graph TD
subgraph Core["Core Layer"]
PY[pyPhases<br/><i>Phase - Config - Project</i>]
PH[phases<br/><i>CLI - project.yaml runner</i>]
PY -->|required by| PH
end
subgraph Plugins["Extension Plugins"]
ML[pyPhasesML<br/><i>PyTorch / TensorFlow</i>]
RL[pyPhasesRecordloader<br/><i>Unified signal base</i>]
SH[SleepHarmonizer<br/><i>PSG harmonization</i>]
PY --> ML
PY --> RL
end
subgraph Loaders["Optional Dataset Loaders"]
SEDF[...SleepEDF<br/><i>dataset</i>]
ALICE[pyPhasesRecordloaderAlice<br/><i>vendor</i>]
end
SPY[SleePyPhases<br/><i>sleep ml framework</i>]
YOU[<b>Your Study</b>]
RL -.->|plugin| SEDF
RL -.->|plugin| ALICE
RL --> SH
PH --> SPY
ML --> SPY
SH --> SPY
SPY --> YOU
SEDF -.->|loaded via project.yaml| SH
ALICE -.->|loaded via project.yaml| SH
SEDF -.->|loaded via project.yaml| YOU
ALICE -.->|loaded via project.yaml| YOU
SleePyPhases
Open-source framework for reproducible sleep ML pipeline development
SleePyPhases is an open-source Python framework providing unified access to multiple sleep data repositories. It offers integrated data harmonization, configuration-based preprocessing, and the development of machine learning pipelines.
Built on top of pyPhases — a config-driven pipeline framework where every experiment is described by a single project.yaml file and every intermediate result is cached and reproducible.
Start here
Build a sleep ML study
Follow the step-by-step tutorial using the spp-example project: from loading SleepEDF data to training and evaluating a CNN sleep stager.
→ Tutorial: Sleep Study
Add a new dataset
Build a record-loader package for your PSG device or configure an existing vendor device with a YAML file.
→ New Record Loader · Vendor Config
Integrate and publish a new sleep study (HuggingFace, GitLab, Docker, PyPI)
Load a pre-trained PPG-based sleep stager from HuggingFace and run inference on your own recordings in minutes.
→ Integrate a Study
Run a published sleep study
Load a pre-trained PPG-based sleep stager from HuggingFace and run inference on your own recordings in minutes.
→ Use a Published Study
Ecosystem
Package overview
| Package (PyPi) | Role |
|---|---|
pyPhases |
Foundation — Phase, Config, Project, Exporter |
phases |
CLI — phases create, phases run |
pyPhasesML |
ML extension — PyTorch / TensorFlow model manager |
pyPhasesRecordloader |
Signal loading base — EDF, H5, MAT, WFDB |
SleepHarmonizer |
PSG channel / annotation harmonization |
SleePyPhases |
Multi-dataset sleep research framework |
→ Full table with all dataset loaders: Plugins & Loaders reference