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get_12ECG_features.py
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get_12ECG_features.py
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#!/usr/bin/env python
import numpy as np
from scipy.signal import butter, lfilter
from scipy import stats
def detect_peaks(ecg_measurements,signal_frequency,gain):
"""
Method responsible for extracting peaks from loaded ECG measurements data through measurements processing.
This implementation of a QRS Complex Detector is by no means a certified medical tool and should not be used in health monitoring.
It was created and used for experimental purposes in psychophysiology and psychology.
You can find more information in module documentation:
https://github.com/c-labpl/qrs_detector
If you use these modules in a research project, please consider citing it:
https://zenodo.org/record/583770
If you use these modules in any other project, please refer to MIT open-source license.
If you have any question on the implementation, please refer to:
Michal Sznajder (Jagiellonian University) - technical contact (msznajder@gmail.com)
Marta lukowska (Jagiellonian University)
Janko Slavic peak detection algorithm and implementation.
https://github.com/c-labpl/qrs_detector
https://github.com/jankoslavic/py-tools/tree/master/findpeaks
MIT License
Copyright (c) 2017 Michal Sznajder, Marta Lukowska
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
filter_lowcut = 0.001
filter_highcut = 15.0
filter_order = 1
integration_window = 30 # Change proportionally when adjusting frequency (in samples).
findpeaks_limit = 0.35
findpeaks_spacing = 100 # Change proportionally when adjusting frequency (in samples).
refractory_period = 240 # Change proportionally when adjusting frequency (in samples).
qrs_peak_filtering_factor = 0.125
noise_peak_filtering_factor = 0.125
qrs_noise_diff_weight = 0.25
# Detection results.
qrs_peaks_indices = np.array([], dtype=int)
noise_peaks_indices = np.array([], dtype=int)
# Measurements filtering - 0-15 Hz band pass filter.
filtered_ecg_measurements = bandpass_filter(ecg_measurements, lowcut=filter_lowcut, highcut=filter_highcut, signal_freq=signal_frequency, filter_order=filter_order)
filtered_ecg_measurements[:5] = filtered_ecg_measurements[5]
# Derivative - provides QRS slope information.
differentiated_ecg_measurements = np.ediff1d(filtered_ecg_measurements)
# Squaring - intensifies values received in derivative.
squared_ecg_measurements = differentiated_ecg_measurements ** 2
# Moving-window integration.
integrated_ecg_measurements = np.convolve(squared_ecg_measurements, np.ones(integration_window)/integration_window)
# Fiducial mark - peak detection on integrated measurements.
detected_peaks_indices = findpeaks(data=integrated_ecg_measurements,
limit=findpeaks_limit,
spacing=findpeaks_spacing)
detected_peaks_values = integrated_ecg_measurements[detected_peaks_indices]
return detected_peaks_values,detected_peaks_indices
def bandpass_filter(data, lowcut, highcut, signal_freq, filter_order):
"""
Method responsible for creating and applying Butterworth filter.
:param deque data: raw data
:param float lowcut: filter lowcut frequency value
:param float highcut: filter highcut frequency value
:param int signal_freq: signal frequency in samples per second (Hz)
:param int filter_order: filter order
:return array: filtered data
"""
nyquist_freq = 0.5 * signal_freq
low = lowcut / nyquist_freq
high = highcut / nyquist_freq
b, a = butter(filter_order, [low, high], btype="band")
y = lfilter(b, a, data)
return y
def findpeaks(data, spacing=1, limit=None):
"""
Janko Slavic peak detection algorithm and implementation.
https://github.com/jankoslavic/py-tools/tree/master/findpeaks
Finds peaks in `data` which are of `spacing` width and >=`limit`.
:param ndarray data: data
:param float spacing: minimum spacing to the next peak (should be 1 or more)
:param float limit: peaks should have value greater or equal
:return array: detected peaks indexes array
"""
len = data.size
x = np.zeros(len + 2 * spacing)
x[:spacing] = data[0] - 1.e-6
x[-spacing:] = data[-1] - 1.e-6
x[spacing:spacing + len] = data
peak_candidate = np.zeros(len)
peak_candidate[:] = True
for s in range(spacing):
start = spacing - s - 1
h_b = x[start: start + len] # before
start = spacing
h_c = x[start: start + len] # central
start = spacing + s + 1
h_a = x[start: start + len] # after
peak_candidate = np.logical_and(peak_candidate, np.logical_and(h_c > h_b, h_c > h_a))
ind = np.argwhere(peak_candidate)
ind = ind.reshape(ind.size)
if limit is not None:
ind = ind[data[ind] > limit]
return ind
def get_12ECG_features(data, header_data):
tmp_hea = header_data[0].split(' ')
ptID = tmp_hea[0]
num_leads = int(tmp_hea[1])
sample_Fs= int(tmp_hea[2])
gain_lead = np.zeros(num_leads)
for ii in range(num_leads):
tmp_hea = header_data[ii+1].split(' ')
gain_lead[ii] = int(tmp_hea[2].split('/')[0])
# for testing, we included the mean age of 57 if the age is a NaN
# This value will change as more data is being released
sex = 0
age = 0
for iline in header_data:
if iline.startswith('#Age'):
tmp_age = iline.split(': ')[1].strip()
age = int(tmp_age if tmp_age != 'NaN' else 57)
elif iline.startswith('#Sex'):
tmp_sex = iline.split(': ')[1]
if tmp_sex.strip()=='Female':
sex =1
else:
sex=0
# elif iline.startswith('#Dx'):
# label = iline.split(': ')[1].split(',')[0]
# We are only using data from lead1
peaks,idx = detect_peaks(data[0],sample_Fs,gain_lead[0])
# mean
mean_RR = np.mean(idx/sample_Fs*1000)
mean_Peaks = np.mean(peaks*gain_lead[0])
# median
median_RR = np.median(idx/sample_Fs*1000)
median_Peaks = np.median(peaks*gain_lead[0])
# standard deviation
std_RR = np.std(idx/sample_Fs*1000)
std_Peaks = np.std(peaks*gain_lead[0])
# variance
var_RR = stats.tvar(idx/sample_Fs*1000)
var_Peaks = stats.tvar(peaks*gain_lead[0])
# Skewness
skew_RR = stats.skew(idx/sample_Fs*1000)
skew_Peaks = stats.skew(peaks*gain_lead[0])
# Kurtosis
kurt_RR = stats.kurtosis(idx/sample_Fs*1000)
kurt_Peaks = stats.kurtosis(peaks*gain_lead[0])
features = np.hstack([age,sex,mean_RR,mean_Peaks,median_RR,median_Peaks,std_RR,std_Peaks,var_RR,var_Peaks,skew_RR,skew_Peaks,kurt_RR,kurt_Peaks])
return features