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RunsData edited this page Dec 14, 2020 · 5 revisions

USER GUIDE for the Python Package wearablecompute.

wearablecompute is a package for Feature Engineering of Wearable Sensors.

Metrics computed:

  • Mean Heart Rate Variability
  • Median Heart Rate Variability
  • Maximum Heart Rate Variability
  • Minimum Heart Rate Variability
  • SDNN (HRV)
  • RMSSD (HRV)
  • NNx (HRV)
  • pNNx (HRV)
  • HRV Frequency Domain Metrics:
  • PowerVLF
  • PowerLF
  • PowerHF
  • PowerTotal
  • LF/HF
  • PeakVLF
  • PeakLF
  • PeakHF
  • FractionLF
  • FractionHF
  • EDA Peaks
  • Activity Bouts
  • Interday Summary:
  • Interday Mean
  • Interday Median
  • Interday Maximum
  • Interday Minimum
  • Interday Quartile 1
  • Interday Quartile 3
  • Interday Standard Deviation
  • Interday Coefficient of Variation
  • Intraday Standard Deviation (mean, median, standard deviation)
  • Intraday Coefficient of Variation (mean, median, standard deviation)
  • Intraday Mean (mean, median, standard deviation)
  • Daily Mean
  • Intraday Summary:
  • Intraday Mean
  • Intraday Median
  • Intraday Minimum
  • Intraday Maximum
  • Intraday Quartile 1
  • Intraday Quartile 3
  • TIR (Time in Range of default 1 SD)
  • TOR (Time outside Range of default 1 SD)
  • POR (Percent outside Range of default 1 SD)
  • MASE (Mean Amplitude of Sensor Excursions, default 1 SD)
  • Hours from Midnight (circadian rhythm feature)
  • Minutes from Midnight (ciracadian rhythm feature)

Functions:

e4import()

Brings in an empatica compiled file this is not raw empatica data Args: filepath (String): path to file sensortype (Sting): Options: 'EDA', 'HR', 'ACC', 'TEMP', 'BVP' Starttime (String): (optional, default arg = 'NaN') format '%Y-%m-%d %H:%M:%S.%f', if you want to only look at data after a specific time Endtime (String): (optional, default arg = 'NaN') format '%Y-%m-%d %H:%M:%S.%f', if you want to only look at data before a specific time window (String): default '5min'; this is the window your data will be resampled on. Returns: (pd.DataFrame): dataframe of data with Time, Mean, Std columns

HRV()

Computes Heart Rate Variability metrics Args: time (pandas.DataFrame column or pandas series): time column IBI (pandas.DataFrame column or pandas series): column with inter beat intervals ibimultiplier (IntegerType): defualt = 1000; transforms IBI to milliseconds. If data is already in ms, set as 1 Returns: maxHRV (FloatType): maximum HRV minHRV (FloatType): minimum HRV meanHRV (FloatType): mean HRV medianHRV(FloatType): median HRV

SDNN

Computes Heart Rate Variability metric SDNN Args: time (pandas.DataFrame column or pandas series): time column IBI (pandas.DataFrame column or pandas series): column with inter beat intervals ibimultiplier (IntegerType): defualt = 1000; transforms IBI to milliseconds. If data is already in ms, set as 1 Returns: SDNN (FloatType): standard deviation of NN intervals

RMSSD

Computes Heart Rate Variability metric RMSSD Args: time (pandas.DataFrame column or pandas series): time column IBI (pandas.DataFrame column or pandas series): column with inter beat intervals ibimultiplier (IntegerType): defualt = 1000; transforms IBI to milliseconds. If data is already in ms, set as 1 Returns: RMSSD (FloatType): root mean square of successive differences

NNx

Computes Heart Rate Variability metrics NNx and pNNx Args: time (pandas.DataFrame column or pandas series): time column IBI (pandas.DataFrame column or pandas series): column with inter beat intervals ibimultiplier (IntegerType): defualt = 1000; transforms IBI to milliseconds. If data is already in ms, set as 1 x (IntegerType): default = 50; set the number of times successive heartbeat intervals exceed 'x' ms Returns: NNx (FloatType): the number of times successive heartbeat intervals exceed x ms pNNx (FloatType): the proportion of NNx divided by the total number of NN (R-R) intervals.

