import pylab
import scipy.signal as signal
import numpy
print('Simulating heart ecg')
# The "Daubechies" wavelet is a rough approximation to a real,
# single, heart beat ("pqrst") signal
pqrst = signal.wavelets.daub(10)
# Add the gap after the pqrst when the heart is resting.
samples_rest = 10
zero_array = numpy.zeros(samples_rest, dtype=float)
pqrst_full = numpy.concatenate([pqrst,zero_array])
# Plot the heart signal template
pylab.plot(pqrst_full)
pylab.xlabel('Sample number')
pylab.ylabel('Amplitude (normalised)')
pylab.title('Heart beat signal Template')
pylab.show()
# Simulated Beats per minute rate
# For a health, athletic, person, 60 is resting, 180 is intensive exercising
bpm = 60
bps = bpm / 60
# Simumated period of time in seconds that the ecg is captured in
capture_length = 10
# Caculate the number of beats in capture time period
# Round the number to simplify things
num_heart_beats = int(capture_length * bps)
# Concatonate together the number of heart beats needed
ecg_template = numpy.tile(pqrst_full , num_heart_beats)
# Plot the heart ECG template
pylab.plot(ecg_template)
pylab.xlabel('Sample number')
pylab.ylabel('Amplitude (normalised)')
pylab.title('Heart ECG Template')
pylab.show()
# Add random (gaussian distributed) noise
noise = numpy.random.normal(0, 0.01, len(ecg_template))
ecg_template_noisy = noise + ecg_template
# Plot the noisy heart ECG template
pylab.plot(ecg_template_noisy)
pylab.xlabel('Sample number')
pylab.ylabel('Amplitude (normalised)')
pylab.title('Heart ECG Template with Gaussian noise')
pylab.show()
# Simulate an ADC by sampling the noisy ecg template to produce the values
# Might be worth checking nyquist here
# e.g. sampling rate >= (2 * template sampling rate)
sampling_rate = 50.0
num_samples = sampling_rate * capture_length
ecg_sampled = signal.resample(ecg_template_noisy, num_samples)
# Scale the normalised amplitude of the sampled ecg to whatever the ADC
# bit resolution is
# note: check if this is correct: not sure if there should be negative bit values.
adc_bit_resolution = 1024
ecg = adc_bit_resolution * ecg_sampled
# Plot the sampled ecg signal
pylab.plot(ecg)
pylab.xlabel('Sample number')
pylab.ylabel('bit value')
pylab.title('%d bpm ECG signal with gaussian noise sampled at %d Hz' %(bpm, sampling_rate) )
pylab.show()
print('saving ecg values to file')
numpy.savetxt("ecg_values.csv", ecg, delimiter=",")
print('Done')
import scipy
import scipy.signal as sig
rr = [1.0, 1.0, 0.5, 1.5, 1.0, 1.0] # rr time in seconds
fs = 8000.0 # sampling rate
pqrst = sig.wavelets.daub(10) # just to simulate a signal, whatever
ecg = scipy.concatenate([sig.resample(pqrst, int(r*fs)) for r in rr])
t = scipy.arange(len(ecg))/fs
pylab.plot(t, ecg)
pylab.show()
史蒂夫特乔的回应给了我一个很好的基础来写下面的脚本。 它非常相似,只是我把一些代码行分解出来,让像我这样的n00b更容易理解。我还增加了一个较长的“休息”时间,让心脏稍微更准确地复制。该脚本允许您设置以下参数:心率bpm、捕获时间长度、添加的噪声、adc分辨率和adc采样率。我建议您安装anaconda来运行它。它将安装必要的库并为您提供优秀的Spyder IDE来运行它。
这适合你的需要吗?如果没有,请告诉我。祝你好运。
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