Calculating HRV features with IBI.csv.

sociality-task
Primoz 2022-04-20 10:44:51 +00:00
parent 8c8fe1fec7
commit 66451160e9
5 changed files with 26 additions and 11 deletions

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@ -576,7 +576,7 @@ EMPATICA_INTER_BEAT_INTERVAL:
'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
WINDOWS: WINDOWS:
COMPUTE: True COMPUTE: True
WINDOW_LENGTH: 4 # specify window length in seconds WINDOW_LENGTH: 120 # specify window length in seconds
SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py
# See https://www.rapids.science/latest/features/empatica-tags/ # See https://www.rapids.science/latest/features/empatica-tags/

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@ -62,7 +62,9 @@ def extract_empatica_data(data, sensor):
df.index.name = 'timestamp' df.index.name = 'timestamp'
elif sensor == 'EMPATICA_INTER_BEAT_INTERVAL': elif sensor == 'EMPATICA_INTER_BEAT_INTERVAL':
df = pd.read_csv(sensor_data_file, names=['timestamp', column], header=None) df = pd.read_csv(sensor_data_file, names=['timestamp', column], header=None)
df['timings'] = df['timestamp']
timestampstart = float(df['timestamp'][0]) timestampstart = float(df['timestamp'][0])
df['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart df['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart
df = df.drop([0]) df = df.drop([0])

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@ -50,6 +50,7 @@ EMPATICA_INTER_BEAT_INTERVAL:
TIMESTAMP: timestamp TIMESTAMP: timestamp
DEVICE_ID: device_id DEVICE_ID: device_id
INTER_BEAT_INTERVAL: inter_beat_interval INTER_BEAT_INTERVAL: inter_beat_interval
TIMINGS: timings
MUTATION: MUTATION:
COLUMN_MAPPINGS: COLUMN_MAPPINGS:
SCRIPTS: # List any python or r scripts that mutate your raw data SCRIPTS: # List any python or r scripts that mutate your raw data

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@ -217,6 +217,7 @@ EMPATICA_INTER_BEAT_INTERVAL:
- TIMESTAMP - TIMESTAMP
- DEVICE_ID - DEVICE_ID
- INTER_BEAT_INTERVAL - INTER_BEAT_INTERVAL
- TIMINGS
EMPATICA_TAGS: EMPATICA_TAGS:
- TIMESTAMP - TIMESTAMP

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@ -1,12 +1,14 @@
import pandas as pd import pandas as pd
from scipy.stats import entropy import numpy as np
from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features
from cr_features.calculate_features import calculate_features from cr_features.hrv import extract_hrv_features_2d_wrapper
import math
import sys import sys
pd.set_option('display.max_rows', 1000) pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', None)
def get_sample_rate(data): def get_sample_rate(data):
@ -28,23 +30,32 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_
if not ibi_intraday_data.empty: if not ibi_intraday_data.empty:
ibi_intraday_features = pd.DataFrame() ibi_intraday_features = pd.DataFrame()
# np.set_printoptions(threshold=sys.maxsize)
# print(ibi_intraday_data.groupby('local_segment').apply(lambda x: math.ceil(x['timings'].iloc[-1])))
# nekaj = ibi_intraday_data.groupby('local_segment').apply(lambda x: \
# convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[1])
print(ibi_intraday_data.head(100)) # sys.exit()
sys.exit()
# apply methods from calculate features module # apply methods from calculate features module
if window_length is None: if window_length is None:
ibi_intraday_features = \ ibi_intraday_features = \
ibi_intraday_data.groupby('local_segment').apply(\ ibi_intraday_data.groupby('local_segment').apply(\
lambda x:
extract_hrv_features_2d_wrapper( extract_hrv_features_2d_wrapper(
convert_to2d(x['inter_beat_interval'], window_length*sample_rate), signal_2D = \
sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[0],
ibi_timings = \
convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[1],
sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
else: else:
ibi_intraday_features = \ ibi_intraday_features = \
ibi_intraday_data.groupby('local_segment').apply(\ ibi_intraday_data.groupby('local_segment').apply(\
lambda x:
extract_hrv_features_2d_wrapper( extract_hrv_features_2d_wrapper(
convert_to2d(x['blood_volume_pulse'], window_length*sample_rate), signal_2D = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[0],
sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) ibi_timings = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[1],
sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
ibi_intraday_features.reset_index(inplace=True) ibi_intraday_features.reset_index(inplace=True)