import os
import pickle
import numpy as np
import matplotlib.pyplot as plt
import neurokit as nk
import seaborn as sns
import pandas as pd
def load_data(path, subject):
"""Given path and subject, load the data of the subject"""
os.chdir(path)
os.chdir(subject)
with open(subject + '.pkl', 'rb') as file:
data = pickle.load(file, encoding='latin1')
return data
class read_data_one_subject:
"""Read data from WESAD dataset"""
def __init__(self, path, subject):
self.keys = ['label', 'subject', 'signal']
self.signal_keys = ['wrist', 'chest']
self.chest_sensor_keys = ['ACC', 'ECG', 'EDA', 'EMG', 'Resp', 'Temp']
self.wrist_sensor_keys = ['ACC', 'BVP', 'EDA', 'TEMP']
os.chdir(path)
os.chdir(subject)
with open(subject + '.pkl', 'rb') as file:
data = pickle.load(file, encoding='latin1')
self.data = data
def get_labels(self):
return self.data[self.keys[0]]
def get_wrist_data(self):
""""""
#label = self.data[self.keys[0]]
#assert subject == self.data[self.keys[1]]
signal = self.data[self.keys[2]]
wrist_data = signal[self.signal_keys[0]]
#wrist_ACC = wrist_data[self.wrist_sensor_keys[0]]
#wrist_ECG = wrist_data[self.wrist_sensor_keys[1]]
return wrist_data
def get_chest_data(self):
""""""
signal = self.data[self.keys[2]]
chest_data = signal[self.signal_keys[1]]
return chest_data
def extract_mean_std_features(ecg_data, label=0, block = 700):
#print (len(ecg_data))
i = 0
mean_features = np.empty(int(len(ecg_data)/block), dtype=np.float64)
std_features = np.empty(int(len(ecg_data)/block), dtype=np.float64)
max_features = np.empty(int(len(ecg_data)/block), dtype=np.float64)
min_features = np.empty(int(len(ecg_data)/block), dtype=np.float64)
idx = 0
while i < len(ecg_data):
temp = ecg_data[i:i+block]
#print(len(temp))
if idx < int(len(ecg_data)/block):
mean_features[idx] = np.mean(temp)
std_features[idx] = np.std(temp)
min_features[idx] = np.amin(temp)
max_features[idx] = np.amax(temp)
i += 700
idx += 1
#print(len(mean_features), len(std_features))
#print(mean_features, std_features)
features = {'mean':mean_features, 'std':std_features, 'min':min_features, 'max':max_features}
one_set = np.column_stack((mean_features, std_features, min_features, max_features))
return one_set
def extract_one(chest_data_dict, idx, l_condition=0):
ecg_data = chest_data_dict["ECG"][idx].flatten()
ecg_features = extract_mean_std_features(ecg_data, label=l_condition)
#print(ecg_features.shape)
eda_data = chest_data_dict["EDA"][idx].flatten()
eda_features = extract_mean_std_features(eda_data, label=l_condition)
#print(eda_features.shape)
emg_data = chest_data_dict["EMG"][idx].flatten()
emg_features = extract_mean_std_features(emg_data, label=l_condition)
#print(emg_features.shape)
temp_data = chest_data_dict["Temp"][idx].flatten()
temp_features = extract_mean_std_features(temp_data, label=l_condition)
#print(temp_features.shape)
baseline_data = np.hstack((eda_features, temp_features, ecg_features, emg_features))
#print(len(baseline_data))
label_array = np.full(len(baseline_data), l_condition)
#print(label_array.shape)
#print(baseline_data.shape)
baseline_data = np.column_stack((baseline_data, label_array))
#print(baseline_data.shape)
return baseline_data
def recur_print(ecg):
while ecg is dict:
print(ecg.keys())
for k in ecg.keys():
recur_print(ecg[k])
def execute():
data_set_path = "/media/jac/New Volume/Datasets/WESAD"
file_path = "ecg.txt"
subject = 'S3'
obj_data = {}
labels = {}
all_data = {}
subs = [2, 3, 4, 5, 6]
for i in subs:
subject = 'S' + str(i)
print("Reading data", subject)
obj_data[subject] = read_data_one_subject(data_set_path, subject)
labels[subject] = obj_data[subject].get_labels()
wrist_data_dict = obj_data[subject].get_wrist_data()
