# filtered_eda = eda_features.filterEDA(eda)
# filtered_ppg = ppg_features.filterPPG(ppg)
#
# eda_final = pd.DataFrame({"time": time, "eda": filtered_eda})
# ppg_final = pd.DataFrame({"time": time, "ppg": filtered_ppg})
#
# eda_final.to_csv(subject + gsr_file +"filtered_eda.csv", index=False)
# ppg_final.to_csv(subject + gsr_file +"filtered_ppg.csv", index=False)
# filter ECG and Resp
ecg_resp = pd.read_csv(glob.glob(subject + resp_file + "*.csv")[0],
header=[0, 1])
time = ecg_resp.iloc[:, 0].values
ecg = ecg_resp.iloc[:, 8].values
resp = ecg_resp.iloc[:, 9].values
filtered_ecg_resp = ecg_features.waveDriftFilter(
ecg_features.filterECG(ecg), n=9)
filtered_resp = resp_features.filterResp(resp)
ecg_final = pd.DataFrame({"time": time, "ecg": filtered_ecg_resp})
resp_final = pd.DataFrame({"time": time, "resp": filtered_resp})
ecg_final.to_csv(subject + resp_file + "filtered_ecg_resp.csv",
index=False)
resp_final.to_csv(subject + resp_file + "filtered_resp.csv",
index=False)
except:
print("Error: " + subject)
time_start = np.min(ecg_road_data["timestamp"].values)
time_end = np.max(ecg_road_data["timestamp"].values)
tdelta = time_end - time_start
#features extractor
featuresExct = ECGFeatures(200)
#road test features
road_test_features = []
for j in np.arange(0, (tdelta // SPLIT_TIME), 0.2):
end = time_end - ((j) * SPLIT_TIME)
start = time_end - ((j + 1) * SPLIT_TIME) - EXTENTION_TIME
ecg = ecg_road_data[(ecg_road_data["timestamp"].values >= start)
& (ecg_road_data["timestamp"].values <= end)]
ecg_values = featuresExct.filterECG(ecg['ecg'].values)
ecg_values = signal.resample(ecg_values,
int((len(ecg_values) / FS_ECG) * 200))
time_domain = featuresExct.extractTimeDomain(ecg_values)
freq_domain = featuresExct.extractFrequencyDomain(ecg_values)
nonlinear_domain = featuresExct.extractNonLinearDomain(ecg_values)
if time_domain.shape[0] != 0 and freq_domain.shape[
0] != 0 and nonlinear_domain.shape[0] != 0:
concatenate_features = np.concatenate(
[time_domain, freq_domain, nonlinear_domain])
road_test_features.append(concatenate_features)
road_test_features = np.concatenate([road_test_features])
# concatenate features and normalize them