207 lines
11 KiB
Markdown
207 lines
11 KiB
Markdown
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# Calculating features
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## Usage
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- Install the library with:
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```
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pip install pep517
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python -m pep517.build .
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Alternative:
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pip install build
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python -m build
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```
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- Basic usage is:
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```
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from calculatingfeatures.CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, frequencyFeatureNames, hrvFeatureNames
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from calculatingfeatures.CalculatingFeatures.calculate_features import calculateFeatures
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import pandas as pd
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pathToHrvCsv = "example_data/S2_E4_Data/BVP.csv"
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windowLength = 500
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# get an array of values from HRV empatica file
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hrv_data, startTimeStamp, sampleRate = convert1DEmpaticaToArray(pathToHrvCsv)
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# Convert the HRV data into 2D array
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hrv_data_2D = convertInputInto2d(hrv_data, windowLength)
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# Create a list with feature names
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featureNames = []
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featureNames.extend(hrvFeatureNames)
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featureNames.extend(frequencyFeatureNames)
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pd.set_option('display.max_columns', None)
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# Calculate features
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calculatedFeatures = calculateFeatures(hrv_data_2D, fs=int(sampleRate), featureNames=featureNames)
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```
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- More usage examples are located in **usage_examples.ipynb** file
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## Features
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- Features are returned (from calculateFeatures() function) in a Pandas DataFrame object.
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- In the case if a feature couldn't be calculated (for example, if input signal is invalid), NaN value is returned.
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- Further in this section, the list with descriptions of all possible features is presented.
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### GSR features:
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These features are useful for 1D GSR(EDA) signals
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- `mean`: mean of the signal
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- `std`: standard deviation of signal
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- `q25`: 0.25 quantile
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- `q75`: 0.75 quantile
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- `qd`: q75 - q25
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- `deriv`: sum of gradients of the signal
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- `power`: power of the signal (mean of squared signal)
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- `numPeaks`: number of EDA peaks
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- `ratePeaks`: average number of peaks per second
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- `powerPeaks`: power of peaks (mean of signal at indexes of peaks)
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- `sumPosDeriv`: sum of positive derivatives divided by number of all derivatives
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- `propPosDeriv`: proportion of positive derivatives per all derivatives
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- `derivTonic`: sum of gradients of the tonic
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- `sigTonicDifference`: mean of tonic subtracted from signal
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- `freqFeats`:
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- `maxPeakAmplitudeChangeBefore`: maximum peak amplitude change before peak
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- `maxPeakAmplitudeChangeAfter`: maximum peak amplitude change after peak
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- `avgPeakAmplitudeChangeBefore`: average peak amplitude change before peak
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- `avgPeakAmplitudeChangeAfter`: average peak amplitude change after peak
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- `avgPeakChangeRatio`: avg_peak_increase_time / avg_peak_decrease_time
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- `maxPeakIncreaseTime`: maximum peak increase time
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- `maxPeakDecreaseTime`: maximum peak decrease time
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- `maxPeakDuration`: maximum peak duration
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- `maxPeakChangeRatio`: max_peak_increase_time / max_peak_decrease_time
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- `avgPeakIncreaseTime`: average peak increase time
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- `avgPeakDecreaseTime`: average peak decreade time
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- `avgPeakDuration`: average peak duration
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- `maxPeakResponseSlopeBefore`: maximum peak response slope before peak
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- `maxPeakResponseSlopeAfter`: maximum peak response slope after peak
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- `signalOverallChange`: maximum difference between samples (max(sig)-min(sig))
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- `changeDuration`: duration between maximum and minimum values
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- `changeRate`: change_duration / signal_overall_change
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- `significantIncrease`:
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- `significantDecrease`:
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### HRV features:
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These features are useful for 1D HRV(BVP) signals.
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If number of RR intervals (numRR) is less than `length of sample / (2 * sampling rate)` (30 BPM) or greater than `length of sample / (sampling rate / 4)` (240 BPM), BPM value is incorrect and thus, all other HRV features are set to NaN.
