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dataloader.py
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dataloader.py
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import scipy.io as sio
import numpy as np
import h5py
import os
import time
import torch
import random
import dsp
# import pyedflib
import mne
# CinC_Challenge_2018
def loadstages(dirpath):
filepath = os.path.join(dirpath,os.path.basename(dirpath)+'-arousal.mat')
mat=h5py.File(filepath,'r')
# N3(S4+S3)->0 N2->1 N1->2 REM->3 W->4 UND->5
#print(mat.keys())
N3 = mat['data']['sleep_stages']['nonrem3'][0]
N2 = mat['data']['sleep_stages']['nonrem2'][0]
N1 = mat['data']['sleep_stages']['nonrem1'][0]
REM = mat['data']['sleep_stages']['rem'][0]
W = mat['data']['sleep_stages']['wake'][0]
UND = mat['data']['sleep_stages']['undefined'][0]
stages = N3*0 + N2*1 + N1*2 + REM*3 + W*4 + UND*5
return stages
def loadsignals(dirpath,name):
hea_path = os.path.join(dirpath,os.path.basename(dirpath)+'.hea')
signal_path = os.path.join(dirpath,os.path.basename(dirpath)+'.mat')
signal_names = []
for i,line in enumerate(open(hea_path),0):
if i!=0:
line=line.strip()
signal_names.append(line.split()[8])
mat = sio.loadmat(signal_path)
return mat['val'][signal_names.index(name)]
def trimdata(data,num):
return data[:num*int(len(data)/num)]
def reducesample(data,mult):
return data[::mult]
def loaddata(dirpath,signal_name,BID = 'median',filter = True):
#load
signals = loadsignals(dirpath,signal_name)
if filter:
signals = dsp.BPF(signals,200,0.2,50,mod = 'fir')
stages = loadstages(dirpath)
#resample
signals = reducesample(signals,2)
stages = reducesample(stages,2)
#Balance individualized differences
if BID == 'median':
signals = (signals*8/(np.median(abs(signals)))).astype(np.int16)
elif BID == 'std':
signals = (signals*55/(np.std(signals))).astype(np.int16)
#trim
signals = trimdata(signals,3000)
stages = trimdata(stages,3000)
#30s per lable
signals = signals.reshape(-1,3000)
stages = stages[::3000]
#del UND
stages_copy = stages.copy()
cnt = 0
for i in range(len(stages_copy)):
if stages_copy[i] == 5 :
signals = np.delete(signals,i-cnt,axis =0)
stages = np.delete(stages,i-cnt,axis =0)
cnt += 1
# print(stages.shape,signals.shape)
return signals,stages
def loaddata_sleep_edf(opt,filedir,filenum,signal_name,BID = 'median',filter = True):
filenames = os.listdir(filedir)
for filename in filenames:
if str(filenum) in filename and 'Hypnogram' in filename:
f_stage_name = filename
if str(filenum) in filename and 'PSG' in filename:
f_signal_name = filename
# print(f_stage_name)
raw_data= mne.io.read_raw_edf(os.path.join(filedir,f_signal_name),preload=True)
raw_annot = mne.read_annotations(os.path.join(filedir,f_stage_name))
eeg = raw_data.pick_channels([signal_name]).to_data_frame().values.T
eeg = eeg.reshape(-1)
raw_data.set_annotations(raw_annot, emit_warning=False)
event_id = {'Sleep stage 4': 0,
'Sleep stage 3': 0,
'Sleep stage 2': 1,
'Sleep stage 1': 2,
'Sleep stage R': 3,
'Sleep stage W': 4,
'Sleep stage ?': 5,
'Movement time': 5}
events, _ = mne.events_from_annotations(
raw_data, event_id=event_id, chunk_duration=30.)
stages = []
signals =[]
for i in range(len(events)-1):
stages.append(events[i][2])
signals.append(eeg[events[i][0]:events[i][0]+3000])
stages=np.array(stages)
signals=np.array(signals)
if BID == 'median':
signals = signals*13/np.median(np.abs(signals))
# #select sleep time
if opt.select_sleep_time:
if 'SC' in f_signal_name:
signals = signals[np.clip(int(raw_annot[0]['duration'])//30-60,0,9999999):int(raw_annot[-2]['onset'])//30+60]
stages = stages[np.clip(int(raw_annot[0]['duration'])//30-60,0,9999999):int(raw_annot[-2]['onset'])//30+60]
stages_copy = stages.copy()
cnt = 0
for i in range(len(stages_copy)):
if stages_copy[i] == 5 :
signals = np.delete(signals,i-cnt,axis =0)
stages = np.delete(stages,i-cnt,axis =0)
cnt += 1
print('shape:',signals.shape,stages.shape)
return signals.astype(np.int16),stages.astype(np.int16)
def loaddataset(opt,filedir,dataset_name = 'CinC_Challenge_2018',signal_name = 'C4-M1',num = 100 ,BID = 'median',shuffle = True):
print('load dataset, please wait...')
filenames = os.listdir(filedir)
if shuffle:
random.shuffle(filenames)
if dataset_name == 'CinC_Challenge_2018':
if num > len(filenames):
num = len(filenames)
print('num of dataset is:',num)
for i,filename in enumerate(filenames[:num],0):
try:
signal,stage = loaddata(os.path.join(filedir,filename),signal_name,BID = None)
if i == 0:
signals =signal.copy()
stages =stage.copy()
else:
signals=np.concatenate((signals, signal), axis=0)
stages=np.concatenate((stages, stage), axis=0)
except Exception as e:
print(filename,e)
elif dataset_name in ['sleep-edfx','sleep-edf']:
if num > 197:
num = 197
if dataset_name == 'sleep-edf':
filenames = ['SC4002E0-PSG.edf','SC4012E0-PSG.edf','SC4102E0-PSG.edf','SC4112E0-PSG.edf',
'ST7022J0-PSG.edf','ST7052J0-PSG.edf','ST7121J0-PSG.edf','ST7132J0-PSG.edf']
cnt = 0
for filename in filenames:
if 'PSG' in filename:
signal,stage = loaddata_sleep_edf(opt,filedir,filename[2:6],signal_name = signal_name)
if cnt == 0:
signals =signal.copy()
stages =stage.copy()
else:
signals=np.concatenate((signals, signal), axis=0)
stages=np.concatenate((stages, stage), axis=0)
cnt += 1
if cnt == num:
break
# print(np.median(np.abs(signals)))
return signals,stages