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ecg_annotation.py
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ecg_annotation.py
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#import matplotlib with pdf as backend
import matplotlib
matplotlib.use('PDF')
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import wfdb
import os
import numpy as np
import math
import sys
import scipy.stats as st
import glob, os
from os.path import basename
import tensorflow as tf
from keras.layers import Dense,Activation,Dropout
from keras.layers import LSTM,Bidirectional #could try TimeDistributed(Dense(...))
from keras.models import Sequential, load_model
from keras import optimizers,regularizers
from keras.layers.normalization import BatchNormalization
import keras.backend.tensorflow_backend as KTF
np.random.seed(0)
# functions
def get_ecg_data(datfile):
## convert .dat/q1c to numpy arrays
recordname=os.path.basename(datfile).split(".dat")[0]
recordpath=os.path.dirname(datfile)
cwd=os.getcwd()
os.chdir(recordpath) ## somehow it only works if you chdir.
annotator='q1c'
annotation = wfdb.rdann(recordname, extension=annotator, sampfrom=0,sampto = None, pbdir=None)
Lstannot=list(zip(annotation.sample,annotation.symbol,annotation.aux_note))
FirstLstannot=min( i[0] for i in Lstannot)
LastLstannot=max( i[0] for i in Lstannot)-1
print("first-last annotation:", FirstLstannot,LastLstannot)
record = wfdb.rdsamp(recordname, sampfrom=FirstLstannot,sampto = LastLstannot) #wfdb.showanncodes()
annotation = wfdb.rdann(recordname, annotator, sampfrom=FirstLstannot,sampto = LastLstannot) ## get annotation between first and last.
annotation2 = wfdb.Annotation(recordname='sel32', extension='niek', sample=(annotation.sample-FirstLstannot), symbol = annotation.symbol, aux_note=annotation.aux_note)
Vctrecord=np.transpose(record.p_signals)
VctAnnotationHot=np.zeros( (6,len(Vctrecord[1])), dtype=np.int)
VctAnnotationHot[5]=1 ## inverse of the others
#print("ecg, 2 lead of shape" , Vctrecord.shape)
#print("VctAnnotationHot of shape" , VctAnnotationHot.shape)
#print('plotting extracted signal with annotation')
#wfdb.plotrec(record, annotation=annotation2, title='Record 100 from MIT-BIH Arrhythmia Database', timeunits = 'seconds')
VctAnnotations=list(zip(annotation2.sample,annotation2.symbol)) ## zip coordinates + annotations (N),(t) etc)
#print(VctAnnotations)
for i in range(len(VctAnnotations)):
#print(VctAnnotations[i]) # Print to display annotations of an ecg
try:
if VctAnnotations[i][1]=="p":
if VctAnnotations[i-1][1]=="(":
pstart=VctAnnotations[i-1][0]
if VctAnnotations[i+1][1]==")":
pend=VctAnnotations[i+1][0]
if VctAnnotations[i+3][1]=="N":
rpos=VctAnnotations[i+3][0]
if VctAnnotations[i+2][1]=="(":
qpos=VctAnnotations[i+2][0]
if VctAnnotations[i+4][1]==")":
spos=VctAnnotations[i+4][0]
for ii in range(0,8): ## search for t (sometimes the "(" for the t is missing )
if VctAnnotations[i+ii][1]=="t":
tpos=VctAnnotations[i+ii][0]
if VctAnnotations[i+ii+1][1]==")":
tendpos=VctAnnotations[i+ii+1][0]
# #print(ppos,qpos,rpos,spos,tendpos)
VctAnnotationHot[0][pstart:pend]=1 #P segment
VctAnnotationHot[1][pend:qpos]=1 #part "nothing" between P and Q, previously left unnanotated, but categorical probably can't deal with that
VctAnnotationHot[2][qpos:rpos]=1 #QR
VctAnnotationHot[3][rpos:spos]=1 #RS
VctAnnotationHot[4][spos:tendpos]=1 #ST (from end of S to end of T)
VctAnnotationHot[5][pstart:tendpos]=0 #tendpos:pstart becomes 1, because it is inverted above
except IndexError:
pass
Vctrecord=np.transpose(Vctrecord) # transpose to (timesteps,feat)
VctAnnotationHot=np.transpose(VctAnnotationHot)
os.chdir(cwd)
return Vctrecord, VctAnnotationHot
def splitseq(x,n,o):
#split seq; should be optimized so that remove_seq_gaps is not needed.
