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C301
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1. Dataset: c301_raw_data c301_raw_data: raw_data for EEG signal and label for data label Each patient has 20 channels, including 19 EEG and 1 ECG. ID Seizure events (Tmin - Tmax) (s) Total Seizure time (s) Total seizure-free time (min) 1 26 (7.4 - 84.4) 980.0 34.5 2 53 (2.0 - 21.8) 300.8 45.8 3 20 (12.1 - 45.3) 499.8 42.5 4 16 (1.6 - 45.3) 89.4 49.3 5 10 (1.6 - 8.3) 45.3 50.1 6 6 (5.1 - 14.1) 55.5 49.9 7 1 (7.8 - 7.8) 7.8 50.7 8 12 (3.5 - 8.6) 78.3 49.5 9 5 (3.1 - 18.6) 51.4 50.0 10 17 (1.2 - 12.5) 129.2 48.7 11 10 (2.3 - 11.3) 80.8 49.5 12 8 (2.0 - 7.2) 44.3 50.1 13 6 (2.7 - 9.4) 34.4 50.2 14 16 (2.3 - 42.1) 179.8 47.8 15 20 (2.3 - 13.7) 133.5 48.6 16 20 (2.3 - 11.7) 133.5 48.6 17 12 (2.0 - 15.6) 95.4 49.2 18 8 (1.6 - 7.4) 41.1 50.1 19 12 (8.6 - 22.5) 206.4 47.4 Sum 258 3186.6 912.4 We combine all 19 patients from the c301 datasets into seizure, non-seizure-1, and non-seziure-2 by choosing the more clean dataset with 4s windows at the start of seizure/non-seizure by sliding_window.m. (non-seizure, seizure) = (9992, 757) The read_data.m can be used to read and analyze the C301 raw data. *We have not uploaded the entire C301 raw data because of space limitation on github. 2. Code: Data_preprocessing.py: c301 datasets are processed by Discrete Wavelet Transform. DBM_train.m: Please use the DBM model to train C301 data for 2 to 10-dimensional outputs. Each layer of training is performed in RBM1--RBM2--RBM3--RBM4 ----> *.m, and one can get the result of two states['transient', 'converged'] All of the results shall be saved under "Different_dimension". Contrast_model.py: The code in this part includes four training models: ['KPCA', 'Isomap', 't-SNE', 'UMAP'], KPCA, Isomap, and UMAP can generate dimensions from 2 to 10, and t-SNE can generate dimensions from 2 to 3. All of the results shall be saved under "Different_dimension". SVM+Fisher_Discriminiant.py: SVM and Fisher discriminant are used to evaluate results from Different_dimensions, and all of the results shall be saved under "Evaluation_results." 2D_visualization.py: The 2D data can be used to visualize the output results for all 6 methods. measure_bar: In order to evaluate the performance of different dimensions, bar plots are used, which shall save the results in "Evaluation_results/Result_bar".