YASA (Yet Another Spindle Algorithm): a Python package to analyze polysomnographic sleep recordings.

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

U-Time: A Fully Convolutional Network for Time Series Segmentation

Sleep As Android integration for Home Assistant

TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG by Akara Supratak and Yike Guo from The Faculty of ICT, Mahidol University and Imperial College London respectively

Dreamento (DReam ENgenieering TOolbox): a Python-based software for dream engineering while monitoring/analyzing real-time EEG data.

An open-source sleep stage classification Python package

Analyzing all of my Quantified Self data

A lucid dreaming device with REM detection that triggers audio cues to promote lucidity. Like having a private sleep lab technician that watches you sleep and alerts you when you are dreaming.

A toolbox for sleep data analysis and sleep stage classification. If you use SLEEPYLAND in your research, please cite:  Rossi, A.D., Metaldi, M., Bechny, M. et al. SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models. npj Digit. Med. 9, 55 (2026). https://doi.org/10.1038/s41746-025-02237-2

The world's first open-source bipolar & depression detector powered by your Apple Health data.

Luna software for sleep EEG analysis (base)

EEGLAB-compatible analysis software for manual / visual sleep stage scoring, signal processing and event marking of polysomnographic (PSG) data for MATLAB.

😴 DeepSleep2 is a compact U-Net-inspired convolutional neural network with 740,551 parameters, designed to predict non-apnea sleep arousals from full-length multi-channel polysomnographic recordings at 5-millisecond resolution. Achieves similar performance to DeepSleep with lower computational cost.

Sleep stage automatic classifier using CNNs and LSTMs to process frequency signals of brain activity recorded via ElectroEncephaloGram EEG. Undergraduate thesis

Linux and Mac OSX compatible script to pull CPAP Data from an Airsense 10/11 for review in Oscar/Sleep HQ.

A systems-level analysis engine that models sleep as a recovery debt process rather than a nightly outcome. Using physiological traits and ecological pressure signals, it estimates predicted sleep need, quantifies sleep debt, and visualizes how stress accumulates silently before visible fatigue or failure occurs.

A CPAP/BiPAP data visualizer for sleep apnea and UARS.