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import sys
from biosppy.signals import ecg
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats.stats import pearsonr
from astropy.stats import LombScargle
import math
from random import randint
f = open(sys.argv[1], "r")
dataEcg = []
dataX = []
def getEcgDataFromFile(file=f, delimiter="", positionInCsvFile=2):
for line in f:
lineValues = line.split()
dataEcg.append(float(lineValues[2]))
dataX.append(float(lineValues[0]))
return dataEcg
dataEcg = getEcgDataFromFile()
def getEcgSignal(file=f, delimiter="", positionInCsvFile=2):
return ecg.ecg(signal=dataEcg, sampling_rate=1000., show=False)
th = float(sys.argv[2])
if len(sys.argv) >= 4:
percenteageForFakeCoeffs = int(sys.argv[3])
ecgSignal = getEcgSignal()
def plotEcgSignal(file=f, delimiter="", positionInCsvFile=2):
return ecg.ecg(signal=getEcgDataFromFile(), sampling_rate=1000., show=True)
def getRRTachogram(rPeaks):
rrTachogram = []
prevPeak = rPeaks[0]
for peak in rPeaks[1:(len(rPeaks))]:
rrTachogram.append(peak - prevPeak)
prevPeak = peak
return (rPeaks, rrTachogram)
def getMedianHeartbeatTemplate(templatesForCorrCoef, start=0, end=-1):
cleanTemplates = templatesForCorrCoef[start:end]
medianTemplate = [x / len(cleanTemplates) for x in [sum(x) for x in zip(*cleanTemplates)]]
return medianTemplate
def getCorrelationCoefficients(templatesForCorrCoef, medianTemplate):
corrCoeffs = []
for template in templatesForCorrCoef:
corrCoeffs.append(pearsonr(template, medianTemplate)[0])
return corrCoeffs
def getRRTachogramAfterSQI(rrTachogram, corrCoeffs, rPeaks):
rrTachogramAfterSqi = []
tPeaks = []
cnt = 1
for peak in rrTachogram:
if corrCoeffs[cnt] >= th:
if corrCoeffs[cnt - 1] >= th:
rrTachogramAfterSqi.append(peak)
tPeaks.append(float(float(rPeaks[cnt]) / 1000))
cnt = cnt + 1;
return (tPeaks, rrTachogramAfterSqi)
def getRRTachogramAfterSQIWithNones(rrTachogram, corrCoeffs, rPeaks):
rrTachogramAfterSqi = []
tPeaks = []
cnt = 1
for peak in rrTachogram:
if corrCoeffs[cnt] >= th:
if corrCoeffs[cnt - 1] >= th:
rrTachogramAfterSqi.append(peak)
tPeaks.append(float(float(rPeaks[cnt]) / 1000))
else:
rrTachogramAfterSqi.append(None)
tPeaks.append(float(float(rPeaks[cnt]) / 1000))
else:
rrTachogramAfterSqi.append(None)
tPeaks.append(float(float(rPeaks[cnt]) / 1000))
cnt = cnt + 1
return (tPeaks, rrTachogramAfterSqi)
def movingaverage (values, window):
weights = np.repeat(1.0, window)/window
sma = np.convolve(values, weights, 'valid')
return sma
def getLombScarglePeriodogram(peaks, tachogram, minFreq=0, maxFreq=.4, norm='standard'):
ls = LombScargle(peaks, tachogram)
freq, power = ls.autopower(minimum_frequency=minFreq, maximum_frequency=maxFreq, normalization=norm)
print(sumWithNan(power))
return (freq, power)
def getPower(tPeaks, rrTachogramAfterSqi, minFreq=0, maxFreq=.4, norm='standard'):
component = 0