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- `meanHr`: mean heart rate
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- `ibi`: mean interbeat interval
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- `sdnn`: standard deviation of the ibi
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- `sdsd`: standard deviation of the differences between all subsequent R-R intervals
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- `rmssd`: root of the mean of the list of squared differences
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- `pnn20`: the proportion of NN20 intervals to all intervals
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- `pnn50`: the proportion of NN50 intervals to all intervals
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- `sd`:
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- `sd2`:
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- `sd1/sd2`: sd / sd2 ratio
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- `numRR`: number of RR intervals
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### Accelerometer features:
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These features are useful for 3D signals from accelerometer
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- `meanLow`: mean of low-pass filtered signal
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- `areaLow`: area under the low-pass filtered signal
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- `totalAbsoluteAreaBand`: sum of absolute areas under the band-pass filtered x, y and z signal
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- `totalMagnitudeBand`: square root of sum of squared band-pass filtered x, y and z components
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- `entropyBand`: entropy of band-pass filtered signal
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- `skewnessBand`: skewness of band-pass filtered signal
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- `kurtosisBand`: kurtosis of band-pass filtered signal
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- `postureDistanceLow`: calculates difference between mean values for a given sensor (low-pass filtered)
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- `absoluteMeanBand`: mean of band-pass filtered signal
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- `absoluteAreaBand`: area under the band-pass filtered signal
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- `quartilesBand`: quartiles of band-pass filtered signal
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- `interQuartileRangeBand`: inter quartile range of band-pass filtered signal
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- `varianceBand`: variance of band-pass filtered signal
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- `coefficientOfVariationBand`: dispersion of band-pass filtered signal
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- `amplitudeBand`: difference between maximum and minimum sample of band-pass filtered signal
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- `totalEnergyBand`: total magnitude of band-pass filtered signal
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- `dominantFrequencyEnergyBand`: ratio of energy in dominant frequency
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- `meanCrossingRateBand`: the number of signal crossings with mean of band-pass filtered signal
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- `correlationBand`: Pearson's correlation between band-pass filtered axis
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- `quartilesMagnitudesBand`: quartiles at 25%, 50% and 75% per band-pass filtered signal
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- `interQuartileRangeMagnitudesBand`: interquartile range of band-pass filtered signal
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- `areaUnderAccelerationMagnitude`: area under acceleration magnitude
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- `peaksDataLow`: number of peaks, sum of peak values, peak avg, amplitude avg
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- `sumPerComponentBand`: sum per component of band-pass filtered signal
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- `velocityBand`: velocity of the band-pass filtered signal
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- `meanKineticEnergyBand`: mean kinetic energy 1/2*mV^2 of band-pass filtered signal
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- `totalKineticEnergyBand`: total kinetic energy 1/2*mV^2 for all axes (band-pass filtered)
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- `squareSumOfComponent`: squared sum of component
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- `sumOfSquareComponents`: sum of squared components
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- `averageVectorLength`: mean of magnitude vector
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- `averageVectorLengthPower`: square mean of magnitude vector
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- `rollAvgLow`: maximum difference of low-pass filtered roll samples
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- `pitchAvgLow`: maximum difference of low-pass filtered pitch samples
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- `rollStdDevLow`: standard deviation of roll (calculated from low-pass filtered signal)
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- `pitchStdDevLow`: standard deviation of pitch (calculated from low-pass filtered signal)
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- `rollMotionAmountLow`: amount of wrist roll (from low-pass filtered signal) motion
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- `rollMotionRegularityLow`: regularity of wrist roll motion
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- `manipulationLow`: manipulation of low-pass filtered signals
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- `rollPeaks`: number of roll peaks, sum of roll peak values, roll peak avg, roll amplitude avg
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- `pitchPeaks`: number of pitch peaks, sum of pitch peak values, pitch peak avg, pitch amplitude avg
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- `rollPitchCorrelation`: correlation between roll and peak (obtained from low-pass filtered signal)
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### Gyroscope features:
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These features are useful for 3D signals from gyroscope
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- `meanLow`: mean of low-pass filtered signal
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- `areaLow`: area under the low-pass filtered signal
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- `totalAbsoluteAreaLow`: sum of absolute areas under the low-pass filtered x, y and z signal
