def pmus_passive(fs, rr):
"""
Passive profile, i.e, no respiratory effort (no spontaneous breathing)
:param fs: sample frequency
:param rr: respiratory rate
:return: pmus profile
"""
pmus = np.zeros(1, int(np.floor(60.0 / rr * fs) + 1))
return pmus
def pmus_trapezoidal(fs, rr, ttrap, inspeak, exppeak):
"""
Trapezoidal profile
:param fs: sample frequency
:param rr: respiratory rate
:param ttrap: time array defining trapezoidal profile
:param inspeak: negative peak pressure
:param exppeak: positive peak pressure
:return: pmus profile
"""
pmus = np.zeros(1, int(np.floor(60.0 / rr * fs) + 1))
return pmus
def pmus_ingmar(fs, rr, pp, tp, tf):
"""
Sinusoidal profile
:param fs: sample frequency
:param rr: respiratory rate
:param pp: peak pressure
:param tp: peak time
:param tf: end of effort
:return: pmus profile
"""
ntp = np.floor(tp * fs)
ntf = np.floor(tf * fs)
ntN = np.floor(60.0 * fs / rr)
pmus1 = np.sin(np.pi * np.arange(0, ntp + 1, 1) / fs / 2.0 / tp)
pmus2 = np.sin(np.pi / 2.0 / (tf - tp) *
(np.arange(ntp + 1, ntf + 1, 1) / fs + tf - 2.0 * tp))
pmus3 = 0 * np.arange(ntf + 1, ntN + 1, 1) / fs
pmus = pp * np.concatenate((pmus1, pmus2, pmus3))
return pmus
def pmus_parexp(fs, rr, pp, tp, tf):
"""
Parabolic-exponential profile
:param fs: sample frequency
:param rr: respiratory rate
:param pp: peak pressure
:param tp: peak time
:param tf: end of effort
:return: pmus profile
"""
ntp = np.floor(tp * fs)
ntN = np.floor(60.0 / rr * fs)
taur = abs(tf - tp) / 4.0
pmus1 = pp * (60.0 * rr - np.arange(0, ntp + 1, 1) / fs) * (
np.arange(0, ntp + 1, 1) / fs) / (tp * (60.0 * rr - tp))
pmus2 = pp * (np.exp(-(np.arange(ntp + 1, ntN + 1, 1) / fs - tp) / taur) -
np.exp(-(60.0 * rr - tp) / taur)) / (
1.0 - np.exp(-(60.0 * rr - tp) / taur))
pmus = np.concatenate((pmus1, pmus2))
return pmus
def plot_histograms(df):
""" plots the histograms of the columns of the input dataframe
Args:
df(pandas.DataFrame): input data
"""
cols = df.columns
ncols = len(cols)
if ncols % 15 == 0:
nfigs = int(ncols / 15)
else:
nfigs = int(np.floor(ncols / 15) + 1)
rem = int(ncols % 15)
t = 0
for n in range(nfigs):
if n < nfigs:
f, axes = plt.subplots(5, 3, figsize=(13, 10))
f.suptitle("Histograms. Red = diseased, green = healthy",
fontsize=16)
k = 0
for j in range(3):
for i in range(5):
c = cols[i + k + t - 1]
axes[i, j].hist(df[c][df["num"] == 0],
bins=100,
color="green",
alpha=.5)
axes[i, j].hist(df[c][df["num"] == 1],
bins=100,
color="red",
alpha=.5)
axes[i, j].set_xlabel(c)
k = k + 5
else:
f, axes = plt.subplots(rem, 1, figsize=(6, 10))
f.suptitle("Histograms. Red = diseased, green = healthy",
fontsize=16)
for i in range(rem):
c = cols[-rem:][i]
axes[i].hist(df[c], bins=100)
axes[i].set_xlabel(c)
t = t + 10
plt.subplots_adjust(hspace=.5, right=.95, left=.05)
# plt.tight_layout()
plt.show()
def pmus_linear(fs, rr, pp, tp, tf):
"""
Linear profile
:param fs: sample frequency
:param rr: respiratory rate
:param pp: peak pressure
:param tp: peak time
:param tf: end of effort
:return: pmus profile
"""
nsamples = np.floor(60.0 / rr * fs)
time = np.arange(0, nsamples + 1, 1) / fs
pmus = 0 * time
for i in range(len(time)):
if time[i] <= tp:
pmus[i] = time[i] / tp
elif time[i] <= tf:
pmus[i] = (tf - time[i]) / (tf - tp)
else:
pmus[i] = 0.0
pmus[i] = pp * pmus[i]
return pmus
def solve_model(header_params,params,header_features,features,debugmsg):
#Extracts each parameter
fs = params[header_params.index('Fs')]
rvent = params[header_params.index('Rvent')]
c = params[header_params.index('C')]
rins = params[header_params.index('Rins')]
rexp = rins # params[4]
peep = params[header_params.index('PEEP')]
sp = params[header_params.index('SP')]
