Sleep in patients with disorders of consciousness characterized by means of machine learning DATA

Data underlying findings described in the manuscript: Wielek et al., 'Sleep in patients with disorders of consciousness characterized by means of machine learning'.<br><b>Subfolders:</b><br>=&gt; <b>DOC_classif:</b> .csv files for each patient with epoch-wise: classifier predictions, eyes state and day/night period information. Python script <b>perfMetrics4DOC_pl.py </b>computes average classification performance and reproduces Fig.4<br>=&gt;<b> DOC_clustering</b>: .RData files with sampled and everaged epochs (sampling from each subject and averaging separatelly across UWS and MCS). R script <b>hierarchClust_DOC_pl.R</b> computes cluster analysis and plots heatmaps, used to generate Fig.2<br>=&gt; <b>healthy_classif: .txt </b>files containing classifier prediction and ground truth for each healthy. <b>f1weighted4healthyPred_pl.py</b> reproduces Fig.3 based on data: <br> *_nn ending: neuronal networks classification (e.g: 1trueVSpred_nn.txt)<br> *_dummy ending: dummy classification, chance estimate: (e.g.: 1trueVSpred_dummy.txt)<br> *no additional ending: random forest classification: (e.g.: 1trueVSpred.txt)<br> *subfolders; 14electrodes and 26electrodes: classification based on diff. #channels<br> *subfolders; tau1 and tau3: PE computed with different tau parameter<br><br> <br>