| """This file contains the ecg feature extraction pipelines and functions used for calculating the features.""" | |
| import neurokit2 as nk | |
| from .configs import selected_features | |
| def get_hrv_features(ecg_signal, fs): | |
| # Find peaks | |
| peaks, info = nk.ecg_peaks(ecg_signal, sampling_rate=fs, method="pantompkins1985") | |
| # Compute time domain features | |
| hrv_time_features = nk.hrv_time(peaks, sampling_rate=fs) | |
| # Compute frequency domain features | |
| #hrv_frequency_features = nk.hrv_frequency(peaks, sampling_rate=fs, method="welch", show=False) | |
| # Concat features | |
| #hrv_features = pd.concat([hrv_time_features, hrv_frequency_features], axis=1) | |
| hrv_features = hrv_time_features | |
| # to dict | |
| hrv_features = hrv_features[selected_features].to_dict(orient="records") | |
| return hrv_features | |
| def normalize_features(features_df, normalization_method): | |
| if normalization_method == "difference": | |
| baseline_features = features_df[features_df['baseline'] == True].iloc[0] | |
| features_df.loc[features_df['baseline'] == False, features_df.columns.isin(selected_features)] -= baseline_features | |
| elif normalization_method == "relative": | |
| baseline_features = features_df[features_df['baseline'] == True].iloc[0] | |
| features_df.loc[features_df['baseline'] == False, features_df.columns.isin(selected_features)] /= baseline_features | |
| elif (normalization_method == "separate") or (normalization_method is None): | |
| pass | |
| return features_df | |