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data_interface.py
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/
data_interface.py
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import torch
import numpy as np
from torch.utils.data import Dataset
import torch.utils.data as data
import random
def read_data_dirs(dirs_names):
import glob
import re
file_pairs = []
print('Reading files...',end='')
for dir in dirs_names:
all_files = sorted(glob.glob(dir + '/*.npy'))
files_Vm=[]
files_pECG=[]
for file in all_files:
if 'VmData' in file:
files_Vm.append(file)
if 'pECGData' in file:
files_pECG.append(file)
for i in range(len(files_pECG)):
VmName = files_Vm[i]
VmName = VmName.replace('VmData', '')
pECGName = files_pECG[i]
pECGName = pECGName.replace('pECGData', '')
if pECGName == VmName :
file_pairs.append([files_pECG[i], files_Vm[i]])
else:
print('Automatic sorted not matching, looking for pair ...',end='')
for j in range(len(files_Vm)):
VmName = files_Vm[j]
VmName = VmName.replace('VmData', '')
if pECGName == VmName :
file_pairs.append([files_pECG[i], files_Vm[j]])
print('done.')
print(' done.')
return file_pairs
class Ecg2TimeDataset(Dataset):
""" ECG2Vm dataset."""
def __init__(self, file_pairs, outLead, num_timesteps, scaling_ecg= 'none', scaling_vm= 'none', initial_time_aug = False, noise_ecg= 'none', noise_vm= 'none'):
# This is needed in the __getitem__ method
self.initial_time_aug = initial_time_aug
self.num_timesteps = num_timesteps
# First time we concatenate to global tensor
initialized = False
self.X = torch.zeros(len(file_pairs), 12, self.num_timesteps)
self.Y = torch.zeros(len(file_pairs), 75, 1)
for i, pair in enumerate(file_pairs):
print(f'Processing files {pair[0]} {pair[1]} ...', end='')
# Load the whole tensor
# shape = (num_timesteps, 10)
dataECG = torch.Tensor(np.load(pair[0]))
dataVm = np.load(pair[1])
if not noise_ecg == 'none':
torch.manual_seed(i)
noiseECG = torch.normal(torch.zeros(dataECG.shape), std = noise_ecg)
dataECG = dataECG + noiseECG
if not noise_vm == 'none':
torch.manual_seed(i)
noiseVm = torch.normal(torch.zeros(dataVm.shape), std = noise_vm).numpy()
dataVm = dataVm + noiseVm
ecg12aux = torch.zeros(dataECG.shape[0],12)
# The order in dataECG[:,j] = RA LA LL RL V1 V2 V3 V4 V5 V6
# 0 1 2 3 4 5 6 7 8 9
# Order in PTB Diagnostic ECG Database :
# (i, ii, iii, avr, avl, avf, v1, v2, v3, v4, v5, v6)
# Wilson lead = 1/3 *(RA + LA + LL))
# dataECG[:,1] = LA = v(L)
# dataECG[:,0] = RA = v(R)
# dataECG[:,2] = LL = v(F)
WilsonLead = 0.33333333 * (dataECG[:,0] + dataECG[:,1] + dataECG[:,2])
# Lead I: LA - RA
ecg12aux[:,0] = dataECG[:,1] - dataECG[:,0]
# Lead II: LL - RA
ecg12aux[:,1] = dataECG[:,2] - dataECG[:,0]
# Lead III: LL - LA
ecg12aux[:,2] = dataECG[:,2] - dataECG[:,1]
# Lead aVR: 3/2 (RA - Vw)
ecg12aux[:,3] = 1.5*(dataECG[:,0] - WilsonLead)
# Lead aVL: 3/2 (LA - Vw)
ecg12aux[:,4] = 1.5*(dataECG[:,1] - WilsonLead)
# Lead aVF: 3/2 (LL - Vw)
ecg12aux[:,5] = 1.5*(dataECG[:,2] - WilsonLead)
# Lead V1: V1 - Vw
ecg12aux[:,6] = dataECG[:,4] - WilsonLead
# Lead V2: V2 - Vw
ecg12aux[:,7] = dataECG[:,5] - WilsonLead
