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reading_dataset.py
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reading_dataset.py
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import pickle
import regex as re
import os
import matplotlib.pyplot as plt
import scipy.io
import numpy as np
import pickle as pkl
class data ():
"""Class to represent ECG data."""
def __init__(self,index,mat,age, sex,Dx,samples
,sample_rate,Rx=None,Hx=None,Sx=None,folder=None) :
self.folder=folder
self.index=index
self.age=age
self.sex=sex
self.dx=Dx
self.rx=Rx
self.hx=Hx
self.samples=samples
self.sample_rate=sample_rate
self.mat=mat
def find_duration(self):
if self.samples != None:
self.duration=self.samples /self.sample_rate
return self.duration
def intersection(lst1, lst2):
lst3 = [value for value in lst1 if value in lst2]
return lst3
def get_location(folder_number):
folders=['1-Training_WFDB','2-Training_2','3-PhysioNetChallenge2020_Training_StPetersburg'
,'4-PhysioNetChallenge2020_Training_PTB','5- PhysioNetChallenge2020_Training_PTB-XL','6-PhysioNetChallenge2020_Training_E']
location= 'dataset\\'+folders[folder_number] +'\\'
pishvands=['A','Q','I','S','HR','E']
pishvand=pishvands[folder_number]
zfilling_number= [5 if folder_number==5 or folder_number==4 else 4][0]
return location , pishvand , zfilling_number
def get_ecg(index,folder_number):
location , pishvand , zfilling_number=get_location(folder_number)
ecg_annotation_file=f'{location}{pishvand}{(str(index)).zfill(zfilling_number)}.hea'
ecg_mat_file=f'{location}{pishvand}{(str(index)).zfill(zfilling_number)}.mat'
return ecg_mat_file, ecg_annotation_file
def read_ecg(index,folder_number=5 ,):
"""Read ECG data from files and create an ECGData object."""
ecg_mat_file,ecg_annotation_file=get_ecg(index,folder_number)
mat = scipy.io.loadmat(ecg_mat_file)
np.seterr(invalid='ignore')
mat=mat['val']
with open (ecg_annotation_file, 'r') as file:
lines=file.readlines()
first_line=lines[0]
first_line=first_line.split(' ')
ecg_sample_rate=float(first_line[2])
ecg_samples=float(first_line[3])
age_line=lines[13]
ecg_age=re.findall(r'Age:\s(\d*)',age_line)[0]
sex_line=lines[14]
ecg_sex=re.findall(r'Sex:\s(.*)',sex_line)[0]
dx_line=lines[15]
ecg_dx=re.findall(r'\d+',dx_line)
ecg=data (folder=folder_number ,index=index,mat=mat, age=ecg_age, sex=ecg_sex,
Dx=ecg_dx, sample_rate=ecg_sample_rate, samples=ecg_samples,)
ecg_duration=ecg.find_duration()
return ecg
def dx2diagnosis(dx):
diagnosis_dict=pkl.load(open('dataset\\dx.pkl' , 'rb'))
return (diagnosis_dict[dx[0]])
def plot_ecg (ecg,time=None,channels=None ,folder_number=5
, custom_order=True , just_limb=False,subtitle=True):
mat=ecg.mat
if time != None:
assert type(time)==tuple
if channels != None:
subplot(channels,ecg,mat)
else:
plot_mat(mat,time ,custom_order , just_limb=just_limb)
if subtitle ==True:
diagnosis=dx2diagnosis(ecg.dx)
plt.suptitle(f'index:{ecg.index} , age:{ecg.age} , sex:{ecg.sex} , dx:{str(diagnosis)} '
, fontsize=9 )
def plot_mat (mat, time=None, custom_order=True, sample_rate=500, just_limb=False,title=None):
samples= mat.shape[1]
long=samples/sample_rate
if time==None:
time=(0,long)
x=(np.arange(samples, )/sample_rate)[:samples]
y=mat/1000
y_of_time=y[:,int(time[0]*sample_rate):int(time[1]*sample_rate)]
maxy=y_of_time.max()
miny=y_of_time.min()
if custom_order == True:
y_new=np.empty((12,samples))
y_new[0,:]=mat[4,:]
y_new[1,:]=mat[0,:]
y_new[2,:]=-mat[3,:]
y_new[3,:]=mat[1,:]
y_new[4,:]=mat[5,:]
