Quantify delta F via paired motor unit analysis.
Conducts a paired motor unit analysis, quantifying delta F between the
supplied collection of motor units. Origional framework for deltaF provided
in Gorassini et. al., 2002:
https://journals.physiology.org/doi/full/10.1152/jn.00024.2001
Author: James (Drew) Beauchamp
| PARAMETER |
DESCRIPTION |
emgfile |
The dictionary containing the emgfile.
TYPE:
dict
|
smoothfits |
Smoothed discharge rate estimates.
Each array: motor unit discharge rate x samples aligned in time;
instances of non-firing = NaN
Your choice of smoothing. See compute_svr gen_svr for example.
TYPE:
list of arrays
|
average_method |
The method for test MU deltaF value. More to be added.
test_unit_average
The average across all possible control units.
all
This returns all possible MU pairs
TYPE:
str {"test_unit_average", "all"}
DEFAULT:
"test_unit_average"
|
normalisation |
TYPE:
str {"False", "ctrl_max_desc"}
DEFAULT:
"False"
|
recruitment_difference_cutoff |
An exlusion criteria corresponding to the necessary difference between
control and test MU recruitement in seconds.
TYPE:
float
DEFAULT:
1
|
corr_cutoff |
An exclusion criteria corresponding to the correlation between control
and test unit discharge rate.
TYPE:
float (0 to 1)
DEFAULT:
0.7
|
controlunitmodulation_cutoff |
An exclusion criteria corresponding to the necessary modulation of
control unit discharge rate during test unit firing in Hz.
TYPE:
float
DEFAULT:
0.5
|
clean |
To remove values that do not meet exclusion criteria
TYPE:
bool
DEFAULT:
True
|
| RETURNS |
DESCRIPTION |
delta_f
|
A pd.DataFrame containing deltaF values and corresponding MU number.
The resulting df will be different depending on average_method.
In particular, if average_method="all", delta_f[MU][row] will
contain a tuple representing the indices of the two motor units
for each given pair (reporter, test) and their corresponding
deltaF value.
TYPE:
DataFrame
|
See also
- compute_svr : fit MU discharge rates with Support Vector Regression,
nonlinear regression.
Examples:
Quantify delta F using svr fits.
>>> import openhdemg.library as emg
>>> emgfile = emg.emg_from_samplefile()
>>> emgfile = emg.sort_mus(emgfile=emgfile)
>>> svrfits = emg.compute_svr(emgfile)
>>> delta_f = emg.compute_deltaf(
... emgfile=emgfile, smoothfits=svrfits["gensvr"],
... )
delta_f
MU dF
0 0 NaN
1 1 NaN
2 2 NaN
3 3 1.838382
4 4 2.709522
For all possible combinations, not test unit average, MU in this case is
pairs (reporter, test).
>>> delta_f_2 = emg.compute_deltaf(
... emgfile=emgfile, smoothfits=svrfits["gensvr"], average_method='all',
... )
delta_f_2
MU dF
0 (0, 1) NaN
1 (0, 2) NaN
2 (0, 3) 2.127461
3 (0, 4) NaN
4 (1, 2) NaN
5 (1, 3) 1.549303
6 (1, 4) NaN
7 (2, 3) NaN
8 (2, 4) NaN
9 (3, 4) 2.709522