def test_csvREAD():
"""" Tests ecg data extraction from .csv.
"""
from readCSV import readCsv
[time, volts] = readCsv('testcsvreaddata.csv')
[timea, voltsa] = readCsv('baddata.csv')
print(timea)
correctt = [0., 0.003, 0.006, 0.008, 0.011]
correctv = [-0.145, -0.145, -0.145, -0.145, -0.145]
correctva = [-.145, -.145, -.145, -.145, -.145, -.145]
correctta = [0, 0.003, 0.006, 0.008, .011, 0.014]
assert pytest.approx(time) == correctt
assert pytest.approx(volts) == correctv
assert pytest.approx(timea) == correctta
assert pytest.approx(voltsa) == correctva
def test_timeDuration():
"""" Tests extraction of time duration of ECG data.
"""
from readCSV import readCsv
from timeDuration import timeDuration
[time, volts] = readCsv('testcsvreaddata.csv')
t = [0., 0.003, 0.006, 0.008, 0.011]
v = [-0.145, -0.145, -0.145, -0.145, -0.145]
assert pytest.approx(timeDuration(time)) == .011
def test_peakDetect():
"""" Tests R wave peak detection from ECG data.
"""
from peakDetection import peakDetect
from readCSV import readCsv
from processECG import movingAverage
from processECG import subtractDC
[t, v] = readCsv(r'test_data1.csv')
svf = subtractDC(v)
invfssum = 0.0
for i in range(len(t) - 1):
invfssum = invfssum + (t[i + 1] - t[i])
invfs = invfssum / (len(t) - 1)
movingavgwindow = int(.085 / invfs)
avgvolt = movingAverage(t, svf, movingavgwindow)
peaks = peakDetect(t, avgvolt)
correctbeatnum = 36
assert len(peaks) == correctbeatnum
def main():
""" Runs all attached functions
This function reads and analyzes the ECG data and
writes the calculated parameters to a JSON file.
"""
from readCSV import readCsv
from readCSV import recondtiontv
from processECG import subtractDC
from processECG import movingAverage
from peakDetection import peakDetect
from timeDuration import timeDuration
from extVolt import extremeVolt
from hrcalc import hrCalc
import matplotlib.pyplot as plt
from writetoJson import writetoJson
import json
csvfile = r'test_data1.csv'
[traw, vraw] = readCsv(csvfile)
bottomtimebnd = 0
toptimebnd = 100
[t, v] = recondtiontv(bottomtimebnd, toptimebnd, traw, vraw)
svf = subtractDC(v)
invfssum = 0.0
for i in range(len(t) - 1):
invfssum = invfssum + (t[i + 1] - t[i])
invfs = invfssum / (len(t) - 1)
movingavgwindow = int(.085 / invfs)
avgvolt = movingAverage(t, svf, movingavgwindow)
peakindices = peakDetect(t, avgvolt)
heartbeattimes = t[peakindices].tolist()
[min, max] = extremeVolt(v)
voltextremes = (min, max)
duration = timeDuration(t)
beatnum = len(peakindices)
meanhr = hrCalc(peakindices, t)
writetoJson(csvfile, meanhr, voltextremes, duration, beatnum,
heartbeattimes)
plt.plot(t, avgvolt)
plt.plot(t[peakindices], avgvolt[peakindices], 'o')
plt.show()
This function identifies R wave peaks using the continuous
wavelet transform
Args:
t (list): List of ECG time floats
v (list): List of processed ECG voltage floats.
Returns:
peakind (list): List of integer indices corresponding to peaks
in input lists.
"""
peakind = signal.find_peaks_cwt(v, np.arange(20, 40, .1))
return peakind
if __name__ == "__main__":
from readCSV import readCsv
from processECG import subtractDC
from processECG import savgolFilter
from processECG import movingAverage
import matplotlib.pyplot as plt
[t, v] = readCsv(r'test_data4.csv')
sv = subtractDC(v)
svf = savgolFilter(sv)
avgvolt = movingAverage(t, svf, 30)
peaks = peakDetect(t, avgvolt)
plt.plot(t, avgvolt)
plt.plot(t[peaks], avgvolt[peaks], 'o')
plt.show()
return subvolt
def movingAverage(t, volts, window):
""" Reduces noise by applying a moving average window.
Args:
t (list): List of ECG time floats
volts (list): List of ECG voltage floats
window (int): Size of moving average window
Returns:
lengthenedavg (list): List of moving average voltage floats
"""
import numpy as np
from numpy import convolve
bleh = np.repeat(1.0, window) / window
movingavg = np.convolve(volts, bleh, 'valid')
lengthenedavg = np.append(volts[:len(t) - len(movingavg)], movingavg)
logging.debug("Moving average was applied with a window of %f values",
window)
return lengthenedavg
if __name__ == "__main__":
from readCSV import readCsv
import numpy as np
[t, v] = readCsv(r'test_data10.csv')
sv = subtractDC(v)
avgvolt = movingAverage(t, sv, 40)