Camila Guerrero mcguegi
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mcguegi
/ mean_data.csv
Created
August 11, 2020 03:24
Mean of each frequency band per channel in exams EEGs
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| channel,delta,tetha,alpha,beta,gamma,class | |
| EEG O1-REFEEG O1-REFEEG O1-REFEEG O1-REFEEG O1-REFEEG O1-REFEEG O1-REFEEG O1-REFEEG O1-REFEEG O1-REF,0.1485798005359792,0.0900548946359901,0.2242448276011247,0.04434381846679583,0.018261265674641952,1 | |
| EEG F3-REFEEG F3-REFEEG F3-REFEEG F3-REFEEG F3-REFEEG F3-REFEEG F3-REFEEG F3-REFEEG F3-REFEEG F3-REF,0.100885884688964,0.03982581574915123,0.050565276457377385,0.022655879595769604,0.0195413421674164,1 | |
| EEG FZ-REFEEG FZ-REFEEG FZ-REFEEG FZ-REFEEG FZ-REFEEG FZ-REFEEG FZ-REFEEG FZ-REFEEG FZ-REFEEG FZ-REF,0.11813808348982324,0.030982591578498496,0.03604851875930125,0.017402092814197554,0.011459563533253784,1 | |
| EEG T4-REFEEG T4-REFEEG T4-REFEEG T4-REFEEG T4-REFEEG T4-REFEEG T4-REFEEG T4-REFEEG T4-REFEEG T4-REF,0.21108093314616247,0.10505401050935227,0.1371458510301313,0.048901857083469356,0.040938274229057756,1 | |
| EEG P4-REFEEG P4-REFEEG P4-REFEEG P4-REFEEG P4-REFEEG P4-REFEEG P4-REFEEG P4-REFEEG P4-REFEEG P4-REF,0.1340154450185936,0.07589951921127971,0.1582936805360596,0.0368957 |
mcguegi
/ relative_power.csv
Created
August 11, 2020 03:09
Relative band power dataset for EEGs clasiffication.
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| exam,channel,class,alpha,betha,delta,gamma,theta | |
| 00002744_s002_t002.edf,EEG FZ-REF,0,0.00331264029234614,0.01927085562334667,0.9041643990974769,0.05925431676612765,0.00855319831585536 | |
| 00002744_s002_t002.edf,EEG O2-REF,0,0.005708840055366444,0.023925826695482096,0.9014548418077574,0.054774694218668284,0.00881665916328943 | |
| 00002744_s002_t002.edf,EEG FP2-REF,0,0.0059976608322190005,0.009865860069326708,0.9531339021926206,0.021581475594702314,0.008367924594705466 | |
| 00002744_s002_t002.edf,EEG T6-REF,0,0.004286715802523175,0.018283142851878157,0.9188070379868494,0.047826831320528014,0.006073010485710719 | |
| 00002744_s002_t002.edf,EEG O1-REF,0,0.009106224584520954,0.030342519319241982,0.878243881366932,0.06220264666278723,0.014379698929082127 | |
| 00002744_s002_t002.edf,EEG F4-REF,0,0.00461364000054283,0.028009744977394505,0.8339344675953961,0.12138890703328395,0.0038606227780451567 | |
| 00002744_s002_t002.edf,EEG P4-REF,0,0.003741669591018377,0.016004677873927092,0.9313113029166771,0.03940515374761982,0.006079478699094593 | |
| 00002744_ |
mcguegi
/ nn_loss_accuracy_graphs.py
Created
July 1, 2020 22:11
Fragmento de código para graficar los epochs y loss-accuracy para redes neuronales
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| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| sns.set(font_scale=2) | |
| fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(30,10)) | |
| axes.plot(scores_train,color="#34495e") | |
| axes.plot(scores_test,color="#2ecc71") | |
| axes.set_title('Precisión del modelo vs Pasos temporales') | |
| axes.set_ylabel('Valor') | |
| axes.set_xlabel('Paso temporal') |
mcguegi
/ confusion_matrix_plots.py
Created
July 1, 2020 21:00
Fragmento para las gráficas de matrices de confusión
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| import seaborn as sb | |
| import matplotlib.pyplot as plt | |
| import sklearn | |
| sns.set(rc={'figure.figsize':(11.7,8.27)},style='whitegrid',font_scale=1.5,palette=sns.cubehelix_palette(8)) | |
| columnas = ['No epiléptico','Epiléptico'] | |
| df_cm = pd.DataFrame(confusion_matrix,index=columnas, columns=columnas) | |
| grafica = sns.heatmap(df_cm,cmap=sns.cubehelix_palette(8),annot=True,fmt='g') |
mcguegi
/ signals_plots.py
Created
July 1, 2020 03:55
Gráficos señales EEG (Dominio Tiempo/Frecuencia)
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| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| # COnfigurar tamaño de la letra y paleta de seaborn con la que se trabajará | |
| sns.set(rc={'figure.figsize':(11.7,8.27)},style='whitegrid',font_scale=3,palette="PuBuGn_d") | |
| # Figura con dos subgráficos | |
| f, (ax1,ax2) = plt.subplots(2, 1, figsize=(30,10), sharex=True) | |
| ax1.plot(channel_sano,c='#2b2d42') | |
| ax1.set_title('Señal en el dominio del tiempo (Paciente No epiléptico)') |