FrequencyHRV()

Computes Heart Rate Variability frequency domain metrics Args: IBI (pandas.DataFrame column or pandas series): column with inter beat intervals ibimultiplier (IntegerType): defualt = 1000; transforms IBI to milliseconds. If data is already in ms, set as 1 fs (IntegerType): Optional sampling frequency for frequency interpolation (default=1) Returns: (dictionary): dictionary of frequency domain HRV metrics with keys: PowerVLF (FloatType): Power of the Very Low Frequency (VLF): 0-0.04Hz band PowerLF (FloatType): Power of the Low Frequency (LF): 0.04-0.15Hz band PowerHF (FloatType): Power of the High Frequency (HF): 0.15-0.4Hz band PowerTotal (FloatType):Total power over all frequency bands LF/HF (FloatType): Ratio of low and high power Peak VLF (FloatType): Peak of the Very Low Frequency (VLF): 0-0.04Hz band Peak LF (FloatType): Peak of the Low Frequency (LF): 0.04-0.15Hz band Peak HF (FloatType): Peak of the High Frequency (HF): 0.15-0.4Hz band FractionLF (FloatType): Fraction that is low frequency FractionHF (FloatType): Fraction that is high frequency

PeaksEDA()

Calculates peaks in the EDA signal Args: eda (pandas.DataFrame column or pandas series): eda column time (pandas.DataFrame column or pandas series): time column Returns: countpeaks (IntegerType): the number of peaks total peakdf (pandas.DataFrame): a pandas dataframe with time and peaks to easily integrate with your data workflow

exercisepts()

Calculates activity bouts using accelerometry and heart rate Args: acc (pandas.DataFrame column or pandas series): accelerometry column hr (pandas.DataFrame column or pandas series): heart rate column time (pandas.DataFrame column or pandas series): time column Returns: countbouts (IntegerType): the number of acitvity bouts total returndf (pandas.DataFrame): a pandas dataframe with time and activity bouts (designated as a '1') to easily integrate with your data workflow

interdaycv()

Computes the interday coefficient of variation on pandas dataframe Sensor column Args: column (pandas.DataFrame column or pandas series): column that you want to calculate over Returns: cvx (IntegerType): interday coefficient of variation

interdaysd()

Computes the interday standard deviation of pandas dataframe Sensor column Args: column (pandas.DataFrame column or pandas series): column that you want to calculate over Returns: interdaysd (IntegerType): interday standard deviation

intradaycv()

Computes the intradaycv, returns the mean, median, and sd of intraday cv Sensor column in pandas dataframe Args: column (pandas.DataFrame column or pandas series): column that you want to calculate over time (pandas.DataFrame): time column timeformat (String): default = '%Y-%m-%d %H:%M:%S.%f'; format of timestamp in time column Returns: intradaycv_mean (IntegerType): Mean, Median, and SD of intraday coefficient of variation intradaycv_median (IntegerType): Median of intraday coefficient of variation intradaycv_sd (IntegerType): SD of intraday coefficient of variation Requires: interdaycv() function

intradaysd()

Computes the intradaysd, returns the mean, median, and sd of intraday sd Sensor column in pandas dataframe Args: column (pandas.DataFrame column or pandas series): column that you want to calculate over time (pandas.DataFrame): time column timeformat (String): default = '%Y-%m-%d %H:%M:%S.%f'; format of timestamp in time column Returns: intradaysd_mean (IntegerType): Mean, Median, and SD of intraday standard deviation intradaysd_median (IntegerType): Median of intraday standard deviation intradaysd_sd (IntegerType): SD of intraday standard deviation

intradaymean()

