def Entropy_analysis(parallel_inputs):
t0 = time.time()
# Unpack the parallel input
x = parallel_inputs['x'] # Input EEG segment
r = parallel_inputs['r'] # Tolerance value
emb_dim = parallel_inputs['emb_dim'] # Embedding dimension
### Perform entropy analysis
ApEn = measures.ApEn(x, emb_dim, r)
# Sample Entropy
SampEn = measures.SampEn(x, emb_dim, r)
# RangeEn-A (Modified Approximate Entropy)
RangeEn_A = measures.RangeEn_A(x, emb_dim, r)
# RangeEn-B (Modified Sample Entropy)
RangeEn_B = measures.RangeEn_B(x, emb_dim, r)
print('Entropy analysis of the EEG segment was finished! Elapsed time: ' +
str(time.time() - t0))
return ApEn, SampEn, RangeEn_A, RangeEn_B
4 * sim_data.Uniform_noise(int(N_single / 5)),
sim_data.Uniform_noise(int(N_single / 5)))
x3 = np.concatenate(x3)
#### Perform entropy analysis
ApEn_r = np.zeros((3, N_r))
SampEn_r = np.zeros((3, N_r))
RangeEn_A_r = np.zeros((3, N_r))
RangeEn_B_r = np.zeros((3, N_r))
for n_r in range(0, N_r):
t0 = time.time()
# Approximate Entropy
ApEn_r[0, n_r] = measures.ApEn(x1, m_single, r_span[0, n_r])
ApEn_r[1, n_r] = measures.ApEn(x2, m_single, r_span[0, n_r])
ApEn_r[2, n_r] = measures.ApEn(x3 / np.std(x3), m_single, r_span[0,
n_r])
# Sample Entropy
SampEn_r[0, n_r] = measures.SampEn(x1, m_single, r_span[0, n_r])
SampEn_r[1, n_r] = measures.SampEn(x2, m_single, r_span[0, n_r])
SampEn_r[2, n_r] = measures.SampEn(x3 / np.std(x3), m_single,
r_span[0, n_r])
# RangeEn-A (Modified Approximate Entropy)
RangeEn_A_r[0, n_r] = measures.RangeEn_A(x1, m_single, r_span[0, n_r])
RangeEn_A_r[1, n_r] = measures.RangeEn_A(x2, m_single, r_span[0, n_r])
RangeEn_A_r[2, n_r] = measures.RangeEn_A(x3, m_single, r_span[0, n_r])
x = sim_data.Pink_noise(N_single)
elif (sig_type == 'brown_noise'):
x = sim_data.fBm(int(N_single), 0.5)
elif (sig_type == 'fBm025'):
x = sim_data.fBm(int(N_single), 0.25)
elif (sig_type == 'MIX'):
x = sim_data.MIX(int(N_single), 0, 50)
elif (sig_type == 'logistic_map'):
x = np.array(sim_data.Logistic_map(N_single))
elif (sig_type == 'henon_map'):
x, y = sim_data.Henon_map(N_single)
elif (sig_type == 'roessler_osc'):
x, y, z = sim_data.Roessler_osc(N_single, t1=0, t2=50)
# Approximate Entropy
ApEn_r[n_surr, n_r] = measures.ApEn(x, m_single, r_span[0, n_r])
# Sample Entropy
SampEn_r[n_surr, n_r] = measures.SampEn(x, m_single, r_span[0,
n_r])
# RangeEn-A (Modified Approximate Entropy)
RangeEn_A_r[n_surr,
n_r] = measures.RangeEn_A(x, m_single, r_span[0, n_r])
# RangeEn-B (Modified Sample Entropy)
RangeEn_B_r[n_surr,
n_r] = measures.RangeEn_B(x, m_single, r_span[0, n_r])
##### Save the entropy values in an external .npz file
np.savez(output_filename,
for n_h in range(0, N_H):
H = H_span[0, n_h]
# Simulate the input signal (fBm)
x = sim_data.fLm(alpha, H, int(n_nextpow2), dim=1, nm=1)
x = x[0:N_single]
if (STD_correction == 'yes'):
x = x / np.std(x)
t0 = time.time()
for n_r in range(0, N_r):
# Approximate Entropy
ApEn_h[n_h, n_r] = measures.ApEn(x, m_single, r_span[0,
n_r])
# Sample Entropy
SampEn_h[n_h,
n_r] = measures.SampEn(x, m_single, r_span[0,
n_r])
# RangeEn-A (Modified Approximate Entropy)
RangeEn_A_h[n_h, n_r] = measures.RangeEn_A(
x, m_single, r_span[0, n_r])
# RangeEn-B (Modified Sample Entropy)
RangeEn_B_h[n_h, n_r] = measures.RangeEn_B(
x, m_single, r_span[0, n_r])
##### Save the entropy values in an external .npz file
t00 = time.time()
for n_N in range(0,N_N):
t0 = time.time()
for n_surr in range(0, N_surr):
# Simulate the input signal
if(sig_type=='white_noise'):
x = sim_data.white_noise(N_span[0, n_N])
elif (sig_type == 'pink_noise'):
x = sim_data.Pink_noise(N_span[0, n_N])
elif (sig_type == 'brown_noise'):
x = sim_data.fBm(int(N_span[0, n_N]), 0.5)
# Approximate Entropy
ApEn_N[n_surr, n_N] = measures.ApEn(x, m_single, r_single)
# Sample Entropy
SampEn_N[n_surr, n_N] = measures.SampEn(x, m_single, r_single)
# RangeEn-A (Modified Approximate Entropy)
RangeEn_A_N[n_surr, n_N] = measures.RangeEn_A(x, m_single, r_single)
# RangeEn-B (Modified Sample Entropy)
RangeEn_B_N[n_surr, n_N] = measures.RangeEn_B(x, m_single, r_single)
##### Save the entropy values in an external .npz file
np.savez(output_filename, SampEn_N=SampEn_N, ApEn_N=ApEn_N, RangeEn_B_N=RangeEn_B_N, RangeEn_A_N=RangeEn_A_N)
print('Length ' + str(N_span[0, n_N]) + ', elapsed time = ' + str(time.time()-t0))
