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Zero-dependency sovereign personal agent skill for non-invasive evaluation of spontaneous Cardiac Baroreflex Sensitivity (BRS), beat-to-beat sequence analysis, cardiovagal tone, and autonomic resilience.
flowchart TD
ContinuousBP["Beat-to-Beat Systolic Blood Pressure (SBP) Stream"] & ContinuousECG["Continuous ECG RR-Interval Stream"] --> SequenceDetection["Sequence Extraction: >= 3 Consecutive Beats (r >= 0.85)"]
SequenceDetection --> UpUp["Up/Up Sequences (Delta SBP > 0, Delta RR > 0)"]
SequenceDetection --> DownDown["Down/Down Sequences (Delta SBP < 0, Delta RR < 0)"]
UpUp & DownDown --> LinearSlope["Regression Slopes (Delta RR / Delta SBP in ms/mmHg)"]
LinearSlope --> BRS_Mean["Mean Baroreflex Sensitivity (BRS) & Autonomic Score"]
BRS_Mean --> Agent["OpenClaw / Hermes Personal Agent Resonant Pacing"]Deterministic Math: Pure Python standard library implementation with zero external dependencies.
Cardiovascular Autonomic Standards: Implements validated Bertinieri sequence method equations for spontaneous arterial baroreflex gain.
Cardiovagal Buffering & Orthostatic Screening: Detects early loss of parasympathetic blood pressure regulation and syncope vulnerability.
Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.
Related MCP server: salivary-alpha-amylase
from client import CardiacBaroreflexSensitivityClient
client = CardiacBaroreflexSensitivityClient()
result = client.evaluate_brs_sequences(
up_sequences_slopes=[14.5, 16.2, 13.8],
down_sequences_slopes=[13.2, 14.8, 15.6]
)
print("Mean BRS:", result["mean_brs_ms_per_mmhg"], "ms/mmHg")
print("Baroreflex Score:", result["baroreflex_autonomic_score"])
print("Classification:", result["classification"])Apache-2.0
This server cannot be deployed
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