physioex 1.0.0.dev1

A python package for explainable sleep staging via deep learning

PhysioEx

PhysioEx ( Physiological Signal Explainer ) is a versatile python library tailored for building, training, and explaining deep learning models for physiological signal analysis.

The main purpose of the library is to propose a standard and fast methodology to train and evalutate state-of-the-art deep learning architectures for physiological signal analysis, to shift the attention from the architecture building task to the explainability task.

With PhysioEx you can simulate a state-of-the-art experiment just running the train, test_model and finetune commands; evaluating and saving the trained model; and start focusing on the explainability task!

Supported deep learning architectures

• Chambon2018 model for sleep stage classification ( raw time series as input).
• TinySleepNet model for sleep stage classification (raw time series as input).
• SeqSleepNet model for sleep stage classification (time-frequency images as input).

Supported datasets

• SHHS (Sleep Heart Health Study): A multi-center cohort study designed to investigate the cardiovascular consequences of sleep-disordered breathing.
• MROS (MrOS Sleep Study): A study focusing on the outcomes of sleep disorders in older men.
• MESA (Multi-Ethnic Study of Atherosclerosis): A study examining the prevalence, correlates, and progression of subclinical cardiovascular disease.
• DCSM (Dreem Challenge Sleep Monitoring): A dataset from the Dreem Challenge for automatic sleep staging.
• MASS (Montreal Archive of Sleep Studies): A comprehensive collection of polysomnographic sleep recordings.
• HMC (Home Monitoring of Cardiorespiratory Health): A dataset for the study of cardiorespiratory health using home monitoring devices.

For the public available datasets ( DCSM, HMC ) PhysioEx takes care of automatically download the data thanks to the preprocess command. The other datasets needs to be acquired first ( mostly on NSSR ) and then fetched by PhysioEx via the preprocess command.

Installation guidelines

1. Clone the Repository:

   git clone https://github.com/guidogagl/physioex.git
   cd physioex

2. Create a Virtual Environment (Optional but Recommended)

    conda create -n physioex python==3.10
    conda activate physioex
    conda install pip
    pip install --upgrade pip  # On Windows, use `venv\Scripts\activate`

3. Install Dependencies and Package in Development Mode

    pip install -e .