def test_ppg_simulate(duration, sampling_rate, heart_rate, freq_modulation):
ppg = nk.ppg_simulate(
duration=duration,
sampling_rate=sampling_rate,
heart_rate=heart_rate,
frequency_modulation=freq_modulation,
ibi_randomness=0,
drift=0,
motion_amplitude=0,
powerline_amplitude=0,
burst_amplitude=0,
burst_number=0,
random_state=42,
show=False,
)
assert ppg.size == duration * sampling_rate
signals, _ = nk.ppg_process(ppg, sampling_rate=sampling_rate)
assert np.allclose(signals["PPG_Rate"].mean(), heart_rate, atol=1)
# Ensure that the heart rate fluctuates in the requested range.
groundtruth_range = freq_modulation * heart_rate
observed_range = np.percentile(signals['PPG_Rate'], 90) - np.percentile(
signals['PPG_Rate'], 10)
assert np.allclose(groundtruth_range,
observed_range,
atol=groundtruth_range * .15)
def _physio_process(data_path):
"""Basic signal cleaning and peak extraction."""
json_path = str(data_path).replace("tsv.gz", "json")
meta = read_json(json_path)
sampling_rate = meta["SamplingFrequency"]
data = read_tsv(data_path,
compression="gzip",
names=meta["Columns"],
index_col=False)
info = {}
rsp_signals, rsp_info = nk.rsp_process(data["respiratory"], sampling_rate)
px_signals, px_info = nk.ppg_process(data["cardiac"], sampling_rate)
info.update(rsp_info)
info.update(px_info)
signal = pd.concat([rsp_signals, px_signals], axis=1)
signal["time"] = signal.index * 1 / sampling_rate
signal = signal.set_index("time")
info["SamplingFrequency"] = sampling_rate
return signal, info
def neurokit_index(request):
data = pd.read_csv(
"/Users/siyuqian/Study/django-docker/712AF22B_Mar11_14-07-59.csv")
# Generate 15 seconds of PPG signal (recorded at 250 samples / second)
# ppg = nk.ppg_simulate(duration=15, sampling_rate=250, heart_rate=70)
ppg = nk.ppg_process(data['PPG'], sampling_rate=50)
# Clear the noise
ppg_clean = nk.ppg_clean(ppg)
# Peaks
peaks = nk.ppg_findpeaks(ppg_clean, sampling_rate=100)
# Compute HRV indices
hrv_indices = nk.hrv(peaks, sampling_rate=100, show=True)
result = hrv_indices.to_json()
parsed = json.loads(result)
context = {'response': json.dumps(parsed)}
return render(request, 'neurokit/neurokit_index.html', context)
def process_bvp(bvp, show_fig=False):
"""
Compute BVP signal features (more info: https://neurokit2.readthedocs.io/en/latest/functions.html#module-neurokit2.ppg).
Compute HRV indices (more info: https://neurokit2.readthedocs.io/en/latest/functions.html#module-neurokit2.hrv)
Parameters
----------
bvp : dict [timestamp : value]
EDA signal.
Returns
-------
bvp_signals : DataFrame
bvp_info : dict
"""
bvp_signals, bvp_info = nk.ppg_process(bvp['value'], sampling_rate=64)
# First 5 seconds of the signal.
# Find peaks
peaks = bvp_signals['PPG_Peaks'][:320]
# Compute HRV indices
time_hrv = nk.hrv_time(peaks, sampling_rate=64, show=show_fig)
hrv_base = time_hrv
hrv_base['type'] = 'base'
# The rest part of the signal.
# Find peaks
peaks = bvp_signals['PPG_Peaks'][320:]
# Compute HRV indices
phase_hrv = nk.hrv_frequency(peaks, sampling_rate=64, show=show_fig)
time_hrv = nk.hrv_time(peaks, sampling_rate=64, show=show_fig)
nonlinear_hrv = nk.hrv_nonlinear(peaks, sampling_rate=64, show=show_fig)
hrv_indices = pd.concat([phase_hrv, time_hrv, nonlinear_hrv], axis=1)
hrv_indices['type'] = 'stimul'
hrv_indices = pd.concat([hrv_indices, hrv_base])
return bvp_signals, bvp_info, hrv_indices
def test_ppg_simulate_ibi(ibi_randomness, std_heart_rate):
ppg = nk.ppg_simulate(
duration=20,
sampling_rate=50,
heart_rate=70,
frequency_modulation=0,
ibi_randomness=ibi_randomness,
drift=0,
motion_amplitude=0,
powerline_amplitude=0,
burst_amplitude=0,
burst_number=0,
random_state=42,
show=False,
)
assert ppg.size == 20 * 50
signals, _ = nk.ppg_process(ppg, sampling_rate=50)
assert np.allclose(signals["PPG_Rate"].mean(), 70, atol=1.5)
# Ensure that standard deviation of heart rate
assert np.allclose(signals["PPG_Rate"].std(), std_heart_rate, atol=1)
plot = nk.emg_plot(signals, sampling_rate=250)
plot.set_size_inches(10, 6, forward=True)
plot.savefig("README_emg.png", dpi=300, h_pad=3)