# Lead V3: V3 - Vw
ecg12aux[:,8] = dataECG[:,6] - WilsonLead
# Lead V4: V4 - Vw
ecg12aux[:,9] = dataECG[:,7] - WilsonLead
# Lead V5: V5 - Vw
ecg12aux[:,10] = dataECG[:,8] - WilsonLead
# Lead V6: V6 - Vw
ecg12aux[:,11] = dataECG[:,9] - WilsonLead
dataECG = ecg12aux
if scaling_ecg.lower() in ('normalized', 'normalization'):
min_ECG = torch.Tensor([-5.9542925, -5.5712219, -3.57188496, -4.288217553657807, -4.222570670021488, -3.156398588951382, -2.447856981540037, -2.3431330150222047, -2.514583015022205, -2.310517893645093, -1.9708348277623609, -2.32984159187901]);
max_ECG = torch.Tensor([4.5093912, 4.2175924, 4.23887649, 5.473125233494979, 3.555297089959163, 3.018831728255531, 2.984374763664689, 2.359350064582923, 2.205404624762396, 2.014215222417507, 1.7023503410995358, 2.128710478327503]);
dataECG = (dataECG - min_ECG)/(max_ECG - min_ECG)
if scaling_ecg.lower() in ('normalized_unit', 'normalization_unit'):
diff_ECG = torch.max(ecg12aux,0)[0] - torch.min(ecg12aux,0)[0]
dataECG = dataECG/diff_ECG
if scaling_ecg.lower() in ('normalized_zero', 'normalization_zero'):
min_ECG = torch.Tensor([-5.9542925, -5.5712219, -3.57188496, -4.288217553657807, -4.222570670021488, -3.156398588951382, -2.447856981540037, -2.3431330150222047, -2.514583015022205, -2.310517893645093, -1.9708348277623609, -2.32984159187901]);
max_ECG = torch.Tensor([4.5093912, 4.2175924, 4.23887649, 5.473125233494979, 3.555297089959163, 3.018831728255531, 2.984374763664689, 2.359350064582923, 2.205404624762396, 2.014215222417507, 1.7023503410995358, 2.128710478327503]);
dataECG = dataECG/(max_ECG - min_ECG)
if scaling_ecg.lower() in ('standardized', 'standardization'):
mean_ECG = torch.Tensor([0.05337660278745852, 0.07800385312690625, 0.024627250339440174, -0.06569022815751291, 0.014374676023680694, 0.05131555153284552, -0.010237228016673849, -0.003921020891815309, 0.0022406280086986834, 0.009890627175816347, 0.028677603573294373, 0.03275114376978516]);
std_ECG = torch.Tensor([0.5222836421647369, 0.46912923915554305, 0.37388222388756165, 0.4598741596222561, 0.3889260864136291, 0.33427537906748767, 0.3705529026825294, 0.2952769469657289, 0.25925690993927925, 0.22944537376676, 0.1901723804732019, 0.21860623885510164]);
dataECG = (dataECG - mean_ECG)/std_ECG
actTime = []
for col in range(0,75,1):
actTime.append(np.argmax(dataVm[:,col]>0))
actTime = np.asarray(actTime)
actTime = np.reshape(actTime,(75,1))
actTime = torch.Tensor(actTime)
if scaling_vm.lower() in ('normalized', 'normalization'):
#min_Vm = torch.Tensor([-91.547070965423, -90.845797294849, -92.346791462634, -92.121723283769, -91.874668667206, -91.569205803862, -92.573625470269, -92.097113730859, -92.376835295723, -92.153128683318, -92.273465804737, -92.515685511545, -91.543891527166, -91.396827592875, -91.828461777899, -92.471531295168, -91.786368851851, -91.465714821866, -92.022001689223, -91.967088751123, -92.143020796117, -91.376752270548, -91.18683934801, -91.597161602795, -91.404739481162, -91.189016018189, -91.367746620938, -91.523352844722, -91.28281668588, -91.254153963368, -90.876479993773, -