y_new[5,:]=mat[2,:]
if just_limb==False:
y_new[6:12,:]=mat[6:12,:]
leads =['aVL','I','-aVR','II','aVF','III','V1','V2','V3','V4','V5','V6']
mat=y_new
else:
leads=['I','II','III','aVR','aVL','aVF','V1','V2','V3','V4','V5','V6']
mat=mat/1000
if just_limb==False:
fig, ax = plt.subplots(6,2)
if title!=None:
plt.title (f'{title}')
for i in range (6):
for j in range (2):
y=mat[i+6*j,:]
ax[i,j].plot(x,y)
ax[i,j].set_ylabel (leads[i+6*j] )
small_vertical=np.arange(0,long ,0.04)
big_vertical=np.arange(0,long,0.2)
[ax[i,j].axvline(x=k, linestyle='--',linewidth=0.2 ) for k in small_vertical]
[ax[i,j].axvline(x=k, linestyle='--',linewidth=0.5 ) for k in big_vertical]
small_horizontal=np.arange(-3,+3 ,0.1)
big_horizontal=np.arange(-3,+3,0.5)
[ax[i,j].axhline(y=k, linestyle='--',linewidth=0.2 ) for k in small_horizontal]
[ax[i,j].axhline(y=k, linestyle='--',linewidth=0.5 ) for k in big_horizontal]
ax[i,j].axis([time[0], time[1],miny,maxy])
else:
fig, ax = plt.subplots(6)
if title!=None:
fig.suptitle (f'{title}' , fontdict={'fontfamily':'serif'})
j=0
mat=mat[:6,:]
maxy=y_of_time.max()
miny=y_of_time.min()
for i in range (6):
y=mat[i,:]
ax[i].plot(x,y)
ax[i].set_ylabel(leads[i] , fontdict={'fontfamily':'serif' ,'fontsize':11, })
# ax[i].set_yticklabels(ax[i].get_xticks(), rotation=0, size=9)
# if i != 5:
# ax[i].set_xticks([])
small_vertical=np.arange(0,long ,0.04)
big_vertical=np.arange(0,long,0.2)
[ax[i].axvline(x=k, linestyle='--',linewidth=0.2 ) for k in small_vertical]
[ax[i].axvline(x=k, linestyle='--',linewidth=0.5 ) for k in big_vertical]
small_horizontal=np.arange(-3,+3 ,0.1)
big_horizontal=np.arange(-3,+3,0.5)
[ax[i].axhline(y=k, linestyle='--',linewidth=0.2 ) for k in small_horizontal]
[ax[i].axhline(y=k, linestyle='--',linewidth=0.5 ) for k in big_horizontal]
ax[i].axis([time[0], time[1],miny,maxy])
plt.subplots_adjust(left=0.11, bottom=0.1, right=0.95, top=0.93, wspace=.12, hspace=0.22)
def subplot (channels , ecg , mat):
leads=['I','II','III','aVR','aVL','aVF'
,'V1','V2','V3','V4','V5','V6']
x=np.arange(ecg.samples, )/ecg.sample_rate
if type(channels)!=list and channels!=None :
plt.plot(x,mat [channels ,:]/1000)
plt.ylabel(leads[channels])
else:
if channels==None:
channels=[i for i in range(12)]
x=np.arange(ecg.samples, )/ecg.sample_rate
num=len(channels)
fig, ax = plt.subplots(num)
for i in range (num):
y=mat[channels[i],:]/1000
maxy=y.max()
miny=y.min()
tool=ecg.duration
small_vertical=np.arange(0,tool ,0.04)
big_vertical=np.arange(0,tool,0.2)
[ax[i].axvline(x=j, linestyle='--',linewidth=0.1 ) for j in small_vertical]
[ax[i].axvline(x=j, linestyle='--',linewidth=0.5 ) for j in big_vertical]
small_horizontal=np.arange(miny,maxy ,0.1)
big_horizontal=np.arange(miny,maxy,0.5)
[ax[i].axhline(y=j, linestyle='--',linewidth=0.1 ) for j in small_horizontal]
[ax[i].axhline(y=j, linestyle='--',linewidth=0.5 ) for j in big_horizontal]
ax[i].plot(x,y)
ax[i].set_ylabel (leads[channels[i]])
plt.subplots_adjust(left=0.11, bottom=0.02, right=0.98, top=0.98, wspace=0, hspace=0)
fig.suptitle(f'index:{ecg.index} , age:{ecg.age} , sex:{ecg.sex} ' , fontsize=9 )
def find_normals(folder_number=5):
location=get_location(folder_number)[0]
number_of_files=int (len([name for name in os.listdir(location)
if os.path.isfile(os.path.join(location, name))])/2)
count=0
normal_numbers=[]
for i in range (1,number_of_files+1):
ecg=read_ecg(i , folder_number)
if ecg.dx==['426783006']:
normal_numbers.append(i)
count+=1
return normal_numbers , count