trigger_type = features[header_features.index('Triggertype')]
trigger_arg = params[header_params.index('Triggerarg')]
rise_type = features[header_features.index('Risetype')]
rise_time = params[header_params.index('Risetime')]
cycle_off = params[header_params.index('Cycleoff')]
rr = params[header_params.index('RR')]
pmus_type = features[header_features.index('Pmustype')]
pp = params[header_params.index('Pp')]
tp = params[header_params.index('Tp')]
tf = params[header_params.index('Tf')]
noise = params[header_params.index('Noise')]
e2 = params[header_params.index('E2')]
model = features[header_features.index('Model')]
expected_len = int(np.floor(180.0 / np.min(RR) * np.max(Fs)) + 1)
#Assings pmus profile
pmus = pmus_profile(fs, rr, pmus_type, pp, tp, tf)
pmus = pmus + peep #adjusts PEEP
pmus = np.concatenate((np.array([0]), pmus)) #sets the first value to zero
#Unit conversion from cmH2O.s/L to cmH2O.s/mL
rins = rins / 1000.0
rexp = rexp / 1000.0
rvent = rvent / 1000.0
#Generates time, flow, volume, insex and paw waveforms
time = np.arange(0, np.floor(60.0 / rr * fs) + 1, 1) / fs
time = np.concatenate((np.array([0]), time))
flow = np.zeros(len(time))
volume = np.zeros(len(time))
insex = np.zeros(len(time))
paw = np.zeros(len(time)) + peep #adjusts PEEP
len_time = len(time)
#Peak flow detection
peak_flow = flow[0]
detect_peak_flow = False
#Support detection
detect_support = False
time_support = -1
#Expiration detection
detect_exp = False
time_exp = -1
if trigger_type == 'flow':
# units conversion from L/min to mL/s
trigger_arg = trigger_arg / 60.0 * 1000.0
for i in range(1, len(time)):
# period until the respiratory effort beginning
if (((trigger_type == 'flow' and flow[i] < trigger_arg) or
(trigger_type == 'pressure' and paw[i] > trigger_arg + peep) or
(trigger_type == 'delay' and time[i] < trigger_arg)) and
(not detect_support) and (not detect_exp)):
paw[i] = peep
y0 = volume[i - 1]
tspan = [time[i - 1], time[i]]
args = (paw[i], pmus[i], model, c, e2, rins)
sol = odeint(flow_model, y0, tspan, args=args)
volume[i] = sol[-1]
flow[i] = flow_model(volume[i], time[i], paw[i], pmus[i], model, c, e2, rins)
if debugmsg:
print('volume[i]= {:.2f}, flow[i]= {:.2f}, paw[i]= {:.2f}, waiting'.format(volume[i], flow[i], paw[i]))
if (((trigger_type == 'flow' and flow[i] >= trigger_arg) or
(trigger_type == 'pressure' and paw[i] <= trigger_arg + peep) or
(trigger_type == 'delay' and time[i] >= trigger_arg))):
detect_support = True
time_support = time[i+1]
continue
# detection of inspiratory effort
# ventilator starts to support the patient
elif (detect_support and (not detect_exp)):
if rise_type == 'step':
paw[i] = sp + peep
elif rise_type == 'exp':
rise_type = rise_type if np.random.random() > 0.01 else 'linear'
if paw[i] < sp + peep:
paw[i] = (1.0 - np.exp(-(time[i] - time_support) / rise_time )) * sp + peep
if paw[i] >= sp + peep:
paw[i] = sp + peep
elif rise_type == 'linear':
rise_type = rise_type if np.random.random() > 0.01 else 'exp'
if paw[i] < sp + peep:
paw[i] = (time[i] - time_support) / rise_time * sp + peep
if paw[i] >= sp + peep:
paw[i] = sp + peep
y0 = volume[i - 1]
tspan = [time[i - 1], time[i]]
args = (paw[i], pmus[i], model, c, e2, rins)
sol = odeint(flow_model, y0, tspan, args=args)
volume[i] = sol[-1]
flow[i] = flow_model(volume[i], time[i], paw[i], pmus[i], model, c, e2, rins)
if debugmsg:
print('volume[i]= {:.2f}, flow[i]= {:.2f}, paw[i]= {:.2f}, supporting'.format(volume[i], flow[i], paw[i]))
if flow[i] >= flow[i - 1]:
peak_flow = flow[i]
detect_peak_flow = False
elif flow[i] < flow[i - 1]:
detect_peak_flow = True
if (flow[i] <= cycle_off * peak_flow) and detect_peak_flow and i<len_time:
