BR

Research & Validation 

Peer-reviewed publications, empirical validation against public databases, and 86 nights of continuous wearable sensor data.

Publications

Paper IPreprint

S-Entropy and the Partition Capacity Axiom: A Mathematical Foundation for Categorical State Spaces

K. Sachikonye

Introduces the foundational axiom C(n) = 2n² and derives the S-entropy coordinate system (S_k, S_t, S_e) for representing physiological states in a unified three-dimensional space. Establishes the Kuramoto order parameter framework and five partition regimes.

partition-theoryS-entropyKuramotofoundations
Paper IIPreprint

Cardiac Equations of State: Pressure-Volume Thermodynamics from Partition Boundary Conditions

K. Sachikonye

Derives the Frank-Starling law, Windkessel model, and baroreflex as emergent properties of the partition equation of state PdV + VdP = C(n)kT. Classifies cardiac pathologies via Kuramoto regime boundaries, validated against MIT-BIH arrhythmia database.

cardiacequations-of-stateFrank-Starlinghemodynamics
Paper IIIPreprint

Cardiac-Neural-Metabolic Integration: Cross-Scale Coherence and the Consciousness Window

K. Sachikonye

Establishes the universal coupling law R_n/R_c = 0.87/√R_c, derives the consciousness window formula, and demonstrates REM active decoupling. Includes empirical validation across PhysioNet databases and Oura Ring sleep data.

neuralcouplingconsciousnesssleepREM
Paper IVIn Preparation

Sensor Disambiguation via Partition-Coupled Metrics: From Single-Sensor Readings to Physiological State

K. Sachikonye

Introduces Partition-Coupled Heart Rate (PCHR), S-entropy health coordinates, Temperature-Corrected Coherence (TCC), and the Cross-Scale Coherence Index (CSCI) — novel composite metrics that leverage the partition framework to disambiguate identical sensor readings into distinct physiological states.

wearablesPCHRTCCCSCIdisambiguation

Validation Datasets

Validated against multiple independent data sources spanning arrhythmia, heart failure, sleep, and longitudinal wearable recordings.

MIT-BIH Arrhythmia

PhysioNet
48 records

Beat-to-beat RR intervals, rhythm annotations, 1,439 analysis windows

CHF RR Intervals

PhysioNet
15 records

Long-term RR intervals from CHF patients, 1,399 analysis windows

Sleep-EDF

PhysioNet
197 records

Polysomnography: EEG, EOG, EMG, event markers, hypnograms

Oura Ring

Personal
86 nights / 104 days

5-min HR, RMSSD, hypnogram, temperature, SpO2, activity

Validation Results

20 validation panels covering cardiac regime classification, neural coupling, metabolic integration, and sensor disambiguation.

Cardiac Regime Classification

Sleep stage R_c distributions (8,701 epochs)
MIT-BIH rhythm regime mapping (1,439 windows)
CHF vs NSR coherence deficit analysis
Window-level cardiac landscape

Sleep Architecture

Transition matrix (86 nights)
Sleep score correlations
Stage-specific RMSSD profiles
Activity-sleep coupling dynamics

Cardiac-Neural Coupling

R_c vs R_n per sleep stage
Coupling formula validation (error = 0.011)
REM active decoupling (gap = 0.375)
EEG band profile analysis

Equations of State

Cardiac PV loop thermodynamics
Frank-Starling derived curves
Windkessel arterial compliance
Regime sweep predictions

Sensor Disambiguation

PCHR decomposition across sleep stages
S-entropy health coordinates mapping
Temperature-corrected coherence (TCC)
Cross-Scale Coherence Index (CSCI)

Predicted vs Measured

17 testable predictions status
5 confirmed, 2 revised, 3 new discoveries
Regime boundary calibration
Cross-database consistency checks
78.8%
AFIB epoch classification accuracy
33.2
Cohen's d: AFIB vs NSR
0.011
Coupling formula error (N1/N2)
0.375
REM cardiac-neural gap
0.797
CHF paradox R_c (> NSR 0.710)