def calculate_corr_change(self, data, ref_mean, ref_std, data_type = 'none'):
corr_change = []
for i in range(int(self.s*SAMPLE_FREQUENCY), int(self.e*SAMPLE_FREQUENCY), SLIDING_PERIOD):
if (data_type == 'Time'):
data_correlation = transforms.TimeCorrelation_whole(self.end_f, 'usf').apply(data[:, i:i+WINDOW_RANGE])
elif (data_type == 'Frequency'):
data_correlation = transforms.FreqCorrelation_whole(self.start_f, self.end_f, 'usf').apply(data[:, i:i+WINDOW_RANGE])
if i == self.s*SAMPLE_FREQUENCY:
print 'data_corr:', data_correlation.shape
corr = []
for j in range(data_correlation.shape[0]):
sum = 0.0
for k in range(data_correlation.shape[1]):
sum += abs(data_correlation[j][k])
corr.append(sum)
w = transforms.Eigenvalues().apply(data_correlation)
corr_change.append(corr)
corr_change = np.array(corr_change)
corr_change = np.swapaxes(corr_change, 0, 1)
print data_type,' change:', corr_change.shape
for i in range (0, corr_change.shape[1]):
for j in range(0, CH_NUM):
corr_change[j][i] = (corr_change[j][i] - ref_mean[j]) / ref_std[j][0]
print data_type, 'corr change normalized:', corr_change.shape
return corr_change
def calculate_eigen_change(data, ref_mean, ref_std, data_type = 'none'):
eigen_change = []
for i in range(int(START_TIME*SAMPLE_FREQUENCY), int(END_TIME*SAMPLE_FREQUENCY), SAMPLE_FREQUENCY/4):
if (data_type == 'Time'):
data_correlation = transforms.TimeCorrelation_whole(50, 'usf').apply(data[:, i:i+WINDOW_RANGE])
elif (data_type == 'Frequency'):
data_correlation = transforms.FreqCorrelation_whole(1, 50, 'usf').apply(data[:, i:i+WINDOW_RANGE])
w = transforms.Eigenvalues().apply(data_correlation)
eigen_change.append(w)
eigen_change = np.array(eigen_change)
eigen_change = np.swapaxes(eigen_change, 0, 1)
print data_type,' change:', eigen_change
for i in range (0, eigen_change.shape[1]):
for j in range(0, CH_NUM):
if i < 2:
print i, j
print eigen_change[j][i],
print t_eigen_ref_mean[j],
print t_eigen_ref_std[j][0]
eigen_change[j][i] = (eigen_change[j][i] - ref_mean[j]) / ref_std[j][0]
print data_type, 'eigen change normalized:', eigen_change.shape
print eigen_change
return eigen_change
def calculate_eigenvalue_ref(self, data, data_type = 'None'):
"""
Using sliding window to calculate the change of eigenvalue
with time.
"""
print 'Calculating reference change of eigenvalue in ', data_type, ' domain .. (may take some time)'
#the change of eigenvalue with time in frequency/time domain
eigen_ref = []
for i in range(int(REF_TIME_START*SAMPLE_FREQUENCY), int(REF_TIME_END*SAMPLE_FREQUENCY)):
if data_type == 'Time':
data_correlation = transforms.TimeCorrelation_whole(self.end_f, 'usf').apply(data[:, i:i+WINDOW_RANGE])
elif data_type == 'Frequency':
data_correlation = transforms.FreqCorrelation_whole(self.start_f, self.end_f, 'usf').apply(data[:, i:i+WINDOW_RANGE])
w = transforms.Eigenvalues().apply(data_correlation)
eigen_ref.append(w)
eigen_ref = np.array(eigen_ref)
eigen_ref = np.swapaxes(eigen_ref, 0, 1)
print data_type, ' eigen ref:', eigen_ref.shape
return eigen_ref
def calculate_corr_ref(self, data, data_type = 'None'):
print 'Calculating reference change of corr in ', data_type, ' domain'
corr_ref = []
for i in range(int(REF_TIME_START*SAMPLE_FREQUENCY), int(REF_TIME_END*SAMPLE_FREQUENCY), SLIDING_PERIOD):
if data_type == 'Time':
data_correlation = transforms.TimeCorrelation_whole(self.end_f, 'usf').apply(data[:, i:i+WINDOW_RANGE])
elif data_type == 'Frequency':
data_correlation = transforms.FreqCorrelation_whole(self.start_f, self.end_f, 'usf').apply(data[:, i:i+WINDOW_RANGE])
corr = []
for j in range(data_correlation.shape[0]):
sum = 0.0
for k in range(data_correlation.shape[1]):
sum += abs(data_correlation[j][k])
corr.append(sum)
corr_ref.append(corr)
corr_ref = np.array(corr_ref)
corr_ref = np.swapaxes(corr_ref, 0, 1)
print data_type, ' corr ref:', corr_ref.shape
return corr_ref
