def calc_metrics(rrs):
buf_duration_scnds = 300
tacho_buf_dur = 300
min_rr = 0.3
max_rr = 1.7
sp_skip = 10
buf_size = int(max(buf_duration_scnds, tacho_buf_dur) / min_rr) + 1
buf = Buffer(buf_size)
print "[+] Calculating all usefull metrics: "
si = SI(buf, buf_duration_scnds, min_rr, max_rr)
sis = np.zeros(len(rrs)) # stress indices
shs = np.zeros(
(len(si.get_histogram()), len(rrs) / sp_skip + 1)) # stress histograms
si_ready = 0
sp = RRTachogrammEnergySpectrum(buf, tacho_buf_dur, min_rr, max_rr)
sp_ready = 0
sps = np.zeros((len(sp.get_spectrum()), len(rrs) / sp_skip + 1))
ics = np.zeros(len(rrs))
iscas = np.zeros(len(rrs))
vbs = np.zeros(len(rrs))
hrvs = np.zeros(len(rrs))
cnt = -1
ls = len(rrs)
md = ls / 10
for r in rrs:
if cnt % md == 0:
print "[+] Done {0:.2f}%".format(float(100 * cnt) / ls)
cnt += 1
si.update(r)
sp.update(r)
buf.add_sample(r)
# ## Calculating stress indices
si.calc_si()
if si.is_ready():
si_ready += 1
sis[cnt] = si.get_stress()
if cnt % sp_skip == 0:
shs[:, cnt / sp_skip] = si.get_histogram()
# ## Calculating RR-Tachogram spectrums
if sp.is_ready():
sp.calc_energy_spectrum()
if cnt % sp_skip == 0:
sps[:, cnt / sp_skip] = sp.get_spectrum()
ics[cnt] = sp.get_IC()
iscas[cnt] = sp.get_ISCA()
vbs[cnt] = sp.get_vegetative_balance()
sp_ready += 1
# ## Fulfil heart rate buffer
print "[+] Calculation finnished: SI ready " + "{0:.2f}%".format(
float(si_ready * 100) / cnt) + " SP ready: {0:.2f}%".format(
float(sp_ready * 100) / cnt)
return sis, shs, sps, ics, iscas, vbs
class RRTachogrammEnergySpectrum:
def __init__(self, buf, buf_duration_scnds=300, min_rr=0.3, max_rr=1.71):
self.time_span = TimeSpanBuffer(
buf, buf_duration_scnds) # remembers last 300 sec
buf_size = int(buf_duration_scnds / min_rr) + 1
# energy spectrum
self.cum_times_buf = Buffer(buf_size)
self.time_step = 1.0 / 67 #min_rr
self.spectr_intervals_cnt = int(
buf_duration_scnds /
self.time_step) # assume 1 heart beat per second
self.freqs = np.fft.fftfreq(self.spectr_intervals_cnt, self.time_step)
idx = np.argsort(self.freqs)
self.idx = [i for i in idx if 0.0 <= self.freqs[i] < 0.5]
self.last_spectrum = np.zeros(len(self.idx), dtype=float)
self._wnd = np.hamming(self.spectr_intervals_cnt)
self.may_calc = 1.0 - max_rr / buf_duration_scnds # when 90% of RR times collected in buffer - may start calculations
def _get_section_params(self, ps):
return np.sum(ps), np.max(ps)
def get_total_power(self):
return self._get_section_params(self.last_spectrum)
def get_hf_power(self):
ids = [i for i in self.idx if 0.15 <= self.freqs[i] < 0.4]
return self._get_section_params(self.last_spectrum[ids])
def get_lf_power(self):
ids = [i for i in self.idx if 0.04 <= self.freqs[i] < 0.15]
return self._get_section_params(self.last_spectrum[ids])
def get_vlf_power(self):
ids = [i for i in self.idx if 0.015 <= self.freqs[i] < 0.04]
return self._get_section_params(self.last_spectrum[ids])
def get_ulf_power(self):
ids = [i for i in self.idx if 0.0 <= self.freqs[i] < 0.015]
return self._get_section_params(self.last_spectrum[ids])
def get_IC(self):
"""
Index of Centralization -IC (Index of centralization, IC = (VLF +LF / HF)
IC reflects a degree of prevalence of non-respiratory sinus arrhythmia over the respiratory one.
Actually - this is quantitative characteristic of ratio between central and independent contours
of heart rhythm regulation.
"""
vlf = self.get_vlf_power()[0]
lf = self.get_lf_power()[0]
hf = self.get_hf_power()[0]
if hf > 0 and self.is_vlf_ready():
return vlf + float(lf) / hf
return 0
def get_ISCA(self):
"""
index of activation of sub cortical nervous centers ISCA (Index of Subcortical
Centers Activity, ISCA = VLF / LF).
ISCA characterizes activity of cardiovascular subcortical nervous center in relation
to higher levels of management. The increased activity of sub cortical nervous centers
is played by growth of ISCA.
With help of this index processes the brain inhibiting effect can be supervised.
"""
vlf = self.get_vlf_power()[0]
lf = self.get_lf_power()[0]
if lf > 0 and self.is_vlf_ready():
return vlf / float(lf)
return 0
def get_vegetative_balance(self):
"""HF/LF is interpreted as a parameter of vegetative balance"""
hf = self.get_hf_power()[0]
lf = self.get_lf_power()[0]
if lf > 0 and self.is_lf_ready():
return hf / float(lf)
return 0
def get_freq(self):
return self.freqs[self.idx]
def get_spectrum(self):
return self.last_spectrum
def calc_energy_spectrum(self):
#http://stackoverflow.com/questions/15382076/plotting-power-spectrum-in-python
# get unidistant sampled times
use_N_last_RRs = self.time_span.get_N()
unitimes = np.linspace(self.cum_times_buf.samples[-use_N_last_RRs],
self.cum_times_buf.samples[-1],
self.spectr_intervals_cnt)
# interpolate rr_ms_sqr
uni_rr_ms_sqr = np.interp(
unitimes,
self.cum_times_buf.samples[-use_N_last_RRs:],
#1.0/np.array(self.time_span.get_samples())) # energy levels over heart frequency
np.array(self.time_span.get_samples()
)) # 1.0/energy levels over 1.0/heart_frequency
uni_rr_ms_sqr -= np.mean(uni_rr_ms_sqr)
# calculate spectrum
ps = np.abs(np.fft.fft(uni_rr_ms_sqr * self._wnd))**2
#plt.plot(freqs[self.idx], ps[self.idx])
#s = np.sum(ps[self.idx])
#if s < self.zero_eps:
# s = 1.0
self.last_spectrum = ps[self.idx] #/ s
#return self.last_spectrum
def is_hf_ready(self):
return self.time_span.buf_real_duration > 14.
def is_lf_ready(self):
return self.time_span.buf_real_duration > 50.
def is_vlf_ready(self):
return self.time_span.buf_real_duration > 132.0
def is_ulf_ready(self):
"""
Different buffer duration allows to perform different calculations
ULF Less than 0,015 More than 66 secs
"""
return self.time_span.buf_real_duration > 300. # standard time
def is_ready(self):
return self.is_vlf_ready()
#return self.get_progress() >= self.may_calc
def get_progress(self):
return self.time_span.get_progress()
def update(self, rr):
self.time_span.update_buf_duration(rr)
if len(self.cum_times_buf.samples) > 0:
self.cum_times_buf.add_sample(self.cum_times_buf.samples[-1] + rr)
else:
self.cum_times_buf.add_sample(rr)
