#time in mins ##### Extract RR intervals here ####### RR_sec_unclean = pr.get_RR_interval(rec_name, annotation, start_time, end_time) ####DELETE THE FIRST RR_sec_unclean value##### del RR_sec_unclean[0] ##### APPLY FILTERS TO CLEAN DATA ####### #RR_sec=dc.detrend_data(dc.quotient_filt(dc.square_filt(RR_sec_unclean))) #RR_sec=dc.detrend_data((RR_sec_unclean) RR_sec = dc.quotient_filt(dc.square_filt(RR_sec_unclean)) #RR_sec=dc.square_filt(RR_sec_unclean) #RR_sec=RR_sec_unclean ##### Extract delta RR intervals here ####### delta_RR_sec = pr.get_delta_rr(RR_sec) ###### Calculating GOLBAL statistical features for RR_sec VALUES ################# ###calculating AVG/mean of RR intervals ### mean_global = np.mean(RR_sec) * 1000 mean_global_arr.append(mean_global) #do we need this? feature_rec.append(mean_global) global_vocab, index_of_features = cl.fill_global_vocab( "mean_global", index_of_features, global_vocab) # if "mean_global" not in global_vocab.keys():
RR_sec_unclean=pr.get_RR_interval(rec_name,annotation,start_time,end_time) print RR_sec_unclean ####DELETE THE FIRST RR_sec_unclean value##### del RR_sec_unclean[0]; ##### APPLY FILTERS TO CLEAN DATA ####### #RR_sec=dc.detrend_data(dc.quotient_filt(dc.square_filt(RR_sec_unclean))) #RR_sec=dc.detrend_data((RR_sec_unclean) #RR_sec=dc.quotient_filt(dc.square_filt(RR_sec_unclean)) RR_sec=dc.square_filt(RR_sec_unclean) #RR_sec=RR_sec_unclean ##### Extract delta RR intervals here ####### #if "n" in record: ####plot sodp and save fig #### #fig_rr,plot_rr=graphs.plotScatter(rec_name,range(len(RR_sec)),RR_sec, "RR interval count", " RR interval (ms)", "Raw RR interval plot", 'b',xlim_lo=0, xlim_hi=80, ylim_lo=0.4, ylim_hi=1.0,axline=0) #fig_rr.savefig(output_folder+"raw_rr_scatter_"+record+".pdf",format='pdf') #if "p" in record: ####plot sodp and save fig #### #fig_rr,plot_rr=graphs.plotScatter(rec_name,range(len(RR_sec)),RR_sec, "RR interval count", " RR interval (ms)", "Raw RR interval plot", 'r',xlim_lo=0, xlim_hi=80, ylim_lo=0.4, ylim_hi=1.0,axline=0) #fig_rr.savefig(output_folder+"raw_rr_scatter_"+record+".pdf",format='pdf') delta_RR_sec = pr.get_delta_rr(RR_sec); total_min=30; ##### Calculating std of 5min features in all 6 intervals in 30min sodp features #################
##### Extract RR intervals here ####### RR_sec_unclean = pr.get_RR_interval(rec_name, annotation, start_time, end_time) ####DELETE THE FIRST RR_sec_unclean value##### del RR_sec_unclean[0] ##### APPLY FILTERS TO CLEAN DATA ####### #RR_sec=dc.detrend_data(dc.quotient_filt(dc.square_filt(RR_sec_unclean))) #RR_sec=dc.detrend_data((RR_sec_unclean) #RR_sec=dc.quotient_filt(dc.square_filt(RR_sec_unclean)) RR_sec = dc.square_filt(RR_sec_unclean) #RR_sec=RR_sec_unclean ##### Extract delta RR intervals here ####### delta_RR_sec = pr.get_delta_rr(RR_sec) ###### Calculating GOLBAL statistical features for RR_sec VALUES ################# ###calculating AVG/mean of RR intervals ### mean_global = np.mean(RR_sec) * 1000 mean_global_arr.append(mean_global) #do we need this? feature_rec.append(mean_global) global_vocab, index_of_features = cl.fill_global_vocab( "mean_global", index_of_features, global_vocab) # if "mean_global" not in global_vocab.keys(): # global_vocab["mean_global"]=index_of_features;
##### Extract RR intervals here ####### RR_sec_unclean=pr.get_RR_interval(rec_name,annotation,start_time,end_time) ####DELETE THE FIRST RR_sec_unclean value##### del RR_sec_unclean[0]; ##### APPLY FILTERS TO CLEAN DATA ####### #RR_sec=dc.detrend_data(dc.quotient_filt(dc.square_filt(RR_sec_unclean))) #RR_sec=dc.detrend_data((RR_sec_unclean) RR_sec=dc.quotient_filt(dc.square_filt(RR_sec_unclean)) #RR_sec=dc.square_filt(RR_sec_unclean) #RR_sec=RR_sec_unclean ##### Extract delta RR intervals here ####### delta_RR_sec = pr.get_delta_rr(RR_sec); ###### Calculating GOLBAL statistical features for RR_sec VALUES ################# ###calculating AVG/mean of RR intervals ### mean_global=np.mean(RR_sec)*1000; mean_global_arr.append(mean_global) #do we need this? feature_rec.append(mean_global) global_vocab,index_of_features=cl.fill_global_vocab("mean_global", index_of_features, global_vocab) # if "mean_global" not in global_vocab.keys():