def calculate_eigen_change(self, data, ref_mean, ref_std, data_type = 'none'):
print 'Calculating change of eigenvalue in ', data_type, ' domain .. (may take some time)'
eigen_change = []
for i in range(int(self.s*SAMPLE_FREQUENCY), int(self.e*SAMPLE_FREQUENCY), SAMPLE_FREQUENCY/4):
if (data_type == 'Time'):
data_correlation = transforms.TimeCorrelation_whole(self.end_f, 'usf').apply(data[:, i:i+WINDOW_RANGE])
elif (data_type == 'Frequency'):
data_correlation = transforms.FreqCorrelation_whole(self.start_f, self.end_f, 'usf').apply(data[:, i:i+WINDOW_RANGE])
w = transforms.Eigenvalues().apply(data_correlation)
eigen_change.append(w)
eigen_change = np.array(eigen_change)
eigen_change = np.swapaxes(eigen_change, 0, 1)
print data_type,' change:', eigen_change.shape
for i in range (0, eigen_change.shape[1]):
for j in range(0, CH_NUM):
eigen_change[j][i] = (eigen_change[j][i] - ref_mean[j]) / ref_std[j][0]
print data_type, 'eigen change normalized:', eigen_change.shape
self.do_eigen = True
return eigen_change
def process_raw_data(mat_data, with_latency):
start = time.get_seconds()
initial_start = time.get_seconds()
print 'Loading data',
if 'data_behavior' in mat_data:
dataKey = 'data_behavior'
elif 'data_3sFIR' in mat_data:
dataKey = 'data_3sFIR'
else:
dataKey = 'data'
print "mat:", mat_data[dataKey].shape
data = mat_data[dataKey][0:CH_NUM, :]
print data.shape, data
if mat_data[dataKey].shape[0] > CH_NUM:
latencies = mat_data[dataKey][CH_NUM, :]
else:
latencies = np.zeros(len(data[0]))
print latencies
"""
Plot out the original EEG signals.
"""
print 'Plotting out the original EEG signals... ',
channels_fig = plt.figure()
x1 = np.arange(START_TIME, END_TIME, 1.0 / SAMPLE_FREQUENCY)
for i in range(0, CH_NUM):
plt.subplot(CH_NUM, 1, i + 1)
plt.plot(
x1, data[i, START_TIME * SAMPLE_FREQUENCY:END_TIME *
SAMPLE_FREQUENCY])
print '(%ds)' % (time.get_seconds() - start)
start = time.get_seconds()
"""
Using sliding window to calculate the change of eigenvalue
with time.
"""
print 'Calculating change of eigenvalue in frequncy and time domain ... ',
#the change of eigenvalue with time in frequency/time domain
t_eigen_ref = []
for i in range(int(REF_TIME_START * SAMPLE_FREQUENCY),
int(REF_TIME_END * SAMPLE_FREQUENCY)):
data_tc = transforms.TimeCorrelation_whole(50, 'usf').apply(
data[:, i:i + WINDOW_RANGE])
w = transforms.Eigenvalues().apply(data_tc)
t_eigen_ref.append(w)
t_eigen_ref = np.array(t_eigen_ref)
t_eigen_ref = np.swapaxes(t_eigen_ref, 0, 1)
t_eigen_ref_std = transforms.Stats().apply(t_eigen_ref)
t_eigen_ref_mean = np.average(t_eigen_ref, axis=1)
print 't eigen ref', t_eigen_ref
print "t eigen ref std", t_eigen_ref_std
print 't eigen ref mean', t_eigen_ref_mean
f_eigen_change = []
t_eigen_change = []
for i in range(int(START_TIME * SAMPLE_FREQUENCY),
int(END_TIME * SAMPLE_FREQUENCY)):
data_tc = transforms.TimeCorrelation_whole(50, 'usf').apply(
data[:, i:i + WINDOW_RANGE])
w = transforms.Eigenvalues().apply(data_tc)
t_eigen_change.append(w)
data_fc = transforms.FreqCorrelation_whole(1, 50, 'usf').apply(
data[:, i:i + WINDOW_RANGE])
w = transforms.Eigenvalues().apply(data_fc)
f_eigen_change.append(w)
t_eigen_change = np.array(t_eigen_change)
t_eigen_change = np.swapaxes(t_eigen_change, 0, 1)
print 't eigen change', t_eigen_change
for i in range(0, t_eigen_change.shape[1]):
for j in range(0, CH_NUM):
t_eigen_change[j][i] = (
t_eigen_change[j][i] -
t_eigen_ref_mean[j]) / t_eigen_ref_std[j][0]
if i < 2:
print i, j
print t_eigen_change[j][i]
print t_eigen_ref_mean[j]
print t_eigen_ref_std[j][0]
print 't eigen change normalized', t_eigen_change
"""
for i in range(0, len(f_eigen_change)):
f_avg = 0
t_avg = 0
for j in range(0, SMOOTHING_PERIOD):
f_avg += f_eigen_change[]
"""
print '(%ds)' % (time.get_seconds() - start)
start = time.get_seconds()
"""
Calculate the standard deviation and normalized slope to define seizures.
