使用Scipy的反卷积函数反卷积皮肤电活动数据

Question

我希望使用Scipy的反卷积功能，使用here中所述的贝特曼函数作为滤波器对EDA（皮肤电活动）信号进行反卷积。

但是，当我尝试这样做时，反卷积图看起来并不像我期望的那样。也就是说，它通常采用大致为扁平线的形状，有时在滤波器长度的倍数处具有尖峰：

我在这里想念什么？我应该平滑EDA曲线吗？我是否希望deconvolve取得太多收益？我的代码如下：

import csv
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as signal
import math

with open('test session 1.csv', newline='') as csvfile: 
    filereader = csv.reader(csvfile, delimiter=' ')
    i = 0

    timestamps = []
    conductances = []

    for row in filereader: 
        i += 1

        fields = ' '.join(row).split()

        if i > 3:
            timestamps.append(float(fields[0]))
            conductances.append(float(fields[5]))

    timestamps = [timestamp - timestamps[0] for timestamp in timestamps]

    c = 10.
    tau1 = 300
    tau2 = 2000

    bateman = [c * ( math.exp(-time / tau2) - math.exp(-time / tau1)) for time in timestamps]
    bateman = bateman[3:1700]

    deconv, remain = signal.deconvolve(conductances, bateman)

    fig, ax = plt.subplots(nrows=4)

    ax[0].plot(conductances, label="EDA Signal")
    ax[1].plot(bateman, label="Bateman Function")
    ax[2].plot(deconv, label="Deconvolution Result")
    ax[3].plot(remain, label="Remainder")


    for i in range(len(ax)):
        ax[i].legend(loc=4)

    plt.show()