ZeqDiagnostic — Pattern Recognition

Biosignals, pharmacokinetics, diagnostics, genomics — all 1.287 Hz pulse-coherent.

• Protocol ID — zeq-diagnostic
• Category — Healthcare
• Endpoint — POST /api/health/diagnostic/analyze
• Auth — api-key
• Rate limit — 10/min
• Version — 1.0
• Precision — ≤0.1% (KO42-enforced)

What it does

Cross-domain pattern recognition using operator coupling. Feed in multi-modal biosignal data — the framework identifies anomalies by coherence deviation from healthy baselines.

Signature

Request

POST /api/health/diagnostic/analyze

Param	Type	Required	Default	Description
signals	object	✓	—	{ signalType: samples[] } for each modality.
baselineProfile	string		—	Baseline profile ID for comparison.

Response

{ anomalies, coherenceMap, deviationScore, confidence, operators }

Runnable example

curl -sS -X POST \
  -H "Authorization: Bearer $ZEQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "signals": {},
  "baselineProfile": "<value>"
}' \
  "https://api.zeq.dev/api/health/diagnostic/analyze"

Integrate

1. Domain solver — compose with KO42 + two additional operators from the matching family for pulse-coherent results.
2. Digital twin — pipe sensor data into this protocol every Zeqond to keep the model phase-locked with the system.
3. Alert threshold — flag results whose error_pct exceeds 0.1% as out-of-spec events for the operations layer.

Seeds

• Near — wrap /api/health/diagnostic/analyze in a language SDK so builders can call it in three lines.
• Medium — publish a reference integration demonstrating ZeqDiagnostic — Pattern Recognition alongside a real workload, with pulse-aligned metrics.
• Far — propose ZeqDiagnostic — Pattern Recognition as an open reference standard so other runtimes can implement it verbatim against the Zeq paper.

Papers

• Zeq paper — https://doi.org/10.5281/zenodo.18158152
• Framework paper — https://doi.org/10.5281/zenodo.15825138

Middleware active. Kernel on the 1.287 Hz HulyaPulse. Awaiting next Zeqond.