91.03747607014, -90.572647978259, -91.344873762032, -90.665037255865, -90.753762415098, -90.333292034956, -90.548678702044, -91.758256112755, -91.462933175156, -91.125926520588, -90.098179775406, -89.982124612163, -91.870810868507, -91.812413078483, -91.228393395603, -90.903016617148, -90.223510567776, -90.6423528435, -91.202203410457, -91.343243777991, -90.175414069802, -91.581373995542, -91.179774480065, -91.14930469691, -91.276732728796, -91.542486604186, -91.371611542998, -91.439310192837, -91.458498024566, -91.039596307374, -91.375934955627, -93.086583844575, -91.943027066598, -92.312558736778, -92.298525819992, -92.163025092035, -91.925944305106, -91.670715595924, -92.092823395069, -91.636319866411, -91.351636589078, -91.513032593541, -92.759957565172, -92.909840231414]);
#max_Vm = torch.Tensor([46.248612640394, 49.487851490803, 49.446893077964, 49.436440277584, 49.407731608337, 49.419148769913, 49.351070038178, 49.530741145564, 49.468609033673, 49.418661992349, 49.412661638605, 49.439210059139, 49.4748682181, 49.356023962068, 49.414200321741, 49.408214439059, 49.477346732457, 49.40465524901, 49.339450491527, 49.427935235289, 49.403089385726, 49.457490510949, 49.152477436907, 49.126083653248, 49.494071838239, 49.18925460132, 49.435260208725, 49.44632171256, 49.139387403648, 49.406245558922, 49.426448104522, 49.104352111511, 48.987290952476, 48.976584491626, 49.124628836841, 49.036325003464, 49.297364351335, 49.23575427809, 49.478087212507, 49.366296897108, 49.252277385538, 49.125440811127, 49.200253621663, 49.224181420603, 49.241860273767, 49.180818724109, 49.329411225067, 49.283662392119, 49.336344104329, 49.204564827616, 48.985532589323, 49.451937575983, 49.196266981012, 49.146763295051, 49.221315098211, 49.173917830409, 49.184332499024, 49.170704682484, 49.087771790222, 49.084745546986, 49.152269095092, 49.365064723731, 49.249407543761, 49.373140706951, 49.462549062546, 49.127556631397, 49.182025848462, 49.232466484771, 49.040873382318, 49.370722057302, 49.46667861127, 49.435221512643, 48.016476126128, 49.21249261001, 48.079434835315]);
min_Vm = -85.50618677
max_Vm = 50;
dataVm = (dataVm - min_Vm)/(max_Vm - min_Vm)
if scaling_vm.lower() in ('standardized', 'standardization'):
mean_Vm = torch.Tensor([-27.02603307424577, -26.42599561013216, -27.08525685162167, -27.072770564186968, -27.243808102053098, -27.223278514349435, -27.083835486582533, -27.4110323325722, -27.331364526546558, -27.292912836782325, -27.2623019983753, -27.309269034155918, -27.338672021582383, -26.96347544613768, -27.158927183083865, -27.148198380852556, -26.883271193655993, -27.257562163163644, -27.415302995291814, -26.94089741455307, -26.380328171515497, -25.54398827003744, -23.23964030494721, -23.786303859496492, -24.34536973736025, -22.657021527711205, -22.97386209700062, -24.401331456790917, -22.85421172290724, -23.53197373751619, -22.835052832991884, -22.262005001437203, -22.455317361231046, -22.89109758332462, -22.188754372239817, -23.255352662227548, -24.477588837594464, -21.57554287398308, -23.21185108937846, -24.408397658499197, -23.575402103548807