vanSomerenPhaseEstimate

MATLAB implementation of circadian phase estimation from actigraphy data, based on the method described by Van Someren and Nagtegaal (2007).

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flowchart LR
    A[Actigraphy Data]:::input --> B[Phase Estimation Algorithm]
    B --> C[Circadian Phase]
    C --> D[Melatonin Onset Prediction]:::output

    classDef input fill:#FDF2E9,stroke:#D4A574
    classDef output fill:#EAFAF1,stroke:#82C9A1

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Overview

This toolbox estimates circadian phase from wrist actigraphy recordings by fitting progressively complex cosine models to activity data. The approach enables non-invasive prediction of dim-light melatonin onset (DLMO) timing without requiring salivary or plasma melatonin sampling.

The implementation fits four cosine model variants with increasing complexity:

• BCF -- Baseline Cosine Function
• SBCF -- Skewed Baseline Cosine Function
• BBCF -- Bimodal Baseline Cosine Function
• BSBCF -- Bimodal Skewed Baseline Cosine Function

Key Features

• Non-linear least-squares fitting with configurable bounds and initial values
• Four cosine model variants capturing asymmetry and bimodality in activity patterns
• Execution timing for each fit stage
• Demo script with sample actigraphy data (testVanSomeren2007_fits.m)

Quick Start

% Load sample data
load('inputDataForCosineFitting.mat');

% Run the demo
testVanSomeren2007_fits

Citation

Van Someren EJW, Nagtegaal E (2007). Improving melatonin circadian phase estimates. Sleep Medicine 8:590--601. doi:10.1016/j.sleep.2007.03.012

License

MIT License. See LICENSE for details.