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- `totalMagnitudeLow`: square root of sum of squared band-pass filtered x, y and z components
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- `entropyLow`: entropy of low-pass filtered signal
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- `skewnessLow`: skewness of low-pass filtered signal
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- `kurtosisLow`: kurtosis of low-pass filtered signal
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- `quartilesLow`: quartiles of low-pass filtered signal
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- `interQuartileRangeLow`: inter quartile range of low-pass filtered signal
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- `varianceLow`: variance of low-pass filtered signal
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- `coefficientOfVariationLow`: dispersion of low-pass filtered signal
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- `amplitudeLow`: difference between maximum and minimum sample of low-pass filtered signal
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- `totalEnergyLow`: total magnitude of low-pass filtered signal
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- `dominantFrequencyEnergyLow`: ratio of energy in dominant frequency
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- `meanCrossingRateLow`: the number of signal crossings with mean of low-pass filtered signal
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- `correlationLow`: Pearson's correlation between low-pass filtered axis
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- `quartilesMagnitudeLow`: quartiles at 25%, 50% and 75% per low-pass filtered signal
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- `interQuartileRangeMagnitudesLow`: interquartile range of band-pass filtered signal
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- `areaUnderMagnitude`: area under magnitude
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- `peaksCountLow`: number of peaks in low-pass filtered signal
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- `averageVectorLengthLow`: mean of low-pass filtered magnitude vector
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- `averageVectorLengthPowerLow`: square mean of low-pass filtered magnitude vector
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### Generic features:
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These are generic features, useful for many different types of signals
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- `autocorrelations`: autocorrelations of the given signal with lags 5, 10, 20, 30, 50, 75 and 100
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- `countAboveMean`: number of values in signal that are higher than the mean of signal
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- `countBelowMean`: number of values in signal that are lower than the mean of signal
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- `maximum`: maximum value of the signal
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- `minimum`: minimum value of the signal
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- `meanAbsChange`: the mean of absolute differences between subsequent time series values
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- `longestStrikeAboveMean`: longest part of signal above mean
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- `longestStrikeBelowMean`: longest part of signal below mean
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- `stdDev`: standard deviation of the signal
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- `median`: median of the signal
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- `meanChange`: the mean over the differences between subsequent time series values
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- `numberOfZeroCrossings`: number of crossings of signal on 0
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- `absEnergy`: the absolute energy of the time series which is the sum over the squared values
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- `linearTrendSlope`: a linear least-squares regression for the values of the time series versus the sequence from 0 to length of the time series minus one
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- `ratioBeyondRSigma`: ratio of values that are more than r*std(x) (so r sigma) away from the mean of signal. r in this case is 2.5
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- `binnedEntropy`: entropy of binned values
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- `numOfPeaksAutocorr`: number of peaks of autocorrelations
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- `numberOfZeroCrossingsAutocorr`: number of crossings of autocorrelations on 0
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- `areaAutocorr`: area under autocorrelations
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- `calcMeanCrossingRateAutocorr`: the number of autocorrelation crossings with mean
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- `countAboveMeanAutocorr`: umber of values in signal that are higher than the mean of autocorrelation
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- `sumPer`: sum per component
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- `sumSquared`: squared sum per component
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- `squareSumOfComponent`: square sum of component
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- `sumOfSquareComponents`:sum of square components
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### Frequency features:
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These are frequency features, useful for many different types of signals. The signal is converted to power spectral density signal and features are calculated on this signal
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- `fqHighestPeakFreqs`: three frequencies corresponding to the largest peaks added to features
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- `fqHighestPeaks`: three largest peaks added to features
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- `fqEnergyFeat`: energy calculated as the sum of the squared FFT component magnitudes, and normalized
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- `fqEntropyFeat`: entropy of the FFT of the signal
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- `fqHistogramBins`: Binned distribution (histogram)
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- `fqAbsMean`: absolute mean of the raw signal
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- `fqSkewness`: skewness of the power spectrum of the data
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- `fqKurtosis`: kurtosis of the power spectrum of the data
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- `fqInterquart`: inter quartile range of the raw signal
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