, -22.70741982489576, -23.26547254656032, -22.556876909612093, -23.473493713381124, -23.17731478310807, -23.3978924708568, -24.21465872516993, -24.124127267631177, -23.007716729212664, -21.946535996882478, -23.82175334518444, -22.123872753464493, -22.58709327049051, -22.922965821427884, -21.81210974698793, -23.108769012452854, -23.667390240852303, -23.176059221741134, -24.46574628391638, -24.280569203528557, -24.02632635847834, -24.47809251732797, -24.53259190412837, -21.119403077336877, -23.46734272271838, -22.150611059141283, -20.056483455611414, -23.7378480642948, -25.00461526170495, -25.54193419748148, -26.139503214725956, -24.044751574496654, -18.106829931867534, -24.050961364158532]);
std_Vm = torch.Tensor([47.641536695345756, 48.06899998911084, 48.07414930671482, 48.11237730036997, 48.129058287035924, 48.0640893651176, 48.04744258911431, 48.13517961142586, 48.11835490821148, 47.962570832683326, 48.045036073334025, 48.10542334800333, 48.1246977818833, 48.050984724069615, 48.150803515787764, 48.06273803810126, 48.14493033200949, 48.12373790487682, 47.94787316863915, 48.02717365919841, 48.16793355687098, 47.43404028501432, 47.69275264593347, 47.429461227850354, 47.27577038148137, 47.483485304378185, 47.35513468894153, 47.476477552939, 47.40493732227091, 47.36480405400306, 47.27410640503226, 47.326784740079724, 47.14305866446591, 47.14583848299832, 47.199156343425514, 47.34212272065462, 47.28098038286131, 47.327434856895884, 47.41420703719095, 47.40323474130151, 47.35621333118535, 47.23712532841057, 47.27712443301808, 47.32096036086348, 47.36162912212813, 47.254781765575274, 47.51120274336878, 47.573638059579956, 47.39408888626409, 47.22356400053576, 47.1674285402863, 47.07278873397787, 47.37766720677062, 47.180738258200996, 47.107764513714976, 47.39300851962479, 47.13585147999253, 47.44995913959363, 47.58919183197946, 47.60075423860692, 47.519772776916206, 47.668316011259535, 47.64926124185182, 47.36382151594125, 47.22019575795546, 47.17374251749065, 47.035487361419165, 47.01706929688778, 47.22759542323285, 47.63130076314641, 47.79796415558373, 47.84059288765446, 47.06397431137122, 46.780332763572964, 47.112581773104374]);
dataVm = (dataVm - mean_Vm)/std_Vm
# Construct the sequence correlations for this particular record
# If we want to fix the padding size for all the dataset
# if initial_time_aug :
# initial_time = random.randint(0, 40)
# else:
# initial_time = 0
# self.X[i,:,initial_time:num_timesteps] = dataECG[0:num_timesteps-initial_time,:].t()
self.X[i,:,:] = dataECG[:,:].t()
self.Y[i,:,:] = actTime
print('done.')
# The length of the dataset is
self.len = self.X.shape[0]
def __getitem__(self, index):
if self.initial_time_aug :
initial_time = random.randint(0, 40)
else:
initial_time = 0
paddingX = torch.zeros(12, self.num_timesteps)
paddingX[:,initial_time:self.num_timesteps] = self.X[index,:,0:self.num_timesteps - initial_time]
return paddingX, self.Y[index,:,:] + initial_time
# Original
# return self.X[index,:,:], self.Y[index,:,:]
def __len__(self):
return self.len
class Ecg2VmDataset(Dataset):
""" ECG2Vm dataset."""