wrist_dict_length = {key: len(value) for key, value in wrist_data_dict.items()}
chest_data_dict = obj_data[subject].get_chest_data()
chest_dict_length = {key: len(value) for key, value in chest_data_dict.items()}
print(chest_dict_length)
chest_data = np.concatenate((chest_data_dict['ACC'], chest_data_dict['ECG'], chest_data_dict['EDA'],
chest_data_dict['EMG'], chest_data_dict['Resp'], chest_data_dict['Temp']), axis=1)
# Get labels
# 'ACC' : 3, 'ECG' 1: , 'EDA' : 1, 'EMG': 1, 'RESP': 1, 'Temp': 1 ===> Total dimensions : 8
# No. of Labels ==> 8 ; 0 = not defined / transient, 1 = baseline, 2 = stress, 3 = amusement,
# 4 = meditation, 5/6/7 = should be ignored in this dataset
# Do for each subject
baseline = np.asarray([idx for idx, val in enumerate(labels[subject]) if val == 1])
# print("Baseline:", chest_data_dict['ECG'][baseline].shape)
# print(baseline.shape)
stress = np.asarray([idx for idx, val in enumerate(labels[subject]) if val == 2])
# print(stress.shape)
amusement = np.asarray([idx for idx, val in enumerate(labels[subject]) if val == 3])
# print(amusement.shape)
baseline_data = extract_one(chest_data_dict, baseline, l_condition=1)
stress_data = extract_one(chest_data_dict, stress, l_condition=2)
amusement_data = extract_one(chest_data_dict, amusement, l_condition=3)
full_data = np.vstack((baseline_data, stress_data, amusement_data))
print("One subject data", full_data.shape)
all_data[subject] = full_data
i = 0
for k, v in all_data.items():
if i == 0:
data = all_data[k]
i += 1
print(all_data[k].shape)
data = np.vstack((data, all_data[k]))
print(data.shape)
return data
if __name__ == '__main__':
execute()
"""
ecg, eda = chest_data_dict['ECG'], chest_data_dict['EDA']
x = [i for i in range(len(baseline))]
for one in baseline:
x = [i for i in range(99)]
plt.plot(x, ecg[one:100])
break
"""
#x = [i for i in range(10000)]
#plt.plot(x, chest_data_dict['ECG'][:10000])
#plt.show()
# BASELINE
# [ecg_features[k] for k in ecg_features.keys()])
#ecg = nk.ecg_process(ecg=ecg_data, rsp=chest_data_dict['Resp'][baseline].flatten(), sampling_rate=700)
#print(os.getcwd())
"""
#recur_print
print(type(ecg))
print(ecg.keys())
for k in ecg.keys():
print(k)
for i in ecg[k].keys():
print(i)
resp = nk.eda_process(eda=chest_data_dict['EDA'][baseline].flatten(), sampling_rate=700)
resp = nk.rsp_process(chest_data_dict['Resp'][baseline].flatten(), sampling_rate=700)
for k in resp.keys():
print(k)
for i in resp[k].keys():
print(i)
# For baseline, compute mean, std, for each 700 samples. (1 second values)
#file_path = os.getcwd()
with open(file_path, "w") as file:
#file.write(str(ecg['df']))
file.write(str(ecg['ECG']['HRV']['RR_Intervals']))
file.write("...")
file.write(str(ecg['RSP']))
#file.write("RESP................")
#file.write(str(resp['RSP']))
#file.write(str(resp['df']))
#print(type(ecg['ECG']['HRV']['RR_Intervals']))
#file.write(str(ecg['ECG']['Cardiac_Cycles']))
#print(type(ecg['ECG']['Cardiac_Cycles']))
#file.write(ecg['ECG']['Cardiac_Cycles'].to_csv())
# Plot the processed dataframe, normalizing all variables for viewing purpose
"""
"""
bio = nk.bio_process(ecg=chest_data_dict["ECG"][baseline].flatten(), rsp=chest_data_dict['Resp'][baseline].flatten()
, eda=chest_data_dict["EDA"][baseline].flatten(), sampling_rate=700)
#nk.z_score(bio["df"]).plot()
print(bio["ECG"].keys())
print(bio["EDA"].keys())
print(bio["RSP"].keys())
#ECG
print(bio["ECG"]["HRV"])
print(bio["ECG"]["R_Peaks"])
#EDA
print(bio["EDA"]["SCR_Peaks_Amplitudes"])
print(bio["EDA"]["SCR_Onsets"])
#RSP
print(bio["RSP"]["Cycles_Onsets"])
print(bio["RSP"]["Cycles_Length"])
"""
print("Read data file")
#Flow: Read data for all subjects -> Extract features (Preprocessing) -> Train the model
Datasets
Models
