lstm-cnn-eeg-sleep-staging

LSTM-CNN Based EEG Sleep Staging Algorithm

This project uses a hybrid LSTM-CNN architecture to classify sleep stages based on two channel EEG data. The EEG data is segmented into 5 pairs of 30 second blocks, and a spectrogram is computed on each 30 second block. Each of the 5 blocks is then classified as either wake, NREM, or REM.

The data used for this project is from the Sleep-EDF Database: https://archive.physionet.org/physiobank/database/sleep-edfx/

The following diagram is a high level description of the model architecture. Note that ch1/ch2 refers to channel 1 or channel 2, and b1/b2/b3/b4/b5 refers to block 1, block 2, block 3, etc.

To run the code in this project, run the following notebooks:

1. load_parse_data.ipynb: This notebook pulls and parses data from the Physionet EDF database, which is the data source for this project
2. gen_spectrogram.ipynb: This notebook calculates spectrograms of the time series EEG data
3. train_model.ipynb: This notebook trains the model and evaluates it on the test set

The remainder of this readme will cover the different steps in the analysis pipeline.

1. Download/Parse the Data

The RECORDS.txt file contains a list of every file in the database, all of which belong to the same root URL. After downloading each file, the pyedflib library is used to parse the EDF files in the database. As an example, this is the data from file ST7152JA:

2. Generate Spectrograms

Each file is now segmented into five thirty-second consecutive blocks, and then Mel Spectrograms of each 30 second block are computed. An example spectrogram output from file SC4121EC: Note that this is not an audio application, and therefore the use of a Mel Spectrogram is not particularly justified, however the model seems to be training better on Mel Spectrograms as opposed to standard power spectral density.

3. Train the Model

For training, a validation split of 10% was used and an early stopping criterion was implemented based on the validation loss. The loss and categorical accuracy over the training session: Note that the validation loss did not decrease after epoch 8, and therefore the final saved model is based on the results of epoch 8

4. Evaluate the Model

20% of the data was used for the test set.

The confusion matrix:

	Wake	NREM	REM
Wake	29290	508	77
NREM	351	11836	722
REM	76	703	2572

A per-class f1 score:

Class	f1 Score
Wake	0.983
NREM	0.912
REM	0.765

For file ST7152JA, the computed output hypnogram vs the annotated labels (note that the time here is relative, not absolute):

References

https://pubmed.ncbi.nlm.nih.gov/30445569/
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0216456