time_domain_features = eeg_features_exct.extractTimeDomainAll(
eeg_filtered)
freq_domain_features = eeg_features_exct.extractFrequencyDomainAll(
eeg_filtered)
plf_features = eeg_features_exct.extractPLFFeatures(
eeg_filtered)
power_features = eeg_features_exct.extractPowerFeatures(
eeg_filtered)
eeg_features = np.concatenate([
time_domain_features, freq_domain_features,
plf_features, power_features
])
# extract cvx of eda and time domain of ppg to check whether the inputs are not disorted
cvx_features = eda_features_exct.extractCVXEDA(eda)
scr_features = eda_features_exct.extractSCRFeatures(
eda)
ppg_time = ppg_features_exct.extractTimeDomain(ppg)
eda_features = np.concatenate([
cvx_features,
eda_features_exct.extractMFCCFeatures(
eda, min_len=min_eda_len), scr_features
])
ppg_features = np.concatenate([
ppg_time,
ppg_features_exct.extractFrequencyDomain(ppg),
ppg_features_exct.extractNonLinear(ppg)
])
# extract ecg resp features
ecg_resp_time_domain = ecg_resp_features_exct.extractTimeDomain(
ecg_resp)
from GSR.GSRFeatures import PPGFeatures, EDAFeatures
from Conf import Settings as set
import pandas as pd
data = pd.read_csv("..\\Data\\Dummy\\GSR_sample.csv", header=[0, 1])
#PPG
ppg = data["GSR_PPG_A13_CAL"].iloc[0:900].values
ppgExtract = PPGFeatures(set.FS_GSR)
print(ppgExtract.extractTimeDomain(ppg.flatten()))
print(ppgExtract.extractFrequencyDomain(ppg.flatten()))
print(ppgExtract.extractNonLinear(ppg.flatten()))
#EDA
eda = data["GSR_GSR_Skin_Conductance_CAL"].iloc[0:900].values
edaExtract = EDAFeatures(set.FS_GSR)
print(edaExtract.extractMFCCFeatures(ppg.flatten()))
print(edaExtract.extractSCRFeatures(ppg.flatten()))