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rpeakdetect.py
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rpeakdetect.py
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"""
Copyright (c) 2013 Jami Pekkanen
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.
"""
import sys
import numpy as np
import scipy.signal
import scipy.ndimage
def detect_beats(
ecg, # The raw ECG signal
rate, # Sampling rate in HZ
# Window size in seconds to use for
ransac_window_size=5.0,
# Low frequency of the band pass filter
lowfreq=5.0,
# High frequency of the band pass filter
highfreq=15.0,
):
"""
ECG heart beat detection based on
http://link.springer.com/article/10.1007/s13239-011-0065-3/fulltext.html
with some tweaks (mainly robust estimation of the rectified signal
cutoff threshold).
"""
ransac_window_size = int(ransac_window_size*rate)
lowpass = scipy.signal.butter(1, highfreq/(rate/2.0), 'low')
highpass = scipy.signal.butter(1, lowfreq/(rate/2.0), 'high')
# TODO: Could use an actual bandpass filter
ecg_low = scipy.signal.filtfilt(*lowpass, x=ecg)
ecg_band = scipy.signal.filtfilt(*highpass, x=ecg_low)
# Square (=signal power) of the first difference of the signal
decg = np.diff(ecg_band)
decg_power = decg**2
# Robust threshold and normalizator estimation
thresholds = []
max_powers = []
for i in range(int(len(decg_power)/ransac_window_size)):
sample = slice(i*ransac_window_size, (i+1)*ransac_window_size)
d = decg_power[sample]
thresholds.append(0.5*np.std(d))
max_powers.append(np.max(d))
threshold = np.median(thresholds)
max_power = np.median(max_powers)
decg_power[decg_power < threshold] = 0
decg_power /= max_power
decg_power[decg_power > 1.0] = 1.0
square_decg_power = decg_power**2
shannon_energy = -square_decg_power*np.log(square_decg_power)
shannon_energy[~np.isfinite(shannon_energy)] = 0.0
mean_window_len = int(rate*0.125+1)
lp_energy = np.convolve(shannon_energy, [1.0/mean_window_len]*mean_window_len, mode='same')
#lp_energy = scipy.signal.filtfilt(*lowpass2, x=shannon_energy)
lp_energy = scipy.ndimage.gaussian_filter1d(lp_energy, rate/8.0)
lp_energy_diff = np.diff(lp_energy)
zero_crossings = (lp_energy_diff[:-1] > 0) & (lp_energy_diff[1:] < 0)
zero_crossings = np.flatnonzero(zero_crossings)
zero_crossings -= 1
return zero_crossings
def plot_peak_detection(ecg, rate):
import matplotlib.pyplot as plt
dt = 1.0/rate
t = np.linspace(0, len(ecg)*dt, len(ecg))
plt.plot(t, ecg)
peak_i = detect_beats(ecg, rate)
plt.scatter(t[peak_i], ecg[peak_i], color='red')
plt.show()
if __name__ == '__main__':
rate = float(sys.argv[1])
ecg = np.loadtxt(sys.stdin)
if len(sys.argv) > 2 and sys.argv[2] == 'plot':
plot_peak_detection(ecg, rate)
else:
peaks = detect_beats(ecg, rate)
sys.stdout.write("\n".join(map(str, peaks)))
sys.stdout.write("\n")