pdkit 0.4

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.process(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.process(ts, 'welch')

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, 'ft_cloudupdrs')
>>> ftp = pdkit.FingerTappingProcessor()
>>> ftp.mean_alnt_target_distance(ts)

kinesia scores (the number of key taps)

>>> ftp.kinesia_scores(ts)