def __init__(self, file_pairs, outLead, num_timesteps, scaling_ecg= 'none', scaling_vm= 'none', noise_ecg= 'none', noise_vm= 'none'):
# First time we concatenate to global tensor
initialized = False
self.X = torch.zeros(len(file_pairs), 12, num_timesteps)
self.Y = torch.zeros(len(file_pairs), len(outLead), num_timesteps)
for i, pair in enumerate(file_pairs):
print(f'Processing files {pair[0]} {pair[1]} ...', end='')
# Load the whole tensor
# shape = (num_timesteps, 10)
dataECG = torch.Tensor(np.load(pair[0]))
dataVm = torch.Tensor(np.load(pair[1]))
if not noise_ecg == 'none':
torch.manual_seed(i)
noiseECG = torch.normal(torch.zeros(dataECG.shape), std = noise_ecg)
dataECG = dataECG + noiseECG
if not noise_vm == 'none':
torch.manual_seed(i)
noiseVm = torch.normal(torch.zeros(dataVm.shape), std = noise_vm)
dataVm = dataVm + noiseVm
ecg12aux = torch.zeros(dataECG.shape[0],12)
# The order in dataECG[:,j] = RA LA LL RL V1 V2 V3 V4 V5 V6
# 0 1 2 3 4 5 6 7 8 9
# Order in PTB Diagnostic ECG Database :
# (i, ii, iii, avr, avl, avf, v1, v2, v3, v4, v5, v6)
# Wilson lead = 1/3 *(RA + LA + LL))
# dataECG[:,1] = LA = v(L)
# dataECG[:,0] = RA = v(R)
# dataECG[:,2] = LL = v(F)
WilsonLead = 0.33333333 * (dataECG[:,0] + dataECG[:,1] + dataECG[:,2])
# Lead I: LA - RA
ecg12aux[:,0] = dataECG[:,1] - dataECG[:,0]
# Lead II: LL - RA
ecg12aux[:,1] = dataECG[:,2] - dataECG[:,0]
# Lead III: LL - LA
ecg12aux[:,2] = dataECG[:,2] - dataECG[:,1]
# Lead aVR: 3/2 (RA - Vw)
ecg12aux[:,3] = 1.5*(dataECG[:,0] - WilsonLead)
# Lead aVL: 3/2 (LA - Vw)
ecg12aux[:,4] = 1.5*(dataECG[:,1] - WilsonLead)
# Lead aVF: 3/2 (LL - Vw)
ecg12aux[:,5] = 1.5*(dataECG[:,2] - WilsonLead)
# Lead V1: V1 - Vw
ecg12aux[:,6] = dataECG[:,4] - WilsonLead
# Lead V2: V2 - Vw
ecg12aux[:,7] = dataECG[:,5] - WilsonLead
# Lead V3: V3 - Vw
ecg12aux[:,8] = dataECG[:,6] - WilsonLead
# Lead V4: V4 - Vw
ecg12aux[:,9] = dataECG[:,7] - WilsonLead
# Lead V5: V5 - Vw
ecg12aux[:,10] = dataECG[:,8] - WilsonLead
# Lead V6: V6 - Vw
ecg12aux[:,11] = dataECG[:,9] - WilsonLead
dataECG = ecg12aux
if scaling_ecg.lower() in ('normalized', 'normalization'):
min_ECG = torch.Tensor([-5.9542925, -5.5712219, -3.57188496, -4.288217553657807, -4.222570670021488, -3.156398588951382, -2.447856981540037, -2.3431330150222047, -2.514583015022205, -2.310517893645093, -1.9708348277623609, -2.32984159187901]);
max_ECG = torch.Tensor([4.5093912, 4.2175924, 4.23887649, 5.473125233494979, 3.555297089959163, 3.018831728255531, 2.984374763664689, 2.359350064582923, 2.205404624762396, 2.014215222417507, 1.7023503410995358, 2.128710478327503]);
dataECG = (dataECG - min_ECG)/(max_ECG - min_ECG)
if scaling_ecg.lower() in ('normalized_zero', 'normalization_zero'):
min_ECG = torch.Tensor([-5.9542925, -5.5712219, -3.57188496, -4.288217553657807, -4.222570670021488, -3.156398588951382, -2.447856981540037, -2.3431330150222047, -2.514583015022205, -2.310517893645093, -1.9708348277623609, -2.32984159187901]);
max_ECG = torch.Tensor([4.5093912, 4.2175924, 4.23887649, 5.473125233494979, 3.555297089959163, 3.018831728255531, 2.984374763664689, 2.359350064582923, 2.205404624762396, 2.014215222417507, 1.7023503410995358, 2.128710478327503]);
dataECG = dataECG/(max_ECG - min_ECG)
if scaling_ecg.lower() in ('normalized_unit', 'normalization_unit'):
diff_ECG = torch.max(ecg12aux,0)[0] - torch.min(ecg12aux,0)[0]
dataECG = dataECG/diff_ECG
if scaling_ecg.lower() in ('standardized', 'standardization'):
