillance can intervene before the room waits for a noisy threshold alarm to fail.
An autonomous, hardware-agnostic neural bypass.
We don't ask the patient how they feel. We don't wait for their heart to stop. We read the invisible, autonomic warning signs straight off their face using a standard $50 webcam.
TECH STACK
Vision Engine
Python, OpenCV, MediaPipe, SciPy
High-density facial mesh mapping with Remote Photoplethysmography (rPPG). Extracts pulse and detects blood oxygen drops invisible to the human eye using bandpass filters on RGB pixel micro-fluctuations.
Swarm Brain
FastAPI, Asyncio, LLM APIs
High-concurrency Python backend orchestrating a 6-Agent LLM War Room. Routes biological data through a localized Prediction Market to eliminate hallucinations and false alarms.
Display Layer
React, TypeScript, Tailwind, WebSockets
Dark-mode, high-performance Consensus Canvas rendering live MJPEG video streams and WebSocket data with zero latency.
6-Agent LLM War Room
Instead of relying on a single, brittle AI prompt, ~n0 routes biological data through a localized Prediction Market to eliminate hallucinations and false alarms.
Agent_Vision
Ingests raw OpenCV data. Flags micro-sweating and facial blood withdrawal patterns invisible to the human eye.
Agent_Bio
Ingests simulated machine data. Flags rigid Heart Rate Variability (HRV) patterns that indicate cardiac stress.
Agent_Archive
Instantly pulls the patient's Electronic Health Record (EHR) to check for a history of vascular disease.
Agent_CrossCheck
Cross-checks the live patient against nearest historical trajectories to see whether the current signal pattern matches a known danger path or a safer decoy.
Agent_RedTeam
Aggressively challenges other agents. Tries to prove symptoms are false positives (e.g., 'Is pallor just room lighting?').
Agent_Chief
Weighs all data, resolves RedTeam challenges, and calculates a final confidence score for clinical action.
Consensus Protocol
All six agents vote independently. Agent_Chief synthesizes their inputs, resolves conflicts raised by Agent_RedTeam, and produces a single confidence score. Only when consensus exceeds the clinical threshold does the system alert medical staff, dramatically reducing the 85% false alarm rate plaguing traditional ICU monitors.