power = getLombScarglePeriodogram(tPeaks, rrTachogramAfterSqi, minFreq, maxFreq, norm)[1]
return sumWithNan(power)
def sumWithNan(lst):
total = 0
for item in lst:
if not math.isnan(item):
total += item
return total
def getRatioHFLF(tPeaks, rrTachogramAfterSqi):
lfPower = getPower(tPeaks, rrTachogramAfterSqi, 0.04, 0.15)
hfPower = getPower(tPeaks, rrTachogramAfterSqi, 0.15, 0.4)
return (float(hfPower) / float(lfPower))
def getComponent(tPeaks, rrTachogramAfterSqi, lower, upper):
vlfPower = getPower(tPeaks, rrTachogramAfterSqi, lower, upper)
return (vlfPower)
def plot(x,y):
plt.plot(x,y)
plt.show()
def plotLombScarglePeriodogram():
#ecgSignal = getEcgSignal()
rPeaks = ecgSignal[2]
(rPeaks,rrTachogram) = getRRTachogram(rPeaks)
medianTemplate = getMedianHeartbeatTemplate(ecgSignal[4])
corrCoeffs = getCorrelationCoefficients(ecgSignal[4],medianTemplate)
(tPeaks, rrTachogramAfterSqi) = getRRTachogramAfterSQI(rrTachogram, corrCoeffs, rPeaks)
(freq, power) = getLombScarglePeriodogram(tPeaks, rrTachogramAfterSqi)
#print(getRatioHFLF(tPeaks, rrTachogramAfterSqi))
#power = movingaverage(power, 100)
#freq = np.linspace(0, .4, len(power))
plot(freq, power)
def plotLombScarglePeriodogramRaw():
#ecgSignal = getEcgSignal()
rPeaks = ecgSignal[2]
(rPeaks,rrTachogram) = getRRTachogram(rPeaks)
rPeaksCorrected = []
rPeaks = rPeaks.tolist()
for peak in rPeaks:
rPeaksCorrected.append(float(float(peak) / 1000))
(freq, power) = getLombScarglePeriodogram(rPeaksCorrected[1:len(rPeaks)], rrTachogram)
#print(getRatioHFLF(tPeaks, rrTachogramAfterSqi))
plot(freq, power)
def createFakeCorrelationCoefficients(nBeats, percenteage):
fakeBeats = nBeats * percenteage / 100
fakeCorrCoeffs = [1] * nBeats
while checkIfPercenteageReached(fakeCorrCoeffs, percenteage) == False:
randPos = randint(0, len(fakeCorrCoeffs) - 1)
fakeCorrCoeffs[randPos] = 0
return fakeCorrCoeffs
def checkIfPercenteageReached(lst, percenteage):
zeros = 0
for item in lst:
if item == 0:
zeros += 1
if float(zeros) / len(lst) * 100 >= percenteage:
return True
else:
return False
def plotLombScarglePeriodogramWithFakeCorrelationCoefficients(percenteage):
rPeaks = ecgSignal[2]
(rPeaks,rrTachogram) = getRRTachogram(rPeaks)
medianTemplate = getMedianHeartbeatTemplate(ecgSignal[4])
corrCoeffs = createFakeCorrelationCoefficients(len(ecgSignal[4]), percenteage)
(tPeaks, rrTachogramAfterSqi) = getRRTachogramAfterSQI(rrTachogram, corrCoeffs, rPeaks)
(freq, power) = getLombScarglePeriodogram(tPeaks, rrTachogramAfterSqi)
return getRatioHFLF(tPeaks, rrTachogramAfterSqi)
def plotLombScarglePeriodogramWithFakeCorrelationCoefficientsVLFToHFLF(percenteage):
rPeaks = ecgSignal[2]
(rPeaks,rrTachogram) = getRRTachogram(rPeaks)
medianTemplate = getMedianHeartbeatTemplate(ecgSignal[4])
corrCoeffs = createFakeCorrelationCoefficients(len(ecgSignal[4]), percenteage)
