來自rr數據的真實心電信號模擬器，用於matlab或python

Question

我有一系列rr數據（PQRST心電圖信號中r-r峯值之間的距離） ，我想用matlab或python生成逼真的ECG信號。我已經找到了matlab的一些材料（ecg matlab中的內置函數），但我無法弄清楚如何從rr數據生成它，並且我沒有發現任何python。有什麼建議？

Answer 1

這是否適合您的需求？如果沒有，請讓我知道。祝你好運。

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() 

Answer 2

史蒂夫Tjoa響應給了我一個非常好的基礎寫出下面的腳本。 它非常類似，除了我發現了一些代碼行，使它對於像我這樣的n00bs更容易理解。我還爲心臟增加了一個更長的「休息」時間段，以便進行更精確的複製。該腳本可以設置以下內容：心率bpm，捕獲時間長度，噪聲添加，adc分辨率和adc採樣率。我建議安裝anaconda來運行它。它將安裝必要的庫併爲您提供優秀的Spyder IDE來運行它。

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')