Electroretinogram Photopic Negative Response (PhNR) Skill

Zero-dependency sovereign personal agent skill for non-invasive analysis of Electroretinogram Photopic Negative Response (PhNR), retinal ganglion cell health, and ocular strain.

flowchart TD
    PhotopicFlash["Photopic Light Stimulus"] --> ERG_Waveform["Full-Field Electroretinogram Recording"]
    ERG_Waveform --> FeatureExtract["Extraction: a-wave, b-wave, PhNR Trough & Latency"]
    FeatureExtract --> RatioCalc["PhNR/b-wave Ratio & Retinal Ganglion Engine"]
    RatioCalc --> Agent["OpenClaw / Hermes Personal Agent Actions"]

Features

• Deterministic Math: Pure Python standard library implementation with zero external dependencies.
• ISCEV Standard Electrophysiology: Implements clinical electroretinography standards for photopic negative response quantification.
• Retinal Ganglion Strain Stratification: Quantitative detection of inner retinal stress before irreversible visual loss.
• Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.

Quickstart

from client import ElectroretinogramPhNRClient

client = ElectroretinogramPhNRClient()
result = client.evaluate_phnr(
    b_wave_amplitude_uv=118.0,
    phnr_amplitude_uv=46.5,
    phnr_latency_ms=71.5
)
print("PhNR/b-wave Ratio:", result["phnr_b_wave_ratio"])
print("Classification:", result["classification"])

License

Apache-2.0