You can deploy this model and start earning money today!

shamique/
Light-Weight-Neuromorphic-Sleep-Stage-Model

Name: Light-Weight-Neuromorphic-Sleep-Stage-Model
Availability: InStock

cc-by-4.0

Views

No views yet

NeuroSleep — Light-Weight Sleep Stage Model

99,477 parameters, 87.7% accuracy (κ=0.763) — small enough for edge/wearable deployment, scoring Wake/N1/N2/N3/REM from 4-channel PSG.

Quick links: GitHub · Live Demo · Kaggle

A compact PyTorch model for five-stage sleep-stage classification from polysomnography signals. Processes 300 seconds of context (10 × 30-second epochs) and classifies each epoch into Wake, N1, N2, N3, or REM. Designed for edge deployment on resource-constrained devices.

Quick Start

python

import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

# Download checkpoint
path = hf_hub_download(
    repo_id="shamique/Light-Weight-Neuromorphic-Sleep-Stage-Model",
    filename="student_full_finetuned.safetensors",
)

# Load model (see source repo for ImprovedStudent class definition)
# https://github.com/shamiquekhan/neuromorphic-sleep-staging-pipeline
from sleep_staging.models.improved_student import ImprovedStudent

model = ImprovedStudent()
model.load_state_dict(load_file(path, device="cpu"))
model.eval()

# Run inference on preprocessed PSG data
# Input: [batch, 10, 4, 3000] — 10 epochs, 4 channels, 3000 samples @ 100Hz
x = torch.randn(1, 10, 4, 3000)  # replace with real data

with torch.inference_mode():
    logits = model(x)              # [1, 10, 5]
    probs = torch.softmax(logits, dim=-1)
    preds = probs.argmax(dim=-1)   # [1, 10]

STAGE_NAMES = {0: "Wake", 1: "N1", 2: "N2", 3: "N3", 4: "REM"}
for i in range(10):
    print(f"Epoch {i}: {STAGE_NAMES[preds[0, i].item()]} ({probs[0, i, preds[0, i]].item():.2%})")

Architecture

PSG Input (Fpz-Cz, Pz-Oz, EOG, EMG)  [B, 10, 4, 3000]
      ↓
Multi-Resolution Stem (2 parallel Conv1d branches)
      ↓
Depthwise-Separable CNN (2 blocks)
      ↓
Parametric Gabor Feature Extraction (8 learnable filters)
      ↓
2-Layer GRU (hidden=64, 300s context)
      ↓
Linear(64→5) + Softmax
      ↓
Wake / N1 / N2 / N3 / REM

Module	Parameters
Stem (S+L)	7,232
Encoder (2 blocks)	2,304
Gabor FEB	144
GRU	89,856
Head	325
Total	99,477

Input Format

Sampling rate: 100 Hz
Channels: Fpz-Cz, Pz-Oz, EOG, EMG
Epoch length: 30 seconds (3000 samples)
Sequence length: 10 epochs
Shape: [batch, 10, 4, 3000]
Preprocessing: 0.5–35 Hz bandpass → 50 Hz notch → z-score normalization

Output Labels

Index	Stage	Description
0	Wake	Awake state
1	N1	Light sleep
2	N2	Intermediate sleep
3	N3	Deep sleep
4	REM	Rapid eye movement sleep

Evaluation (92 subjects, 10-fold subject-level CV, 3 seeds)

Metric	Value
Accuracy	87.7% ± 2.7%
Cohen's Kappa	0.763 ± 0.043
Macro F1	0.730 ± 0.037
Weighted F1	89.0% ± 2.1%

Per-Class Performance (Full Fine-Tuning)

Stage	F1	Precision	Recall
Wake	0.964 ± 0.016	0.995	0.936
N1	0.445 ± 0.061	0.330	0.712
N2	0.768 ± 0.041	0.878	0.687
N3	0.681 ± 0.114	0.562	0.896
REM	0.771 ± 0.078	0.772	0.785

Honest assessment: Overall accuracy (87.7%) is strong with balanced performance across all five stages. N1 is the most challenging stage (F1=0.445) due to its transitional nature and low prevalence (~4.6% of epochs).

Preprocessing

The model expects preprocessed data:

Bandpass filter: 0.5–35 Hz
Notch filter: 50 Hz
Normalization: z-score per channel
Epoching: 30-second windows at 100 Hz

See the source repo for the full preprocessing pipeline.

Training Details

Dataset: Sleep-EDF Expanded (92 subjects, PhysioNet)
Optimizer: AdamW (lr=3e-4, weight_decay=1e-4)
Epochs: 20 (early stopping patience=5)
Class weights: N1=2x, REM=2x
Supervision: All-position (every epoch in 10-epoch window)
Gradient clipping: max_norm=1.0
Mixed precision: True (CUDA)
Seeds: 42, 43, 44 (30 folds per method)

LoRA Adaptation (Parameter-Efficient Fine-Tuning)

The model supports LoRA (Low-Rank Adaptation) for efficient fine-tuning on new datasets without updating all 99K parameters.

LoRA Configuration

Property	Value
Target modules	`enc.0.pw`, `enc.1.pw`, `head`
Rank	8
Alpha	16
Scaling	2.0
Trainable params	1,448 (1.43% of total)

Apply LoRA

python

from sleep_staging.adaptation.lora import LoRAConfig, apply_lora

lora_config = LoRAConfig(
    rank=8,
    alpha=16,
    target_modules=["enc.0.pw", "enc.1.pw", "head"],
    dropout=0.05,
)

model = ImprovedStudent()
model.load_state_dict(load_file(ckpt_path, device="cpu"))
model = apply_lora(model, lora_config)
# trainable params: 1,448 || all params: 99,477 || trainable%: 1.43%

LoRA vs Full Fine-Tuning (100-Subject Benchmark)

Method	Trainable Params	Accuracy	Macro F1
Frozen Base	0	87.1%	0.673
LoRA CNN+Head	1,448	83.6%	0.674
Full Fine-Tuning	99,477	87.7%	0.730

LoRA CNN+Head uses 68.7× fewer trainable parameters while retaining 95.4% of full FT accuracy.

Intended Use

Research and educational sleep-stage classification
Benchmarking and comparison with other sleep staging methods
Edge deployment on resource-constrained devices (MCUs, wearables)
Transfer learning via LoRA for new sleep datasets

Limitations

Not clinically validated — do not use for diagnosis or clinical decision-making
N1 classification is challenging (F1=0.445) due to brief, transitional light sleep
Trained on Sleep-EDF Expanded (92 subjects); generalizability should be validated
Requires 4-channel PSG (Fpz-Cz, Pz-Oz, EOG, EMG) — single-channel EEG not supported
Class distribution is Wake-dominant (~68%) from untrimmed recordings

Resources

Resource	Link
Source Code	GitHub
Live Demo	Hugging Face Space
Reproduce	Kaggle Notebook
Model Weights	This page

Citation

bibtex

@project{neurosleep_2026,
  title={NeuroSleep: Light-Weight Sleep Stage Scoring},
  author={Kaushik, P. and Vora, S. and Bhatt, S. and Khan, S. and Lone, A.J.},
  year={2026},
  institution={VIT Bhopal University}
}

Download Counting

Hugging Face counts downloads per unique file. For this model, the primary tracked file is student_full_finetuned.safetensors. Each HTTP request (GET or HEAD) to this file counts as one download. Clone operations that download all files are counted once per file.

For granular download analytics (unique users, CI/CD filtering), see Publisher Analytics.