upper=math.ceil( x.shape[0] / n) *n
print("splitting on",n,"with overlap of ",o, "total datapoints:",x.shape[0],"; upper:",upper)
for i in range(0,upper,n):
#print(i)
if i==0:
padded=np.zeros( ( o+n+o,x.shape[1]) ) ## pad with 0's on init
padded[o:,:x.shape[1]] = x[i:i+n+o,:]
xpart=padded
else:
xpart=x[i-o:i+n+o,:]
if xpart.shape[0]<i:
padded=np.zeros( (o+n+o,xpart.shape[1]) ) ## pad with 0's on end of seq
padded[:xpart.shape[0],:xpart.shape[1]] = xpart
xpart=padded
xpart=np.expand_dims(xpart,0)## add one dimension; so that you get shape (samples,timesteps,features)
try:
xx=np.vstack( (xx,xpart) )
except UnboundLocalError: ## on init
xx=xpart
print("output: ",xx.shape)
return(xx)
def remove_seq_gaps(x,y):
#remove parts that are not annotated <- not ideal, but quickest for now.
window=150
c=0
cutout=[]
include=[]
print("filterering.")
print("before shape x,y",x.shape,y.shape)
for i in range(y.shape[0]):
c=c+1
if c<window :
include.append(i)
if sum(y[i,0:5])>0:
c=0
if c >= window:
#print ('filtering')
pass
x,y=x[include,:],y[include,:]
print(" after shape x,y",x.shape,y.shape)
return(x,y)
def normalizesignal(x):
x=st.zscore(x, ddof=0)
return x
def normalizesignal_array(x):
for i in range(x.shape[0]):
x[i]=st.zscore(x[i], axis=0, ddof=0)
return x
def plotecg(x,y,begin,end):
#helper to plot ecg
plt.figure(1,figsize=(11.69,8.27))
plt.subplot(211)
plt.plot(x[begin:end,0])
plt.subplot(211)
plt.plot(y[begin:end,0])
plt.subplot(211)
plt.plot(y[begin:end,1])
plt.subplot(211)
plt.plot(y[begin:end,2])
plt.subplot(211)
plt.plot(y[begin:end,3])
plt.subplot(211)
plt.plot(y[begin:end,4])
plt.subplot(211)
plt.plot(y[begin:end,5])
plt.subplot(212)
plt.plot(x[begin:end,1])
plt.show()
def plotecg_validation(x,y_true,y_pred,begin,end):
#helper to plot ecg
plt.figure(1,figsize=(11.69,8.27))
plt.subplot(211)
plt.plot(x[begin:end,0])
plt.subplot(211)
plt.plot(y_pred[begin:end,0])
plt.subplot(211)
plt.plot(y_pred[begin:end,1])
plt.subplot(211)
plt.plot(y_pred[begin:end,2])
plt.subplot(211)
plt.plot(y_pred[begin:end,3])
plt.subplot(211)
plt.plot(y_pred[begin:end,4])
plt.subplot(211)
plt.plot(y_pred[begin:end,5])
plt.subplot(212)
plt.plot(x[begin:end,1])
plt.subplot(212)
plt.plot(y_true[begin:end,0])
plt.subplot(212)
plt.plot(y_true[begin:end,1])
plt.subplot(212)
plt.plot(y_true[begin:end,2])
plt.subplot(212)
plt.plot(y_true[begin:end,3])
plt.subplot(212)
plt.plot(y_true[begin:end,4])
plt.subplot(212)
plt.plot(y_true[begin:end,5])
def LoaddDatFiles(datfiles):
for datfile in datfiles:
print(datfile)
if basename(datfile).split(".",1)[0] in exclude:
continue
qf=os.path.splitext(datfile)[0]+'.q1c'
if os.path.isfile(qf):
#print("yes",qf,datfile)
x,y=get_ecg_data(datfile)
x,y=remove_seq_gaps(x,y)
x,y=splitseq(x,1000,150),splitseq(y,1000,150) ## create equal sized numpy arrays of n size and overlap of o
x = normalizesignal_array(x)
## todo; add noise, shuffle leads etc. ?
try: ## concat
xx=np.vstack( (xx,x) )
yy=np.vstack( (yy,y) )
except NameError: ## if xx does not exist yet (on init)
xx = x
yy = y
return(xx,yy)
def unison_shuffled_copies(a, b):
assert len(a) == len(b)
p = np.random.permutation(len(a))
return a[p], b[p]
def get_session(gpu_fraction=0.8):
#allocate % of gpu memory.
