实时心电滤波

Question

我正在处理raspi3B+和python2.7。在

我使用的是AD8232心率传感器和MCP3008模数转换器。在

我正在用matplotlib lib绘制传感器数据，但它太吵了。在

我必须过滤心电图数据，但我不知道如何申请。在

我可以实时过滤还是必须将数据保存到txt文件中，然后在将打印数据过滤到新的txt文件后应用过滤？在

我没有将数据保存到txt或csv文件中。如何过滤？在

import matplotlib.pyplot as plt
import numpy as np
import spidev, time
import RPi.GPIO as GPIO
# read adc function
def analog_read(channel):
    r = spi.xfer2([1,(8+channel)<<4,0])
    adc_out = ((r[1]&3) << 8) + r[2]
    return adc_out
spi = spidev.SpiDev()
spi.open(0,0)
fig = plt.figure()
ax = fig.add_subplot(111)
x = np.arange(500)
y=[0 for iii range(500)]
li, = ax.plot(x, y)
plt.ylim([0,3.3])
fig.canvas.draw()
plt.show(block=False)
# convert adc function
def St():
    for ii in range(1,501):
        reading = analog_read(0)
        voltage = reading * 3.3 / 4096
        time.sleep(0.005)
        vv=("%3.3f" % (voltage))
        x[ii-1]=ii-1
        y[ii-1]=vv
while True:
    St()
    li.set_ydata(y)
    fig.canvas.draw()

Answer 1

你的脚本不适合我，所以我只添加代码，你需要理解。在

您的脚本不执行实时操作，而是批量处理大小为500的数据。更改为在实时图形中执行更新。在

我用的是德克。我从deque创建了一个大小为500的缓冲区，所以如果你一直从左边追加它，它会自动弹出旧值，所以如果你继续绘制它，它看起来会是实时的。在

输入新值后，在每个周期绘制数据之前，您有时间执行过滤-例如使用粗粒化方法（您需要对其进行编程），或者您只需根据前面的几个值进行平均。在

修改后的代码

import matplotlib.pyplot as plt
import numpy as np
import spidev, time
import RPi.GPIO as GPIO
from collections import deque
# read adc function
def analog_read(channel):
    r = spi.xfer2([1,(8+channel)<<4,0])
    adc_out = ((r[1]&3) << 8) + r[2]
    return adc_out
spi = spidev.SpiDev()
spi.open(0,0)
fig = plt.figure()
ax = fig.add_subplot(111)
x = np.arange(500)
x = x.tolist()
y=[0 for iii in range(500)]
li, = ax.plot(x, y)
plt.ylim([0,3.3])
fig.canvas.draw()
plt.show(block=False)
# convert adc function


voltage_series = deque([],maxlen=500)
x_axis = x.tolist()
#fill it
for i in range(500):
    voltage_series.append(0)

# Y-axis = voltage_series
# X-axis = x_axis

counter = 0
while True: # grapg updates every read
    time.sleep(0.05)
    reading = analog_read(0)
    voltage = reading * 3.3 / 4096
    vv=("%3.3f" % (voltage))

    voltage_series.appendleft(voltage)
    if counter%500==0:
    ### Time to realtime filtering
        voltage_for_filtering = list(voltage_series)
        # transfor to numpy for example np.array(voltage_for_filtering)
        # or do standard method for smoothing noise - coarse graining
        # voltage_for_filtering = coarse_grain(voltage_for_filtering)  >> example

        li.set_ydata(voltage_for_filtering)
        fig.canvas.draw()