"""
print 'Calculating the reference slope and change of slope ... ',
#reference slope
slope_stats = []
for i in range(int(REF_TIME_START * SAMPLE_FREQUENCY),
int(REF_TIME_END * SAMPLE_FREQUENCY)):
slopes = []
for j in range(0, CH_NUM):
slope = (data[j, i + 1] - data[j, i]) * SAMPLE_FREQUENCY
slopes.append(slope)
slope_stats.append(slopes)
slope_stats = np.array(slope_stats)
slope_stats = transforms.Stats().apply(slope_stats)
print "slope stats:", slope_stats.shape
#change of slope
#note: smoothed by SMOOTHING_PERIOD s average, calculated for each sec
slope_change = []
seizure_num_by_slope = []
for i in range(int(START_TIME * SAMPLE_FREQUENCY),
int(END_TIME * SAMPLE_FREQUENCY), SAMPLE_FREQUENCY):
seizure_channels_by_slope = 0
slopes = []
for j in range(0, CH_NUM):
average_slope = 0.0
for k in range(0, SMOOTHING_PERIOD * SAMPLE_FREQUENCY):
slope = (data[j, i + 1 + k] -
data[j, i + k]) * SAMPLE_FREQUENCY
average_slope += slope
average_slope /= SMOOTHING_PERIOD
slope_normalized = abs(average_slope / slope_stats[j][0])
#slope_normalized = abs(slope / slope_stats[j][0])
if (slope_normalized > SLOPE_THRESHOLD):
seizure_channels_by_slope += 1
slopes.append(slope_normalized)
slope_change.append(slopes)
seizure_num_by_slope.append(seizure_channels_by_slope)
slope_change = np.array(slope_change)
print 'slope change of each channel', slope_change.shape
seizure_num_by_slope = np.array(seizure_num_by_slope)
print 'seizure_num_by_slope', seizure_num_by_slope
print '(%ds)' % (time.get_seconds() - start)
start = time.get_seconds()
#Plot out the seizure period and correlation structure.
print 'Plotting out the other figures.. ',
#seizure onset by observation
fig = plt.figure()
plt.subplot(ROW_NUM, COL_NUM, 3)
plt.title('Seizure Time by Behavior')
x2 = np.arange(START_TIME, END_TIME, 1.0 / SAMPLE_FREQUENCY)
plt.plot(
x2, latencies[START_TIME * SAMPLE_FREQUENCY:END_TIME *
SAMPLE_FREQUENCY])
plt.axis([START_TIME, END_TIME, 0, 5])
plt.xlabel('time(s)')
plt.ylabel('seizure status')
#seizure onset by slope_normalized > 2.5
plt.subplot(ROW_NUM, COL_NUM, 4)
plt.title('Seizure Time by (Normalized Slope > 2.5) num ')
#x3 = np.arange(START_TIME, END_TIME, 1.0/SAMPLE_FREQUENCY)
x3 = np.arange(START_TIME, END_TIME, 1)
#plt.plot(x3, slope_change)
plt.plot(x3, seizure_num_by_slope)
plt.axis([START_TIME, END_TIME, 0, 8])
plt.ylabel('# of (sn > 2.5)')
#slope change of each channel
slope_change = np.array(slope_change)
slope_change = np.swapaxes(slope_change, 0, 1)
plt.subplot(ROW_NUM, COL_NUM, 1)
plt.title('Slope change of each channel(moving average by 5 sec)')
plt.imshow(slope_change,
origin='lower',
aspect='auto',
extent=[START_TIME, END_TIME, 1, CH_NUM],
interpolation='none')
plt.ylabel('channel')
#plt.colorbar()
"""
#time correlation
plt.subplot(ROW_NUM, COL_NUM, 1)
plt.title('Time Domain Correlation Analysis')
plt.imshow(t_eigen_change, origin = 'lower',
aspect = 'auto', extent = [START_TIME,END_TIME,0,7])#, interpolation = 'none')
plt.ylabel('eigenvalues')
plt.colorbar()
#phase correlation
f_eigen_change = np.array(f_eigen_change)
f_eigen_change = np.swapaxes(f_eigen_change, 0, 1)
print "f eigen change", f_eigen_change.shape
plt.subplot(ROW_NUM, COL_NUM, 2)
plt.title('Frequency Domain Correlation Analysis')
plt.imshow(f_eigen_change, origin = 'lower',
aspect = 'auto', extent = [START_TIME,END_TIME,0,7],
interpolation = 'none')
#plt.colorbar()
print '(%ds)' % (time.get_seconds() - start)
start = time.get_seconds()
latencies = np.array(latencies)
print latencies
"""
plt.tight_layout() #adjust the space between plots
plt.show()
