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parametersSetup.py
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Thu, Mar 28, 13:58

parametersSetup.py

''' file with all parameters'''
import numpy as np
import pickle
Dataset='01_CHBMIT' #'01_CHBMIT', '01_iEEG_Bern'
patients =['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16']
class DatasetPreprocessParams:
samplFreq = 256 # sampling frequency of data
#channels to keep
channelNamesToKeep = ['FP1-F7', 'F7-T7', 'T7-P7', 'P7-O1',
'FP1-F3', 'F3-C3', 'C3-P3', 'P3-O1',
'FP2-F4', 'F4-C4', 'C4-P4', 'P4-O2',
'FP2-F8', 'F8-T8', 'T8-P8', 'P8-O2',
'FZ-CZ', 'CZ-PZ']
#pre and post ictal data to be removed
PreIctalTimeToRemove=60 #in seconds
PostIctalTimeToRemove=600 #in seconds
#how to select and rearange data in files before feature extraction and training
FileRearangeAllData='SubselData_NonSeizRandom' #'AllData_FixedSize','AllData_StoS', 'SubselData_NonSeizAroundSeiz', 'SubselData_NonSeizRandom'
FileLen=60 #60, 240 in minutes - only for AllData_FixedSize needed
RatioNonSeizSeiz=1 #1, 10 - only for SubselData needed
#filtering parameters
BorderFreqLow=1#Hz for the bandpass butterworth filter
BorderFreqHigh=30 #Hz for the bandpass butterworth filter
#saving type
SaveType='gzip' #'csv, 'gzip'' #gzip saves a lot of memory so is recommended
class FeaturesUsedParams:
#window size and step in which is moved
winLen= 4 #in seconds, window length on which to calculate features
winStep=0.5 #in seconds, step of moving window length
#when we have more labels in one window, how final label is chosen
LabelVotingType='atLeastOne' #'majority', 'allOne', 'atLeastOne'
#features extracted from data
featNames = np.array( ['MeanAmpl', 'LineLength'])
#SAVING SETUP once again to update if new info
with open('../PARAMETERS.pickle', 'wb') as f:
pickle.dump([DatasetPreprocessParams, FeaturesUsedParams, patients], f)

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