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preprocessing.py
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preprocessing.py
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import pywt
import matplotlib.pyplot as plt
import matplotlib.lines as lines
import numpy as np
import scipy.io as spio
import scipy.signal as spsi
import qrsdetect as qrs
import statsmodels.tsa.ar_model as ar_model
WT_times = 5
mV = 1.0
def preprocess(data): ## data - pojedynczy wykres z spio.loadmat; jak jest wiecej wykresow to podac data[i] do funkcji
array = []
x=[]
y=[]
for index, cell in enumerate(data):
x.append(index)
y.append(float(cell)/mV)
(wtaxA,wtaxD) = makeWT(y,WT_times)
#print(min(wtaxD))
qrsArray = find_QRS(wtaxD,len(wtaxD)) ## dzialalo na wtaxD ale sie zepsulo
qrsArray = [x * (2**WT_times) for x in qrsArray]
#array = extract_arrays(wtaxD,qrsArray,WT_times)
return qrsArray,wtaxA
def makeWT(y,times): ## robi pare razy WT na approx.
(wtaxA, wtaxD) = pywt.dwt((y),'db1')
if(times > 1):
for i in range(times-1):
(wtaxA,wtaxD) = pywt.dwt((wtaxA),'db1')
ymin = min(wtaxD)
for index,x in enumerate(wtaxD):
wtaxD[index-1]=wtaxD[index-1]-ymin
ymin = min(wtaxA)
for index,x in enumerate(wtaxA):
wtaxA[index-1]=wtaxA[index-1] - ymin
return wtaxA,wtaxD
def armodel(y,cutlist):
array = []
result = []
offset = 20
for index,x in enumerate(cutlist[1:-1]):
x = int(x)
x2 = x + y[x-offset:x+offset].index(max(y[x-offset:x+offset]))
x2 = x2 - offset
i = x2 - 200
i1 = x2 + 100
array.append(y[i:i1].copy())
for x in array:
ar = ar_model.AR(x)
arfit = ar.fit(maxlag=3,method='cmle',disp = 0)
result.append(arfit)
return result
def extract_arrays(y,cutlist,times):
arfit = armodel(y,cutlist)
y,wtaxD=makeWT(y,times)
cutlist = [int(x / (2**(times))) for x in cutlist]
arrays = []
offset = 5
for index,x in enumerate(cutlist[1:-1]):
x2 = x+ y[x-offset:x+offset].tolist().index(max(y[x-offset:x+offset]))
x2=x2-offset
i = x2 -20*(2**(3-times))
i1 = x2 + 12*(2**(3-times))
arrays.append(y[i:i1].copy())
return arrays
def extract_arrays_with_arfit(y, cutlist, times):
arfit = armodel(y,cutlist)
y,wtaxD=makeWT(y,times)
cutlist = [int(x / (2**(times))) for x in cutlist]
arrays = []
arrays_with_arfit = []
offset = 5
for index,x in enumerate(cutlist[1:-1]):
x2 = x+ y[x-offset:x+offset].tolist().index(max(y[x-offset:x+offset]))
x2=x2-offset
i = x2 -20*(2**(3-times))
i1 = x2 + 12*(2**(3-times))
arrays.append(y[i:i1].copy())
arrays_with_arfit.append(np.concatenate([arrays[index],arfit[index].params]))
return arrays_with_arfit
return arrays
def find_QRS(y,n):
ymax = max(y)
divide = WT_times
if(WT_times < 1):
divide = 0
test_time = int(100/(2**divide))
#print(test_time)
iter = list(range(0,n+1,test_time)) ## co ile sprawdzany jest potencjalne R
ylen = n
###
#plt.plot(range(0,len(y)),y)
maxlist=[]
xlist=[]
v2=[]
final_list = []
y2 = []
for i in y:
y2.append(i)
for i in range(0,len(iter)-1):
maxlist.append(max(y2[iter[i]:iter[i+1]]))
v = y2[iter[i]:iter[i+1]].index(max(y2[iter[i]:iter[i+1]]))
v2.append( int(v) + int(iter[i]))
vmax = max(y)
for licz, i in enumerate(maxlist):
if(vmax*0.7 < i):
final_list.append(v2[licz])
for index in range(0,len(final_list)-1):
if(final_list[index+1]-final_list[index] < float((test_time/2))):
i=1
#print(final_list)
last = final_list[len(final_list)-1]
final_list[:] = [x for index,x in enumerate(final_list[:-1],start = 1) if not determine(final_list[index-1],final_list[index],test_time)]
if(last-final_list[len(final_list)-1] > test_time):
final_list.append(last)
# print(final_list,last)
return final_list
def determine(x1,x2,time):
#print(x2-x1,time, x1, x2)
if((x2-x1)<(time)):
return True
else:
return False
def moving_average(signal, window=101):
weights = np.repeat(1.0, window) / window
smas = np.convolve(signal, weights, 'valid')
signal = signal[(window - 1) / 2 : len(signal) - ((window - 1) / 2)]
new_signal = []
for index, x in enumerate(signal):
new_signal.append(signal[index] - smas[index])
return new_signal
def getFeatureWithQrs(data, qrs_peaks):
mV = 2000.0
multiple_arrays = []
for i in qrs_peaks:
multiple_arrays.append([])
for index, signal in enumerate(data):
data2 = []
for i in signal:
data2.append(i / mV)
arrays = extract_arrays(data2, qrs_peaks, 3)
for index,array in enumerate(arrays):
multiple_arrays[index].extend(array)
return multiple_arrays
return arrays
def getFeatureWithArWithQrs (data, qrs_peaks):
mV = 2000.0
multiple_arrays = []
for i in qrs_peaks:
multiple_arrays.append([])
for index, signal in enumerate(data):
data2 = []
for i in signal:
data2.append(i / mV)
arrays = extract_arrays_with_arfit(data2, qrs_peaks, 3)
for index,array in enumerate(arrays):
multiple_arrays[index].extend(array)
return multiple_arrays
return arrays
def getFeature(data,info):
info = {'name': "Roman", 'age': 50, 'sex': 'm', 'samplingrate' : 1000}
data2 = []
mV = 2000.0
for i in data:
data2.append(i / mV)
ecg = qrs.Ecg(data2,info)
ecg.qrsDetect(0)
arrays = extract_arrays(data2, ecg.QRSpeaks, 3)
return arrays