This is an official implementation for "Intra- and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG".
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Updated
Mar 24, 2022 - Python
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This is an official implementation for "Intra- and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG".
Exploratory sleep staging and fragmentation analysis from Sleep-EDF PSG data
Sleep stage classification comparing KNN, SVM, RF, and LSTM.
Causal wearable-sensor analysis of population versus personalized sleep-stage models
Enhancing Sleep Stage Classification Performance Using Transformer-Based Approach with OOD Method Integration
End-to-end automated sleep stage classification using wearable EEG (Dreem Headband) — benchmarking classical ML, deep learning, and transfer learning (MobileNetV2) on multimodal physiological signals. Best: 82% accuracy with XGBoost, 71.2% with fine-tuned CNN.
Sleep stage classification from Sleep-EDF EEG signals using a leakage-safe, subject-grouped nested cross-validation pipeline with MLflow-tracked reproducibility.
Config-driven, auditable preprocessing for PSG sleep-staging datasets — 22 profiles, deterministic pairing, QC, provenance, and privacy-aware outputs.
Multimodal TinyML on STM32 smartwatch — on-device HAR, Chinese KWS, PPG heart-rate, fall detection, sleep staging & triple-fusion safety, fully offline.
Class wrapper of the yasa package to detect drowsiness/sleep phase using EEG data
Comparison of Different Input Channels in Deep Automatic Sleep Staging
Notebooks for training the classifier of YASA sleep staging
A Streamlit-based dashboard for managing, analyzing, and running inference with PyTorch Lightning checkpoints trained on SHHS EEG data.
A lightweight version of the Sleep module of the Visbrain package
YASA versus expert Sleep-EDF staging across 20 recordings, with recording-level uncertainty and edge-Wake sensitivity.
Interpretable EEG sleep-stage classification (Sleep-EDF) with subject-wise cross-validation and HMM temporal smoothing.
MAMBOPro v4: bidirectional Mamba-2 for single-channel EEG sleep staging
Online PAP therapy tracking suite.
ML pipelines for sleep apnea detection (ECG/HRV) and 5-stage sleep classification (EEG/EOG/EMG) on PhysioNet data — XGBoost, patient-grouped CV, SHAP explainability.
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