num_threads = os.environ.get('OMP_NUM_THREADS')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction)
if num_threads:
return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, intra_op_parallelism_threads=num_threads))
else:
return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
def getmodel():
model = Sequential()
model.add(Dense(32,W_regularizer=regularizers.l2(l=0.01), input_shape=(seqlength, features)))
model.add(Bidirectional(LSTM(32, return_sequences=True)))#, input_shape=(seqlength, features)) ) ### bidirectional ---><---
model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(Dense(64, activation='relu',W_regularizer=regularizers.l2(l=0.01)))
model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(Dense(dimout, activation='softmax'))
adam = optimizers.adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
print(model.summary())
return(model)
##################################################################
##################################################################
qtdbpath=sys.argv[1] ## first argument = qtdb database from physionet.
perct=0.81 #percentage training
percv=0.19 #percentage validation
exclude = set()
exclude.update(["sel35","sel36","sel37","sel50","sel102","sel104","sel221","sel232", "sel310"])# no P annotated:
##################################################################
# datfile=qtdbpath+"sel49.dat" ## single ECG to test if loading works.
# x,y=get_ecg_data(datfile)
# print(x.shape,y.shape)
# # for i in range(y.shape[0]): #Invert QT-label to actually represent QT. Does give overlapping labels
# # y[i][0] = 1 - y[i][0]
# plotecg(x,y,0,y.shape[0]) ## plot all
# x,y=remove_seq_gaps(x,y) ## remove 'annotation gaps'
# plotecg(x,y,0,y.shape[0]) ## plot all
# x,y=splitseq(x,750,150),splitseq(y,750,150) ## create equal sized numpy arrays of n size and overlap of o
# exit()
##################################################################
# load data
datfiles=glob.glob(qtdbpath+"*.dat")
xxt,yyt=LoaddDatFiles(datfiles[ :round(len(datfiles)*perct) ]) # training data.
xxt,yyt=unison_shuffled_copies(xxt,yyt) ### shuffle
xxv,yyv=LoaddDatFiles(datfiles[ -round(len(datfiles)*percv): ] ) ## validation data.
seqlength=xxt.shape[1]
features=xxt.shape[2]
dimout=yyt.shape[2]
print("xxv/validation shape: {}, Seqlength: {}, Features: {}".format(xxv.shape[0],seqlength,features))
# #plot validation ecgs
# with PdfPages('ecgs_xxv.pdf') as pdf:
# for i in range( xxv.shape[0] ):
# print (i)
# plotecg(xxv[i,:,:],yyv[i,:,:],0,yyv.shape[1])
# pdf.savefig()
# plt.close()
# call keras/tensorflow and build lstm model
KTF.set_session(get_session())
with tf.device('/cpu:0'): #switch to /cpu:0 to use cpu
if not os.path.isfile('model.h5'):
model = getmodel() # build model
model.fit(xxt, yyt, batch_size=32, epochs=100, verbose=1) # train the model
model.save('model.h5')
model = load_model('model.h5')
score, acc = model.evaluate(xxv, yyv, batch_size=4, verbose=1)
print('Test score: {} , Test accuracy: {}'.format(score, acc))
# predict
yy_predicted = model.predict(xxv)
# maximize probabilities of prediction.
for i in range(yyv.shape[0]):
b = np.zeros_like(yy_predicted[i,:,:])
b[np.arange(len(yy_predicted[i,:,:])), yy_predicted[i,:,:].argmax(1)] = 1
yy_predicted[i,:,:] = b
# plot:
with PdfPages('ecg.pdf') as pdf:
for i in range( xxv.shape[0] ):
print (i)
plotecg_validation(xxv[i,:,:],yy_predicted[i,:,:],yyv[i,:,:],0,yy_predicted.shape[1]) # top = predicted, bottom=true
pdf.savefig()
plt.close()
#plotecg(xv[1,:,:],yv[1,:,:],0,yv.shape[1]) ## plot first seq