def test_signal_smooth():
# TODO: test kernels other than "boxcar"
signal = np.cos(np.linspace(start=0, stop=20, num=1000))
smooth1 = nk.signal_smooth(signal, kernel="boxcar", size=100)
smooth2 = nk.signal_smooth(signal, kernel="boxcar", size=500)
# assert that the signal's amplitude is attenuated more with wider kernels
assert np.allclose(np.std(smooth1), 0.6044, atol=0.00001)
assert np.allclose(np.std(smooth2), 0.1771, atol=0.0001)
def signals_analysis(signals, is_out=False, is_show=False, out_path="figs"):
"""
数据的分析 主要是特征参数的获取
:param signals: 初步处理过的数据
:param is_out:
:param is_show:
:param out_path:
:return:
"""
sampling_rate = signals["Sampling Rate"]
feature_points = signals["Feature Points"]
cleaned_pulses = feature_points["Cleaned Pulses"]
peaks = feature_points["Peaks"]
title = "Time Analysis"
hrv_time = nk.hrv_time(peaks, sampling_rate, show=is_show)
if is_out:
no = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
out_name = os.path.join("outputs",
str(out_path) + "___" + no + title + ".png")
plt.savefig(out_name, dpi=300)
if is_show:
plt.show()
title = "Frequency Analysis"
hrv_freq = nk.hrv_frequency(peaks, sampling_rate, show=is_show)
if is_out:
no = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
out_name = os.path.join("outputs",
str(out_path) + "___" + no + title + ".png")
plt.savefig(out_name, dpi=300)
if is_show:
plt.show()
title = "Nonlinear Analysis"
hrv_nonlinear = nk.hrv_nonlinear(peaks, sampling_rate, show=is_show)
if is_out:
no = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
out_name = os.path.join("outputs",
str(out_path) + "___" + no + title + ".png")
plt.savefig(out_name, dpi=300)
if is_show:
plt.show()
# 傅里叶分析 对应给的文章
xFFT = np.abs(np.fft.rfft(cleaned_pulses) / len(cleaned_pulses))
xFFT = xFFT[:600]
xFreqs = np.linspace(0, sampling_rate // 2, len(cleaned_pulses) // 2 + 1)
xFreqs = xFreqs[:600]
# 滤波处理 平滑去噪 只处理前200个信号即可
# TODO 去噪方法可以调节
cleaned_xFFT = nk.signal_smooth(xFFT, method="loess")
# 计算特征值
# F1值
cleaned_FFT = cleaned_xFFT.copy()
locmax, props = spsg.find_peaks(cleaned_xFFT)
hr_hz_index = np.argmax(cleaned_xFFT)
f1 = np.argmax(xFFT)
fmax = np.argmax(cleaned_xFFT[locmax])
cleaned_xFFT[locmax[fmax]] = np.min(cleaned_xFFT)
f2s = np.argmax(cleaned_xFFT[locmax])
if f2s - fmax != 1:
hr_hz_index = locmax[0] + int(np.sqrt(locmax[1] - locmax[0]))
# F2值
f2 = locmax[np.argmax(cleaned_xFFT[locmax])]
F1 = np.round(xFreqs[f1], 2)
F2 = np.round(xFreqs[f2], 2)
# 相位差
F2_F1 = F2 - F1
# 心率
HR_FFT = xFreqs[hr_hz_index] * 60
print(HR_FFT)
if is_show:
plt.plot(xFreqs, cleaned_FFT)
plt.scatter(xFreqs[f1],
cleaned_FFT[f1],
color="red",
label="F1 = " + str(F1) + "HZ")
plt.scatter(xFreqs[f2],
cleaned_FFT[f2],
color="orange",
label="F2 = " + str(F2) + "HZ")
plt.legend(loc="upper right")
plt.ylabel("Power")
plt.xlabel("Freq(Hz)")
title = "FFT analysis"
plt.title(title)
if is_out:
no = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
out_name = os.path.join(
"outputs",
str(out_path) + "___" + no + title + ".png")
plt.savefig(out_name, dpi=300)
plt.show()
hrv_new = {
"Power": xFFT,
"Freq": cleaned_FFT,
"X": xFreqs,
"F1": F1,
"F2": F2,
"F2_F1": F2_F1,
"HR_FFT": HR_FFT
}
return {
"HRV New": hrv_new,
"HRV Time": hrv_time,
"HRV Frequency": hrv_freq,
"HRV Nonlinear": hrv_nonlinear
}