def main():
# Read the ISHNE file
global ecg
ecg = ishne.ISHNE(sys.argv[1])
ecg.read()
# number of samples: 0.06 - 0.1 * SAMPLING_RATE (QRS Time: 60-100ms)
num_samples = int(0.08 * ecg.sampling_rate)
# The math suggests 16 samples is the width of the QRS complex
# Measuring the QRS complex for 9004 gives 16 samples
# Measured correlated peak 7 samples after start of QRS
h_hat = numpy.zeros(num_samples).astype(numpy.float32)
# Mexican hats seem to hold a nonzero value between -4 and 4 w/ sigma=1
sigma = 1.0
maxval = 4 * sigma
minval = -maxval
hat = numpy.zeros(16).astype(numpy.float32)
mexican_hat(cuda.Out(hat),
numpy.float32(sigma),
numpy.float32(minval),
numpy.float32((maxval - minval) / num_samples),
grid=(1, 1),
block=(int(ecg.sampling_rate), 1, 1))
with Timer() as pre_time:
d_mlead, length = preprocess(ecg.leads[0], ecg.leads[1], ecg.leads[2],
hat, 1.0)
with Timer() as time:
y = get_heartbeat(d_mlead, length)
print pre_time.interval, time.interval, pre_time.interval + time.interval
x = numpy.linspace(0, 23, num=len(y))
plt.figure(2)
plt.plot(x, y, 'b')
plt.show()
def read_ISHNE(ecg_filename):
# Read the ISHNE file
ecg = ishne.ISHNE(ecg_filename)
ecg.read()
return ecg
import pycuda.autoinit
import pycuda.driver as cuda
import numpy
import matplotlib.pyplot as plt
import ishne
from pycuda.compiler import SourceModule
import sys
with open("wavelet_mod.cu") as wavelet_file:
mod = SourceModule(wavelet_file.read())
calculate_hats = mod.get_function("calculate_hats")
cross_correlate_with_hat = mod.get_function("cross_correlate_with_hat")
ecg = ishne.ISHNE(sys.argv[1])
ecg.read()
lead = ecg.leads[0].astype(numpy.float32)[:1000]
hats = numpy.zeros(170 * 21).astype(numpy.float32)
correlated = numpy.zeros(1000).astype(numpy.float32)
calculate_hats(cuda.Out(hats), grid=(170, 1), block=(21, 1, 1))
cross_correlate_with_hat(cuda.Out(correlated),
cuda.In(lead),
cuda.In(hats[:21]),
numpy.int32(10000),
grid=(1, 1),
block=(1000, 1, 1))
plt.figure(1)