# =============================================================================
# Photoplethysmography (PPG/BVP)
# =============================================================================
# Generate 15 seconds of PPG signal (recorded at 250 samples / second)
ppg = nk.ppg_simulate(duration=15,
sampling_rate=250,
heart_rate=70,
random_state=333)
# Process it
signals, info = nk.ppg_process(ppg, sampling_rate=250)
# Visualize the processing
nk.ppg_plot(signals, sampling_rate=250)
# Save it
plot = nk.ppg_plot(signals, sampling_rate=250)
plot.set_size_inches(10, 6, forward=True)
plot.savefig("README_ppg.png", dpi=300, h_pad=3)
# =============================================================================
# Electrooculography (EOG)
# =============================================================================
# Import EOG data
eog_signal = nk.data("eog_100hz")
def extract_bvp_features(bvp_data, sampling_rate):
# Extract Heart Rate, RR Interval, and Heart Rate Variability features from PPG signals
# bvp_data = MinMaxScaler().fit_transform(np.array(bvp_data).reshape(-1, 1)).ravel()
ppg_signals, info = nk.ppg_process(bvp_data, sampling_rate=sampling_rate)
hr = ppg_signals['PPG_Rate']
# hr = MinMaxScaler().fit_transform(np.array(hr).reshape(-1, 1)).ravel()
peaks = info['PPG_Peaks']
# Sanitize input
peaks = _hrv_sanitize_input(peaks)
if isinstance(peaks, tuple): # Detect actual sampling rate
peaks, sampling_rate = peaks[0], peaks[1]
rri = _hrv_get_rri(peaks, sampling_rate=sampling_rate, interpolate=False)
diff_rri = np.diff(rri)
hrv_features = nk.hrv(
peaks, sampling_rate=sampling_rate
) # Ignore NeuroKitWarning: The duration of recording is too short to support a sufficiently long window for high frequency resolution as we used another frequency for hrv_frequency
hrv_frequency = nk.hrv_frequency(
peaks,
sampling_rate=sampling_rate,
ulf=(0.01, 0.04),
lf=(0.04, 0.15),
hf=(0.15, 0.4)
) # the parameters of ULF, LF, HF follows the original paper of WESAD dataset
# Philip Schmidt, Attila Reiss, Robert Duerichen, Claus Marberger, and Kristof Van Laerhoven. 2018. Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection.
# In Proceedings of the 20th ACM International Conference on Multimodal Interaction (ICMI '18). Association for Computing Machinery, New York, NY, USA, 400–408. DOI:https://doi.org/10.1145/3242969.3242985
# Not including: f_x_HRV of ULF and HLF, rel_f_x, sum f_x_HRV
mean_HR, std_HR = np.mean(hr), np.std(hr)
mean_HRV, std_HRV = hrv_features['HRV_MeanNN'], hrv_features['HRV_SDNN']
HRV_ULF, HRV_LF, HRV_HF, HRV_LFHF, HRV_LFnorm, HRV_HFnorm = hrv_frequency[
'HRV_ULF'], hrv_frequency['HRV_LF'], hrv_frequency[
'HRV_HF'], hrv_frequency['HRV_LFHF'], hrv_frequency[
'HRV_LFn'], hrv_frequency['HRV_HFn']
rms = np.sqrt(np.nanmean(rri**2))
nn50 = np.sum(np.abs(diff_rri) > 50)
HRV_TINN, HRV_pNN50, HRV_RMSSD = hrv_features['HRV_TINN'], hrv_features[
'HRV_pNN50'], hrv_features['HRV_RMSSD']
# Nkurikiyeyezu, K., Yokokubo, A., & Lopez, G. (2019). The Influence of Person-Specific Biometrics in Improving Generic Stress Predictive Models.
# ArXiv, abs/1910.01770.
kurtosis_HRV, skewness_HRV = kurtosis(rri), skew(rri)
HRV_VLF = hrv_frequency['HRV_VLF']
HRV_SD1, HRV_SD2 = hrv_features['HRV_SD1'], hrv_features['HRV_SD2']
HRV_SDSD = hrv_features['HRV_SDSD']
HRV_SDSD_RMSSD = HRV_SDSD / HRV_RMSSD
adj_sum_rri = diff_rri + 2 * rri[:-1]
HRV_pNN25 = np.sum(np.abs(diff_rri) > 25) / len(rri) * 100
relative_RRI = 2 * diff_rri / adj_sum_rri
mean_relativeRRI, median_relativeRRI, std_relativeRRI, RMSSD_relativeRRI, kurtosis_relativeRRI, skew_relativeRRI = np.mean(
relative_RRI), np.median(relative_RRI), np.std(relative_RRI), np.sqrt(
np.mean(np.diff(relative_RRI)**2)), kurtosis(relative_RRI), skew(
relative_RRI)
# Combining the extracted features
features = [
mean_HR, std_HR, mean_HRV, std_HRV, kurtosis_HRV, skewness_HRV, rms,
nn50, HRV_pNN50, HRV_pNN25, HRV_TINN, HRV_RMSSD, HRV_LF, HRV_HF,
HRV_LFHF, HRV_LFnorm, HRV_HFnorm, HRV_SD1, HRV_SD2, HRV_SDSD,
HRV_SDSD_RMSSD, mean_relativeRRI, median_relativeRRI, std_relativeRRI,
RMSSD_relativeRRI, kurtosis_relativeRRI, skew_relativeRRI
]
features = np.array(list(map(float, features)))
return features