mean_ECG = torch.Tensor([0.05337660278745852, 0.07800385312690625, 0.024627250339440174, -0.06569022815751291, 0.014374676023680694, 0.05131555153284552, -0.010237228016673849, -0.003921020891815309, 0.0022406280086986834, 0.009890627175816347, 0.028677603573294373, 0.03275114376978516]);
std_ECG = torch.Tensor([0.5222836421647369, 0.46912923915554305, 0.37388222388756165, 0.4598741596222561, 0.3889260864136291, 0.33427537906748767, 0.3705529026825294, 0.2952769469657289, 0.25925690993927925, 0.22944537376676, 0.1901723804732019, 0.21860623885510164]);
dataECG = (dataECG - mean_ECG)/std_ECG
if scaling_vm.lower() in ('normalized', 'normalization'):
#min_Vm = torch.Tensor([-91.547070965423, -90.845797294849, -92.346791462634, -92.121723283769, -91.874668667206, -91.569205803862, -92.573625470269, -92.097113730859, -92.376835295723, -92.153128683318, -92.273465804737, -92.515685511545, -91.543891527166, -91.396827592875, -91.828461777899, -92.471531295168, -91.786368851851, -91.465714821866, -92.022001689223, -91.967088751123, -92.143020796117, -91.376752270548, -91.18683934801, -91.597161602795, -91.404739481162, -91.189016018189, -91.367746620938, -91.523352844722, -91.28281668588, -91.254153963368, -90.876479993773, -91.03747607014, -90.572647978259, -91.344873762032, -90.665037255865, -90.753762415098, -90.333292034956, -90.548678702044, -91.758256112755, -91.462933175156, -91.125926520588, -90.098179775406, -89.982124612163, -91.870810868507, -91.812413078483, -91.228393395603, -90.903016617148, -90.223510567776, -90.6423528435, -91.202203410457, -91.343243777991, -90.175414069802, -91.581373995542, -91.179774480065, -91.14930469691, -91.276732728796, -91.542486604186, -91.371611542998, -91.439310192837, -91.458498024566, -91.039596307374, -91.375934955627, -93.086583844575, -91.943027066598, -92.312558736778, -92.298525819992, -92.163025092035, -91.925944305106, -91.670715595924, -92.092823395069, -91.636319866411, -91.351636589078, -91.513032593541, -92.759957565172, -92.909840231414]);
#max_Vm = torch.Tensor([46.248612640394, 49.487851490803, 49.446893077964, 49.436440277584, 49.407731608337, 49.419148769913, 49.351070038178, 49.530741145564, 49.468609033673, 49.418661992349, 49.412661638605, 49.439210059139, 49.4748682181, 49.356023962068, 49.414200321741, 49.408214439059, 49.477346732457, 49.40465524901, 49.339450491527, 49.427935235289, 49.403089385726, 49.457490510949, 49.152477436907, 49.126083653248, 49.494071838239, 49.18925460132, 49.435260208725, 49.44632171256, 49.139387403648, 49.406245558922, 49.426448104522, 49.104352111511, 48.987290952476, 48.976584491626, 49.124628836841, 49.036325003464, 49.297364351335, 49.23575427809, 49.478087212507, 49.366296897108, 49.252277385538, 49.125440811127, 49.200253621663, 49.224181420603, 49.241860273767, 49.180818724109, 49.329411225067, 49.283662392119, 49.336344104329, 49.204564827616, 48.985532589323, 49.451937575983, 49.196266981012, 49.146763295051, 49.221315098211, 49.173917830409, 49.184332499024, 49.170704682484, 49.087771790222, 49.084745546986, 49.152269095092, 49.365064723731, 49.249407543761, 49.373140706951, 49.462549062546, 49.127556631397, 49.182025848462, 49.232466484771, 49.040873382318, 49.370722057302, 49.46667861127, 49.435221512643, 48.016476126128, 49.21249261001, 48.079434835315]);
min_Vm = -85.50618677
max_Vm = 50;
dataVm = (dataVm - min_Vm)/(max_Vm - min_Vm)
if scaling_vm.lower() in ('standardized', 'standardization'):