Computes the intradaymean, returns the mean, median, and sd of the intraday mean of the Sensor data Args: column (pandas.DataFrame column or pandas series): column that you want to calculate over time (pandas.DataFrame): time column timeformat (String): default = '%Y-%m-%d %H:%M:%S.%f'; format of timestamp in time column Returns: intradaymean_mean (IntegerType): Mean, Median, and SD of intraday standard deviation intradaymean_median (IntegerType): Median of intraday standard deviation intradaymean_sd (IntegerType): SD of intraday standard deviation

dailymean()

Computes the mean of each day Args: column (pandas.DataFrame column or pandas series): column that you want to calculate over time (pandas.DataFrame): time column timeformat (String): default = '%Y-%m-%d %H:%M:%S.%f'; format of timestamp in time column Returns: pandas.DataFrame with days and means as columns

dailysummary()

Computes the summary of each day (mean, median, std, max, min, Q1G, Q3G) Args: column (pandas.DataFrame column or pandas series): column that you want to calculate over time (pandas.DataFrame): time column timeformat (String): default = '%Y-%m-%d %H:%M:%S.%f'; format of timestamp in time column Returns: pandas.DataFrame with days and summary metrics as columns

interdaysummary()

Computes interday mean, median, minimum and maximum, and first and third quartile over a column Args: column (pandas.DataFrame column or pandas series): column that you want to calculate over dataframe (True/False): default=True; whether you want a pandas DataFrame as an output or each of the summary metrics as IntegerTypes Returns: pandas.DataFrame with columns: Mean, Median, Standard Deviation, Minimum, Maximum, Quartile 1, Quartile 3

        or
        
        interdaymean (FloatType): mean 
        interdaymedian (FloatType): median 
        interdaysd (FloatType) : standard deviation
        interdaymin (FloatType): minimum 
        interdaymax (FloatType): maximum 
        interdayQ1 (FloatType): first quartile 
        interdayQ3 (FloatType): third quartile
TIR()

Computes time in the range of (default=1 sd from the mean)column in pandas dataframe Args: df (pandas.DataFrame): column (pandas.DataFrame column or pandas series): column that you want to calculate over sd (IntegerType): standard deviation from mean for range calculation (default = 1 SD) sr (IntegerType): sampling rate of sensor Returns: TIR (IntegerType): Time in Range set by sd, *Note time is relative to your SR

TOR()

Computes time outside the range of (default=1 sd from the mean) column in pandas dataframe Args: df (pandas.DataFrame): column (pandas.DataFrame column or pandas series): column that you want to calculate over sd (IntegerType): standard deviation from mean for range calculation (default = 1 SD) sr (IntegerType): sampling rate of sensor Returns: TOR (IntegerType): Time outside of range set by sd, *Note time is relative to your SR

POR()

Computes percent time outside the range of (default=1 sd from the mean) column in pandas dataframe Args: df (pandas.DataFrame): column (pandas.DataFrame column or pandas series): column that you want to calculate over sd (IntegerType): standard deviation from mean for range calculation (default = 1 SD) sr (IntegerType): Returns: POR (IntegerType): percent of time spent outside range set by sd

MASE()

Computes the mean amplitude of sensor excursions (default = 1 sd from the mean) Args: df (pandas.DataFrame): column (pandas.DataFrame column or pandas series): column that you want to calculate over sd (IntegerType): standard deviation from mean to set as a sensor excursion (default = 1 SD) Returns: MASE (IntegerType): Mean Amplitude of sensor excursions

crhythm()

Computes 'minutes from midnight' and 'hours from midnight'- these features will allow you to account for circaidan rhythm effects Args: time (pandas.DataFrame): time column timeformat (String): default = '%Y-%m-%d %H:%M:%S.%f'; format of timestamp in time column Returns: hourfrommid (ListType): Hours from midnight, the same length as your time column minfrommid (ListType): Minutes from midnight, the same length as your time column

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