HRV: changed wrapper calcFeat method with specialized one.
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parent
3c058e4463
commit
075c64d1e5
28
config.yaml
28
config.yaml
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@ -510,18 +510,18 @@ EMPATICA_TEMPERATURE:
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CONTAINER: TEMP
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PROVIDERS:
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DBDP:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
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SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
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CR:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean",
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"longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope",
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"ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr",
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"calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent",
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"sumOfSquareComponents"]
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WINDOWS:
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COMPUTE: True
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COMPUTE: False
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WINDOW_LENGTH: 90 # specify window length in seconds
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SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
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@ -530,18 +530,18 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
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CONTAINER: EDA
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PROVIDERS:
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DBDP:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
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SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
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CR:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic',
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'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore',
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'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
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'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter',
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'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease']
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WINDOWS:
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COMPUTE: True
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COMPUTE: False
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WINDOW_LENGTH: 80 # specify window length in seconds
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SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py
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@ -551,15 +551,15 @@ EMPATICA_BLOOD_VOLUME_PULSE:
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PROVIDERS:
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DBDP:
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COMPUTE: True
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FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features
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"maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] # HRV features
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FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"]
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SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py
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CR:
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COMPUTE: True
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FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR']
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FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
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'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
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WINDOWS:
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COMPUTE: True
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WINDOW_LENGTH: 4 # specify window length in seconds
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WINDOW_LENGTH: 10 # specify window length in seconds
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SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-inter-beat-interval/
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@ -570,6 +570,14 @@ EMPATICA_INTER_BEAT_INTERVAL:
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COMPUTE: False
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FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"]
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SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py
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CR:
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COMPUTE: False
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FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
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'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
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WINDOWS:
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COMPUTE: True
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WINDOW_LENGTH: 4 # specify window length in seconds
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SRC_SCRIPT: src/features/inter_beat_interval/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-tags/
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EMPATICA_TAGS:
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@ -1,8 +1,8 @@
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import pandas as pd
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from scipy.stats import entropy
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from CalculatingFeatures.helper_functions import convertInputInto2d, hrvFeatureNames, frequencyFeatureNames
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from CalculatingFeatures.calculate_features import calculateFeatures
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from CalculatingFeatures.helper_functions import convertInputInto2d, hrvFeatureNames, hrvFreqFeatureNames
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from CalculatingFeatures.hrv import extractHrvFeatures, extractHrvFeatures2D, extractHrvFeatures2DWrapper
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import sys
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@ -13,7 +13,7 @@ def getSampleRate(data):
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except:
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raise Exception("Error occured while trying to get the mean sample rate from the data.")
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return 1000/timestamps_diff
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return int(1000/timestamps_diff)
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def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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@ -21,6 +21,8 @@ def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_lengt
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if not bvp_intraday_data.empty:
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sample_rate = getSampleRate(bvp_intraday_data)
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print(bvp_intraday_data.shape)
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bvp_intraday_data = filter_data_by_segment(bvp_intraday_data, time_segment)
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if not bvp_intraday_data.empty:
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@ -31,12 +33,20 @@ def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_lengt
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if window_length is None:
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bvp_intraday_features = \
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bvp_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), fs=int(sample_rate), featureNames=features))
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lambda x:
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extractHrvFeatures2DWrapper(
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convertInputInto2d(x['blood_volume_pulse'], x.shape[0]),
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sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, featureNames=features))
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else:
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bvp_intraday_features = \
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bvp_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features))
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lambda x:
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extractHrvFeatures2DWrapper(
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convertInputInto2d(x['blood_volume_pulse'], window_length*sample_rate),
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sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, featureNames=features))
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print(sample_rate)
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print(bvp_intraday_features)
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bvp_intraday_features.reset_index(inplace=True)
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return bvp_intraday_features
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@ -55,7 +65,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
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requested_window_length = None
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# name of the features this function can compute
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base_intraday_features_names = hrvFeatureNames + frequencyFeatureNames
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base_intraday_features_names = hrvFeatureNames + hrvFreqFeatureNames
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# the subset of requested features this function can compute
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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@ -0,0 +1,66 @@
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import pandas as pd
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from scipy.stats import entropy
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from CalculatingFeatures.helper_functions import convertInputInto2dTime, convert2DEmpaticaToArray hrvFeatureNames, hrvFreqFeatureNames
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from CalculatingFeatures.calculate_features import calculateFeatures
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import sys
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def getSampleRate(data):
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try:
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timestamps_diff = data['timestamp'].diff().dropna().mean()
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except:
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raise Exception("Error occured while trying to get the mean sample rate from the data.")
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return 1000/timestamps_diff
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def extractIBIFeaturesFromIntradayData(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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ibi_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not ibi_intraday_data.empty:
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sample_rate = getSampleRate(ibi_intraday_data)
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ibi_intraday_data = filter_data_by_segment(ibi_intraday_data, time_segment)
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if not ibi_intraday_data.empty:
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ibi_intraday_features = pd.DataFrame()
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# apply methods from calculate features module
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# if window_length is None:
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# ibi_intraday_features = \
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# ibi_intraday_data.groupby('local_segment').apply(\
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# lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), fs=int(sample_rate), featureNames=features))
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# else:
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# ibi_intraday_features = \
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# ibi_intraday_data.groupby('local_segment').apply(\
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# lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features))
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ibi_intraday_features.reset_index(inplace=True)
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return ibi_intraday_features
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def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_intraday_features = provider["FEATURES"]
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calc_windows = kwargs.get('calc_windows', False)
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if provider["WINDOWS"]["COMPUTE"] and calc_windows:
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requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
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else:
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requested_window_length = None
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# name of the features this function can compute
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base_intraday_features_names = hrvFeatureNames + hrvFreqFeatureNames
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# the subset of requested features this function can compute
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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# extract features from intraday data
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ibi_intraday_features = extractBVPFeaturesFromIntradayData(ibi_intraday_data, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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return ibi_intraday_features
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@ -27,7 +27,7 @@ else:
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if calc_windows:
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sensor_features.to_csv(snakemake.output[1], index=False)
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sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False)
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elif "empatica" in sensor_key and provider_key == "dbdp":
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elif "empatica" in sensor_key:
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pd.DataFrame().to_csv(snakemake.output[1], index=False)
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