mean_Vm = torch.Tensor([-27.02603307424577, -26.42599561013216, -27.08525685162167, -27.072770564186968, -27.243808102053098, -27.223278514349435, -27.083835486582533, -27.4110323325722, -27.331364526546558, -27.292912836782325, -27.2623019983753, -27.309269034155918, -27.338672021582383, -26.96347544613768, -27.158927183083865, -27.148198380852556, -26.883271193655993, -27.257562163163644, -27.415302995291814, -26.94089741455307, -26.380328171515497, -25.54398827003744, -23.23964030494721, -23.786303859496492, -24.34536973736025, -22.657021527711205, -22.97386209700062, -24.401331456790917, -22.85421172290724, -23.53197373751619, -22.835052832991884, -22.262005001437203, -22.455317361231046, -22.89109758332462, -22.188754372239817, -23.255352662227548, -24.477588837594464, -21.57554287398308, -23.21185108937846, -24.408397658499197, -23.575402103548807, -22.70741982489576, -23.26547254656032, -22.556876909612093, -23.473493713381124, -23.17731478310807, -23.3978924708568, -24.21465872516993, -24.124127267631177, -23.007716729212664, -21.946535996882478, -23.82175334518444, -22.123872753464493, -22.58709327049051, -22.922965821427884, -21.81210974698793, -23.108769012452854, -23.667390240852303, -23.176059221741134, -24.46574628391638, -24.280569203528557, -24.02632635847834, -24.47809251732797, -24.53259190412837, -21.119403077336877, -23.46734272271838, -22.150611059141283, -20.056483455611414, -23.7378480642948, -25.00461526170495, -25.54193419748148, -26.139503214725956, -24.044751574496654, -18.106829931867534, -24.050961364158532]);
std_Vm = torch.Tensor([47.641536695345756, 48.06899998911084, 48.07414930671482, 48.11237730036997, 48.129058287035924, 48.0640893651176, 48.04744258911431, 48.13517961142586, 48.11835490821148, 47.962570832683326, 48.045036073334025, 48.10542334800333, 48.1246977818833, 48.050984724069615, 48.150803515787764, 48.06273803810126, 48.14493033200949, 48.12373790487682, 47.94787316863915, 48.02717365919841, 48.16793355687098, 47.43404028501432, 47.69275264593347, 47.429461227850354, 47.27577038148137, 47.483485304378185, 47.35513468894153, 47.476477552939, 47.40493732227091, 47.36480405400306, 47.27410640503226, 47.326784740079724, 47.14305866446591, 47.14583848299832, 47.199156343425514, 47.34212272065462, 47.28098038286131, 47.327434856895884, 47.41420703719095, 47.40323474130151, 47.35621333118535, 47.23712532841057, 47.27712443301808, 47.32096036086348, 47.36162912212813, 47.254781765575274, 47.51120274336878, 47.573638059579956, 47.39408888626409, 47.22356400053576, 47.1674285402863, 47.07278873397787, 47.37766720677062, 47.180738258200996, 47.107764513714976, 47.39300851962479, 47.13585147999253, 47.44995913959363, 47.58919183197946, 47.60075423860692, 47.519772776916206, 47.668316011259535, 47.64926124185182, 47.36382151594125, 47.22019575795546, 47.17374251749065, 47.035487361419165, 47.01706929688778, 47.22759542323285, 47.63130076314641, 47.79796415558373, 47.84059288765446, 47.06397431137122, 46.780332763572964, 47.112581773104374]);
dataVm = (dataVm - mean_Vm)/std_Vm
# Construct the sequence correlations for this particular record
self.X[i,:,:] = dataECG[0:num_timesteps,:].t()
self.Y[i,:,:] = dataVm[0:num_timesteps,outLead].t()
print('done.')
# The length of the dataset is
self.len = self.X.shape[0]
def __getitem__(self, index):
return self.X[index,:,:], self.Y[index,:,:]
def __len__(self):
return self.len