pdkit 0.4.2

Parkinson`s Disease Kit

PDKIT

TREMOR PROCESSOR

Example how to use pdkit to calculate tremor amplitude and frequency:

>>> import pdkit
>>> tp = pdkit.TremorProcessor()
>>> ts = pdkit.TremorTimeSeries().load(filename)
>>> amplitude, frequency = tp.amplitude(ts)

where, filename is the data path to load, by default in the cloudUPDRS format.

Pdkit can also read data in the MPower format, just like:

>>> ts = pdkit.TremorTimeSeries().load(filename, 'mpower')

where, filename is the data path to load in MPower format.

To calculate Welch, as a robust alternative to using Fast Fourier Transform, use like:

>>> amplitude, frequency = tp.amplitude(ts, 'welch')

This class also provides a method named extract_features to extract all the features available in Tremor Processor.

>>> tp.extract_features(ts)

BRADYKINESIA

>>> import pdkit
>>> ts = pdkit.TremorTimeSeries().load(filename)
>>> tp = pdkit.TremorProcessor(lower_frequency=0.0, upper_frequency=4.0)
>>> amplitude, frequency = tp.bradykinesia(ts)

GAIT

Example how to use pdkit to calculate various Gait features:

>>> import pdkit
>>> ts = pdkit.GaitTimeSeries().load(filename)
>>> gp = pdkit.GaitProcessor()
>>> freeze_times, freeze_indexes, locomotion_freezes = gp.freeze_of_gait(ts)
>>> frequency_of_peaks = gp.frequency_of_peaks(ts)
>>> speed_of_gait = gp.speed_of_gait(ts)
>>> step_regularity, stride_regularity, walk_symmetry = gp.walk_regularity_symmetry(ts)

where, filename is the data path to load, by default in the CloudUPDRS format.

FINGER TAPPING

Example how to use pdkit to calculate the mean alternate distance of the finger tapping tests:

>>> import pdkit
>>> ts = pdkit.FingerTappingTimeSeries().load(filename)
>>> ftp = pdkit.FingerTappingProcessor()
>>> ftp.mean_alnt_target_distance(ts)

kinesia scores (the number of key taps)

>>> ftp.kinesia_scores(ts)

TEST RESULT SET

Pdkit can be used to extract all the features for different measurements (i.e. tremor, finger tapping, gait) placed in a single folder. The result is a data frame where the measurements are rows and the columns are the features extracted.

>>> import pdkit
>>> testResultSet = pdkit.TestResultSet(folderpath)
>>> dataframe = testResultSet.process()

where folderpath is the relative folder with the different measurements. For CloudUPDRS there are measurements in the following folder ./tests/data.

We can also write the data frame to a output file like:

>>> testResultSet.write_output(dataframe, name)