def plot_spectrum(signal=None, sampling_rate=1000., path=None, show=True):
"""Plot the power spectrum of a signal (one-sided).
Parameters
----------
signal : array
Input signal.
sampling_rate : int, float, optional
Sampling frequency (Hz).
path : str, optional
If provided, the plot will be saved to the specified file.
show : bool, optional
If True, show the plot immediately.
"""
freqs, power = st.power_spectrum(signal,
sampling_rate,
pad=0,
pow2=False,
decibel=True)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(freqs, power, linewidth=MAJOR_LW)
ax.set_xlabel('Frequency (Hz)')
ax.set_ylabel('Power (dB)')
ax.grid()
# make layout tight
fig.tight_layout()
# save to file
if path is not None:
path = utils.normpath(path)
root, ext = os.path.splitext(path)
ext = ext.lower()
if ext not in ['png', 'jpg']:
path = root + '.png'
fig.savefig(path, dpi=200, bbox_inches='tight')
# show
if show:
plt.show()
else:
# close
plt.close(fig)
def plot_spectrum(signal=None, sampling_rate=1000., path=None, show=True):
"""Plot the power spectrum of a signal (one-sided).
Parameters
----------
signal : array
Input signal.
sampling_rate : int, float, optional
Sampling frequency (Hz).
path : str, optional
If provided, the plot will be saved to the specified file.
show : bool, optional
If True, show the plot immediately.
"""
freqs, power = st.power_spectrum(signal, sampling_rate,
pad=0,
pow2=False,
decibel=True)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(freqs, power, linewidth=MAJOR_LW)
ax.set_xlabel('Frequency (Hz)')
ax.set_ylabel('Power (dB)')
ax.grid()
# make layout tight
fig.tight_layout()
# save to file
if path is not None:
path = utils.normpath(path)
root, ext = os.path.splitext(path)
ext = ext.lower()
if ext not in ['png', 'jpg']:
path = root + '.png'
fig.savefig(path, dpi=200, bbox_inches='tight')
# show
if show:
plt.show()
else:
# close
plt.close(fig)
def calculateECGFeatures(dataFrame, smooth=True, normalize=True):
'''
Inputs: dataFrame pandas.dataFrame containing clip of raw ECG data, should have two columns of
Timestamps (ms) and Sample (V)
smooth optional Boolean, default value is True, flag for whether to smooth input raw
ECG data or not
normalize option Boolean, default value is True, flag for whether to normalize input raw
ECG data or not
Outputs: features pandas.dataFrame for input clip of data, each column corresponds to a different
feature, included features are as follows:
- mean - ultra low frequency power
- median - very low frequency power
- max - low frequency power
- variance - high frequency power
- standard deviation - LF/HF ratio
- absolute deviation - total power
- kurtois - R-R interval
- skew
'''
# ----------------------------------------------------------------------------------------------------
# Data Preprocessing ---------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------
time = dataFrame['Timestamp (ms)'].values
data = dataFrame['Sample (V)'].values
## Smooth raw ECG data
if smooth:
smooth_signal = tools.smoother(
data, kernel='median', size=5) # Convolutional 5x5 kernel window
data = smooth_signal['signal']
## Normalize raw ECG data
if normalize:
norm_signal = tools.normalize(data)
data = norm_signal['signal']
# ----------------------------------------------------------------------------------------------------
# Begin Features Extraction Code ---------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------
## Calculate basic statistic features
s_mean, s_med, s_max, s_var, s_std_dev, s_abs_dev, s_kurtois, s_skew = tools.signal_stats(
data)
## Obtain Power Spectra
power_freqs, power_spectrum = tools.power_spectrum(data,
sampling_rate=2.0,
decibel=False)
## Calculate Ultra Low-Frequency Power (ULF Band: <= 0.003 Hz)
# power_ulf = tools.band_power(freqs=power_freqs, power=power_spectrum, frequency=[0, 0.003])
# power_ulf = np.absolute(power_ulf['avg_power'])
# Calculate Very Low-Frequency Power (VLF Band: 0.0033 - 0.04 Hz)
power_vlf = tools.band_power(freqs=power_freqs,
power=power_spectrum,
frequency=[0.0033, 0.04])
power_vlf = np.absolute(power_vlf['avg_power'])
# Calculate Low-Frequency Power (LF Band: 0.04 - 0.15 Hz Hz)
power_lf = tools.band_power(freqs=power_freqs,
power=power_spectrum,
frequency=[0.04, 0.15])
power_lf = np.absolute(power_lf['avg_power'])
## Calculate High-Frequency Power (HF Band: 0.15 - 0.40 Hz)
power_hf = tools.band_power(freqs=power_freqs,
power=power_spectrum,
frequency=[0.15, 0.40])
power_hf = np.absolute(power_hf['avg_power'])
## Calculate LF/HF Ratio
lf_hf_ratio = power_lf / power_hf
## Calculate Total Power (VLF + LF + HF power)
power_total = power_vlf + power_lf + power_hf
# Calculate R peak indices
r_peaks = ecg.engzee_segmenter(data, sampling_rate=500.0)
if np.size(r_peaks) != 1:
rr_indices = time[r_peaks]
rr_intervals = np.mean(np.diff(rr_indices))
else:
rr_intervals = np.nan # NaN indicates not enough R peaks within window to calculate R-R interval
# ----------------------------------------------------------------------------------------------------
# Collect Features and convert into dataFrame --------------------------------------------------------
# ----------------------------------------------------------------------------------------------------
signal_features = {
'mean': s_mean,
'median': s_med,
'max': s_max,
'variance': s_var,
'std_dev': s_std_dev,
'abs_dev': s_abs_dev,
'kurtois': s_kurtois,
'skew': s_skew,
# 'ulf_power': power_ulf,
'vlf_power': power_vlf,
'lf_power': power_lf,
'hf_power': power_hf,
'lf_hf_ratio': lf_hf_ratio,
'rr_interval': rr_intervals,
}
features = pd.Series(signal_features)
a = [
s_mean,
s_med,
s_max,
s_var,
s_std_dev,
s_abs_dev,
s_kurtois,
s_skew,
# power_ulf,
power_vlf,
power_lf,
power_hf,
lf_hf_ratio,
rr_intervals
# rr_indices
]
return a
