Wearanize+ is a multimodal sleep dataset containing overnight sleep data from 130 young, healthy participants using PSG and three wearables
Automated polysomnography for experimental animal research
Official implementation of our paper "ENHANCING HEALTHCARE WITH EOG: A NOVEL APPROACH TO SLEEP STAGE CLASSIFICATION"
Competitive apnea detector for polysomnographic data
A Python package for sleep analysis and hypnogram processing
Source code for the paper "Automatic Actigraphy and Polysomnography Sleep Scoring using Deep Learning".
Graph-based PSG analysis and machine learning for sleep-stage ADHD biomarkers — reproducible reconstruction of an IEEE ISBI 2025 study.
Config-driven, auditable preprocessing for PSG sleep-staging datasets — 22 profiles, deterministic pairing, QC, provenance, and privacy-aware outputs.
Software Open Source y multiplataforma para visualizar registros de polisomnografía, hacer el scoring de sueño y anotar eventos, con módulo de análisis de bioseñales. Laboratorio de Sueño y Memoria del ITBA. Actualmente en desarrollo.
Python library and CLI for sleep and physiological data workflows.
Sleep stage classification from raw EEG/EOG using a spatial-temporal CNN (Chambon 2018 variant). Trained on PhysioNet SleepEDF-78 with MNE-Python preprocessing, ICA artifact removal, and PyTorch. Achieves ~0.72 Cohen's Kappa on subject-wise held-out test set.
Reproducible scripts and open dataset for converting clinical polysomnography (PSG) reports into FAIR, AI-ready metrics. Companion to IEEE DataPort doi:10.21227/23q6-b434.
TempoSleep is a context-aware framework for automatic single-channel EEG sleep staging. It combines multi-scale temporal feature extraction with hierarchical temporal modeling to capture local and long-range dependencies, with particular emphasis on N1-stage recognition.
Run CAISR (Complete AI Sleep Report) natively on Apple Silicon — no Docker needed
Detect breathing irregularities (hypopnea, obstructive apnea) in overnight sleep recordings by converting nasal airflow, thoracic movement, and SpO₂ into labeled 30-second windows. Train a 1D CNN with leave-one-participant-out validation and evaluate using accuracy, precision, recall, and confusion matrices.
Interpretable EEG sleep-stage classification (Sleep-EDF) with subject-wise cross-validation and HMM temporal smoothing.
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