Added SO feature for sum all rows that are non zero for BVP and IBI sensors.

config.yaml

@@ -3,7 +3,7 @@
 ########################################################################################################################
 
 # See https://www.rapids.science/latest/setup/configuration/#participant-files
-PIDS: [p03] #p01, p02, p03]
+PIDS: [p01] #p01, p02, p03]
 
 # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
 CREATE_PARTICIPANT_FILES:
@@ -559,7 +559,7 @@ EMPATICA_BLOOD_VOLUME_PULSE:
       WINDOWS:
         COMPUTE: True
         WINDOW_LENGTH: 300 # specify window length in seconds
-        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows']
+        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows', 'hrv_num_windows_non_zero']
         STANDARDIZE_FEATURES: False
       SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py
 
@@ -575,11 +575,11 @@ EMPATICA_INTER_BEAT_INTERVAL:
       COMPUTE: True
       FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
                   'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features            
-      PATCH_WITH_BVP: True
+      PATCH_WITH_BVP: False
       WINDOWS:
         COMPUTE: True
         WINDOW_LENGTH: 300 # specify window length in seconds
-        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows']
+        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows', 'hrv_num_windows_non_zero']
         STANDARDIZE_FEATURES: False
       SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py
 

src/features/cr_features_helper_methods.py

@@ -24,7 +24,11 @@ def extract_second_order_features(intraday_features, so_features_names):
 
         # numPeaksNonZero specialized for EDA sensor
         if "eda_num_peaks_non_zero" in so_features_names and "numPeaks" in intraday_features.columns:
-                so_features["SO_numPeaksNonZero"] = intraday_features.groupby("local_segment")["numPeaks"].apply(lambda x: (x!=0).sum())
+            so_features["SO_numPeaksNonZero"] = intraday_features.groupby("local_segment")["numPeaks"].apply(lambda x: (x!=0).sum())
+
+        # numWindowsNonZero specialized for BVP and IBI sensors
+        if "hrv_num_windows_non_zero" in so_features_names and "meanHr" in intraday_features.columns:
+            so_features["SO_numWindowsNonZero"] = intraday_features.groupby("local_segment")["meanHr"].apply(lambda x: (x!=0).sum())
             
         so_features.reset_index(inplace=True)
 

src/features/empatica_electrodermal_activity/cr/main.py

@@ -30,11 +30,11 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_
             if window_length is None:
                 eda_intraday_features = \
                     eda_intraday_data.groupby('local_segment').apply(\
-                    lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, threshold=0, featureNames=features)) 
+                    lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, featureNames=features)) 
             else:
                 eda_intraday_features = \
                     eda_intraday_data.groupby('local_segment').apply(\
-                    lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, threshold=0, featureNames=features)) 
+                    lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, featureNames=features)) 
 
             eda_intraday_features.reset_index(inplace=True)
 

src/features/empatica_inter_beat_interval/cr/main.py

@@ -69,6 +69,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
     # extract features from intraday data
     ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute, 
                                                                 requested_window_length, time_segment, filter_data_by_segment)
+
     if calc_windows:
         if provider["WINDOWS"].get("STANDARDIZE_FEATURES", False):
             fo_columns = ibi_intraday_features.columns.values[2:]
@@ -80,5 +81,4 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
 
         return ibi_intraday_features, ibi_second_order_features
 
-
     return ibi_intraday_features