detect_exp = True
time_exp = i+1
try:
paw[i + 1] = paw[i]
except IndexError:
pass
elif detect_exp:
if rise_type == 'step':
paw[i] = peep
elif rise_type == 'exp':
if paw[i - 1] > peep:
paw[i] = sp * (np.exp(-(time[i] - time[time_exp-1]) / rise_time )) + peep
if paw[i - 1] <= peep:
paw[i] = peep
elif rise_type == 'linear':
rise_type = rise_type if np.random.random() > 0.01 else 'exp'
if paw[i - 1] > peep:
paw[i] = sp * (1 - (time[i] - time[time_exp-1]) / rise_time) + peep
if paw[i - 1] <= peep:
paw[i] = peep
y0 = volume[i - 1]
tspan = [time[i - 1], time[i]]
args = (paw[i], pmus[i], model, c, e2, rexp + rvent)
sol = odeint(flow_model, y0, tspan, args=args)
volume[i] = sol[-1]
flow[i] = flow_model(volume[i], time[i], paw[i], pmus[i], model, c, e2, rexp + rvent)
if debugmsg:
print('volume[i]= {:.2f}, flow[i]= {:.2f}, paw[i]= {:.2f}, exhaling'.format(volume[i], flow[i], paw[i]))
#Generates InsEx trace
if time_exp > -1:
insex = np.concatenate((np.ones(time_exp), np.zeros(len(time) - time_exp)))
#Drops the first element
flow = flow[1:] / 1000.0 * 60.0 # converts back to L/min
volume = volume[1:]
paw = paw[1:]
pmus = pmus[1:] - peep #reajust peep again
insex = insex[1:]
flow,volume,pmus,insex,paw = generate_cycle(expected_len,flow,volume,pmus,insex,paw,peep=peep)
# paw = generate_cycle(expected_len,paw,peep=peep)[0]
flow,volume,paw,pmus,insex = generate_noise(noise,flow,volume,paw,pmus,insex)
# plt.plot(flow)
# plt.plot(volume)
# plt.plot(paw)
# plt.plot(pmus)
# plt.show()
return flow, volume, paw, pmus, insex, rins,rexp, c
if not os.path.exists(filename):
sampling_generator(size, filename)
header_params, param = load_csv(filename)
num_test_cases = len(param)
header_features, feature = get_random_features(features, num_test_cases)
print(f'Number of respiratory cycles to be simulated: {num_test_cases}')
fs = max(Fs)
rr = min(RR)
print(f'Creating waveforms for fs={fs} / rr={rr}')
num_points = int(np.floor(180.0 / rr * fs) + 1)
print("Test cases:", num_test_cases)
print("Number points: ", num_points)
# Target waveforms
flow = np.zeros((num_points, num_test_cases))
volume = np.zeros((num_points, num_test_cases))
paw = np.zeros((num_points, num_test_cases))
pmus = np.zeros((num_points, num_test_cases))
ins = np.zeros((num_points, num_test_cases))
resistances_ins = np.zeros((1, num_test_cases))
resistances_exp = np.zeros((1, num_test_cases))
capacitances = np.zeros((1, num_test_cases))
t = time.time()
err_pmus = []
# R_hat = np.average([denormalize_data(output_pred_test[i, 0], minimum=min_resistances, maximum=max_resistances) for i in range(num_examples)])
# C_hat = np.average([denormalize_data(output_pred_test[i, 1], minimum= min_capacitances, maximum= max_capacitances) for i in range(num_examples)])
R_hat = denormalize_data(output_pred_test[0, 0],
minimum=min_resistances,
maximum=max_resistances)
C_hat = denormalize_data(output_pred_test[0, 1],
minimum=min_capacitances,
maximum=max_capacitances)
alpha = 0.2
rr = min(RR)
fs = max(Fs)
time = np.arange(0, np.floor(180.0 / rr * fs) + 1, 1) / fs
err_pmus_hat = []
err_nmsre = []
for i in range(num_examples - 1):
# R_hat = alpha*denormalize_data(output_pred_test[i, 0], minimum=min_resistances, maximum=max_resistances) + (1-alpha)*R_hat
# C_hat = alpha*denormalize_data(output_pred_test[i, 1], minimum= min_capacitances, maximum= max_capacitances) + (1-alpha)*C_hat
R_hat = denormalize_data(output_pred_test[i, 0],
minimum=min_resistances,
maximum=max_resistances)
C_hat = denormalize_data(output_pred_test[i, 1],
minimum=min_capacitances,
maximum=max_capacitances)
# R = denormalize_data(output_data[i, 0], min_resistances, max_resistances)
