def _tag_qrs(waves):
"""
Creates a new string tag for a QRS complex from a sequence of waves. This
tag matches the name given by cardiologists to the different QRS waveforms.
"""
#This method consists in a concatenation of heuristic rules described with
#more or less precision in "European Heart Journal: Recommendations for
#measurement standards in quantitative electrocardiography. (1985)".
result = ''
waves = list(waves)
while waves:
wav = waves.pop(0)
#If the first wave is negative...
if not result and wav.sign == -1:
if not waves:
result = 'QS' if abs(wav.amp) > ph2dg(0.5) else 'Q'
else:
result = 'Q' if abs(wav.amp) > ph2dg(0.2) else 'q'
else:
newt = 'r' if wav.sign == 1 else 's'
if abs(wav.amp) > ph2dg(0.5):
newt = newt.upper()
result += newt
return result
def _paced_qrs_delineation(signal, points, peak, baseline):
"""
Checks if a sequence of waves is a paced heartbeat. The main criteria is
the presence of a spike at the beginning of the beat, followed by at least
one significant wave.
"""
try:
#Gets the slope between two points.
slope = lambda a, b : abs(dg2mm((signal[b]-signal[a])/sp2mm(b-a)))
#First we search for the spike.
spike = _find_spike(signal, points)
verify(spike)
if not spike[-1] in points:
points = np.insert(points, bisect.bisect(points, spike[-1]),
spike[-1])
#Now we get relevant points, checking some related constraints.
bpts = points[points <= spike[0]]
apts = points[points >= spike[-1]]
verify(len(apts) >= 2)
#Before and after the spike there must be a significant slope change.
verify(slope(spike[0], spike[1]) > 2.0 * slope(bpts[-2], bpts[-1]))
verify(slope(spike[1], spike[-1]) > 2.0 * slope(apts[0], apts[1]))
#Now we look for the end of the QRS complex, by applying the same
#clustering strategy than regular QRS, but only for the end.
slopes = (signal[apts][1:]-signal[apts][:-1])/(apts[1:]-apts[:-1])
features = []
for i in xrange(len(slopes)):
#The features are the slope in logarithmic scale and the distance to
#the peak.
features.append([math.log(abs(slopes[i])+1.0),
abs(apts[i+1] - peak)])
features = whiten(features)
#We initialize the centroids in the extremes (considering what is
#interesting of each feature for us)
fmin = np.min(features, 0)
fmax = np.max(features, 0)
valid = np.where(kmeans2(features, np.array([[fmin[0], fmax[1]],
[fmax[0], fmin[1]]]), minit = 'matrix')[1])[0]
verify(np.any(valid))
end = apts[valid[-1]+1]
#The duration of the QRS complex after the spike must be more than 2
#times the duration of the spike.
verify((end-apts[0]) > 2.0 * (spike[-1]-spike[0]))
#The amplitude of the qrs complex must higher than 0.5 the amplitude
#of the spike.
sgspike = signal[spike[0]:spike[-1]+1]
sgqrs = signal[apts[0]:end+1]
verify(np.ptp(sgqrs) > ph2dg(0.5))
verify(np.ptp(sgqrs) > 0.5 * np.ptp(sgspike))
#There must be at least one peak in the QRS fragment.
qrspt = signal[apts[apts <= end]]
verify(len(qrspt) >= 3)
verify(abs(signal[end] - signal[spike[0]]) <= ph2dg(0.3)
or len(get_peaks(qrspt)) > 0)
#The area of the rest of the QRS complex must be higher than the spike.
verify(np.sum(np.abs(sgspike-sgspike[0])) <
np.sum(np.abs(sgqrs-sgspike[0])))
#The distance between the beginning of the spike and the baseline
#cannot be more than the 30% of the amplitude of the complex.
verify(abs(signal[spike[0]]-baseline) <
0.3 * np.ptp(signal[spike[0]:end+1]))
#At last, we have found the paced QRS limits.
return Iv(spike[0], end)
except InconsistencyError:
return None