(tPeaks, rrTachogramAfterSqi) = getRRTachogramAfterSQI(rrTachogram, corrCoeffs, rPeaks)
(freq, power) = getLombScarglePeriodogram(tPeaks, rrTachogramAfterSqi)
ratioHFLF = getRatioHFLF(tPeaks, rrTachogramAfterSqi)
vlf = getComponent(tPeaks, rrTachogramAfterSqi, 0.0, 0.04)
lf = getComponent(tPeaks, rrTachogramAfterSqi, 0.04, 0.15)
hf = getComponent(tPeaks, rrTachogramAfterSqi, 0.15, 0.4)
return (vlf / (lf + hf))
def getAverageRatioVLFToHFLF(nTimes, percenteage):
cnt = 0
lst = []
while cnt < nTimes:
ratio = float(plotLombScarglePeriodogramWithFakeCorrelationCoefficientsVLFToHFLF(percenteageForFakeCoeffs))
lst.append(ratio)
cnt += 1
print(np.mean(lst))
print(np.std(lst))
def getAverageRatioHFLF(nTimes, percenteage):
cnt = 0
lst = []
while cnt < nTimes:
ratio = float(plotLombScarglePeriodogramWithFakeCorrelationCoefficients(percenteageForFakeCoeffs))
lst.append(ratio)
cnt += 1
print(np.mean(lst))
print(np.std(lst))
def plotRRTachogramAfterSQI():
rPeaks = ecgSignal[2]
(rPeaks,rrTachogram) = getRRTachogram(rPeaks)
medianTemplate = getMedianHeartbeatTemplate(ecgSignal[4])
corrCoeffs = getCorrelationCoefficients(ecgSignal[4],medianTemplate)
(tPeaks, rrTachogramAfterSqi) = getRRTachogramAfterSQI(rrTachogram, corrCoeffs, rPeaks)
plot(tPeaks, rrTachogramAfterSqi)
def plotRRTachogram():
rPeaks = ecgSignal[2]
(rPeaks,rrTachogram) = getRRTachogram(rPeaks)
plot(rPeaks[1:len(rPeaks)], rrTachogram)
def plotRRTachogramAfterSQIWithNones():
print("WTF")
rPeaks = ecgSignal[2]
(rPeaks,rrTachogram) = getRRTachogram(rPeaks)
medianTemplate = getMedianHeartbeatTemplate(ecgSignal[4])
corrCoeffs = getCorrelationCoefficients(ecgSignal[4],medianTemplate)
(tPeaks, rrTachogramAfterSqi) = getRRTachogramAfterSQIWithNones(rrTachogram, corrCoeffs, rPeaks)
plot(tPeaks, rrTachogramAfterSqi)
def getROCValue():
rPeaks = ecgSignal[2]
(rPeaks,rrTachogram) = getRRTachogram(rPeaks)
medianTemplate = getMedianHeartbeatTemplate(ecgSignal[4])
corrCoeffs = getCorrelationCoefficients(ecgSignal[4],medianTemplate)
(tPeaks, rrTachogramAfterSqi) = getRRTachogramAfterSQIWithNones(rrTachogram, corrCoeffs, rPeaks)
f = open("falsePositives", "r")
annotations = []
for line in f:
items = line.split()
annotations.append(items[1])
TP = 0
FP = 0
cnt = 0
for peak in tPeaks:
sqiResult = rrTachogramAfterSqi[cnt]
annotation = annotations[cnt]
if sqiResult != None:
if annotation == 'P':
TP += 1
else:
FP += 1
cnt += 1
print("TP: " + str(TP) + ", FP: " + str(FP))
getROCValue()
def plotPlainEcgSignal():
x = []
offset = dataX[0]
for point in dataX:
x.append(long((float(point) - offset) * 1000))
plot(x, dataEcg)
#plotPlainEcgSignal()
#plotRRTachogramAfterSQIWithNones()
#plotRRTachogram()
#getAverageRatioVLFToHFLF(1000, percenteageForFakeCoeffs)
#getAverageRatioHFLF(1000, percenteageForFakeCoeffs)
#createFakeCorrelationCoefficients(20, 11)
#plotLombScarglePeriodogram()
#plotLombScarglePeriodogramRaw()
#plotEcgSignal()