Data and scripts used in: "autoMEA: Machine learning-based burst detection for multi-electrode array datasets"

This dataset contains the data and scripts used in "autoMEA: Machine learning-based burst detection for multi-electrode array datasets".

The MEA datasets used to train the machine learning models are saved in 'data/datasets/'. The datasets used to validate the models, and compare their results with manual analysis are saved in 'data/datasets_validation/'.

The following steps can be followed to reproduce the training and evaluation of the machine learning models used to predict MaxInterval parameters.

select_5sec_windows.ipynb

Program used to analyze 5-second windows with signal, spikes, and selected max interval parameters 

Input: '.h5' MEA datasets in 'data/datasets/'

Output: 'trainset_info.dat' file containing information about training data (points to h5 dataset files with recorded signal), saved in 'data/'

generate_trainset.py

Generates a trainset file with 5-second slices of the signal (as a float array), and corresponding spikes and bursts (binary array)

 Input: 'data/trainset_info.dat'

Output:

'trainset.hdf5' file containing slices with signal, spikes, and bursts

'data_channel.hdf5' file containing signal of channels used in trainset

split_trainset.py

Splits the full training data into training, validation, and test sets, with spikes and signal arrays being the moving average of the original slices

Input: 'data/trainset_info.dat', 'trainset.hdf5'

Output:

'training_indices.hdf5' file with the indices of 'trainset.hdf5' that are part of the training/validation/test set

train, validation, and test set '.hdf5' files stored in 'data/spikes/' for the spike30 model and in 'data/signal/' for signal30 and signal100 models

train_models.py

Define and train machine learning models that predict max interval parameters

Input: 'data/trainset.hdf5', 'data/training_indices.hdf5', training and validation sets in 'data/spikes/' and 'data/signal/'

Output:

'.h5' files containing trained models, stored in 'models/'

'.csv' files containing custom accuracy and loss for each training epoch, for all models, saved in 'data/custom_accuracy/'

plot_custom_accuracy.py

Generates plot with custom accuracy metric

Input: '.csv' files in 'data/custom_accuracy/'

Output: plot of custom accuracy vs epoch 

generate_pred_vs_def_bursts.py

Generates a 5-second window with signal, spikes, and bursts detected using both the machine learning approach,and by using default Max Interval parameters

Input:

trained machine learning models '.h5' files from 'models/'

'trainset.hdf5', 'trainset_info.dat', and 'data_channel/hdf5' from 'data/'

test sets saved in 'data/spikes/' and 'data/signal/'

Output: '.png' images with 5-second slices with signal, spikes, and bursts detect using the machine learning model method, and the default max interval parameters, stored in 'data/burst_quality/images/'

GUI.py

Graphical Interface used to generate burst_quality metric

Input: images in 'data/burst_quality/images/'

Output: 'burst_quality.txt' file containing the burst quality metric value, in 'GUI/'

 plot_burst_quality.py

Generates plot with histogram showing burst_quality metric

Input: 'GUI/burst_quality.txt'

Output: histogram plot of burst quality metric