def generate_psd_plot_hamilton(data: dict, sampling_frequency: int = 128):
rr_intervals_list = data['hamilton']['rr_intervals']
# Processing pré-pipeline
# This remove outliers from signal
rr_intervals_without_outliers = remove_outliers(
rr_intervals=rr_intervals_list, low_rri=300, high_rri=2000)
# This replace outliers nan values with linear interpolation
interpolated_rr_intervals = interpolate_nan_values(
rr_intervals=rr_intervals_without_outliers,
interpolation_method="linear")
# This remove ectopic beats from signal
nn_intervals_list = remove_ectopic_beats(
rr_intervals=interpolated_rr_intervals, method="malik")
# This replace ectopic beats nan values with linear interpolation
interpolated_nn_intervals = interpolate_nan_values(
rr_intervals=nn_intervals_list)
plot_psd(interpolated_nn_intervals,
method="lomb",
sampling_frequency=sampling_frequency)
print("ecg time", time)
print("ecg new points", points)
#downsampling
resampled_signal = scipy.signal.resample(ecg, points)
print("ecg resampled len", len(resampled_signal))
#r peak detector
detectors = Detectors(256)
r_peaks = detectors.engzee_detector(resampled_signal[0:34304])
rr = np.diff(r_peaks)
'''
#r peak plot
print(r_peaks)
plt.figure()
plt.plot(ecg[0:2560])
plt.plot(r_peaks, ecg[r_peaks], 'ro')
plt.title('Detected R-peaks')
plt.savefig('new_downsampled_rpeaks.png', dpi=300) #plot for 5 seconds (2500 points)
'''
#HRV time domain parameters
time_domain_features = get_time_domain_features(r_peaks)
print(time_domain_features)
#HRV frequency domain parameters
plot_psd(rr, method="welch")
plt.show()
fv.append(hf['Triang'])
fv.append(hf['ULF'])
fv.append(hf['VHF'])
fv.append(hf['VLF'])
return fv
#Feature Extraction
feature_array = []
sampling_rate = 300
#for i in range(len(arp)):
# x_rpeaks=arp[i]
# hrv_t = nk.bio_ecg.ecg_hrv(rpeaks=x_rpeaks, sampling_rate=sampling_rate, hrv_features=['time'])
# hrv_f = nk.bio_ecg.ecg_hrv(rpeaks=x_rpeaks, sampling_rate=sampling_rate, hrv_features=['frequency'])
## hrv_nl = nk.bio_ecg.ecg_hrv(rpeaks=x_rpeaks, sampling_rate=sampling_rate, hrv_features=['nonlinear'])
# temp_fa=feat_array(hrv_t,hrv_f)
# feature_array.append(temp_fa)
from hrvanalysis import get_csi_cvi_features
rr_intervals_list = arp[0]
time_domain_features = get_csi_cvi_features(rr_intervals_list)
from hrvanalysis import plot_psd, plot_distrib
# nn_intervals_list contains integer values of NN Interval
plot_psd(rr_intervals_list, method="welch")
plot_distrib(rr_intervals_list, bin_length=8)
from hrvanalysis import plot_poincare
plot_poincare(rr_intervals_list)
plot_poincare(rr_intervals_list, plot_sd_features=True)
low, high = 0.15, 0.4
idx_delta = np.logical_and(freq >= low, freq <= high)
freq_res = 0.00001
delta_power_2 = simps(psd[idx_delta],x=freq[idx_delta], dx=freq_res)
ratio = (delta_power_1/delta_power_2)
print('LF/HF ratio: %.3f' % ratio)
# Calculate of indices between desired frequency bands
vlf_indexes = np.logical_and(freq >= vlf_band[0], freq < vlf_band[1])
lf_indexes = np.logical_and(freq >= lf_band[0], freq < lf_band[1])
hf_indexes = np.logical_and(freq >= hf_band[0], freq < hf_band[1])
frequency_band_index = [vlf_indexes, lf_indexes, hf_indexes]
label_list = ["VLF component", "LF component", "HF component"]
if method == "welch":
plt.title("FFT Spectrum : Welch's periodogram", fontsize=20)
for band_index, label in zip(frequency_band_index, label_list):
plt.fill_between(freq[band_index], 0, psd[band_index] / (1000 * len(psd[band_index])), label=label)
plt.legend(prop={"size": 15}, loc="best")
plt.xlim(0, hf_band[1])
plt.show()
else:
raise ValueError("Not a valid method. Choose between 'lomb' and 'welch'")
plot_psd(rms, method="welch",sampling_frequency = Sampling_rate)