Sleep EEG Event Detector (SEED)

Repository for the code and pretrained weights of our deep-learning based detector (SEED) described in:

Tapia-Rivas, N.I., Estévez, P.A. & Cortes-Briones, J.A. A robust deep learning detector for sleep spindles and K-complexes: towards population norms. Sci Rep 14, 263 (2024). https://doi.org/10.1038/s41598-023-50736-7

If you find this software useful, please consider citing our work.

Roadmap

• Paper officially published online. (jan 2nd, 2024)
• Share a working (but messy) code. The existing code uses tensorflow 1, which is deprecated. As a temporary fix, tensorflow 1 behaviour is requested to tensorflow 2 at import time. (jan 8th, 2024)
• Clean, update and simplify. Migrate from tensorflow 1 to tensorflow 2.
• Generate and share working checkpoints.

Note on existing pre-trained weights: Existing checkpoints require a deprecated implementation of LSTM layers (CuDNNLSTM in tf.contrib), that was removed in TF2 and does not have an exact equivalent (so tensors won't match).

Getting started

For now, your simplest entrypoint is /scripts/.

• train.py: Trains SEED, and generates predictions of the final model.
• crossval_performance.py: For a given training run, it fits the detection threshold of SEED and reports the cross-validation performance of that optimal threshold.

Setup

SEED is implemented using tensorFlow in python.

For a safe installation, create a virtual environment with python 3.10. For example, if you use conda:

conda create -n seed python=3.10
conda activate seed

Inside the environment, install dependencies running pip install -r requirements.txt

Note on Apple Silicon: If you have an apple-silicon mac, you can accelerate tensorflow with pip install tensorflow-metal (ref).