Extracellular Features Extraction for MEA Data¶
This example shows how to use extracellular features implemented in eFEL for recordings from Microelectrode Arrays (MEA). Also see Buccino et al., 2024 and another example using these efeatures for more details.
from efel.pyfeatures import extrafeats
import numpy as np
Currently, the following features are implemented in eFEL:
extrafeats.all_1D_features
['peak_to_valley',
'halfwidth',
'peak_trough_ratio',
'repolarization_slope',
'recovery_slope',
'neg_peak_relative',
'pos_peak_relative',
'neg_peak_diff',
'pos_peak_diff',
'neg_image',
'pos_image']
These features are described as functions in extrafeats.py. You can find the docstrings of these functions by using the help function in Python. You can find more documentation of the features here: https://efel.readthedocs.io/en/latest/
help(extrafeats.peak_to_valley)
Help on function peak_to_valley in module efel.pyfeatures.extrafeats:
peak_to_valley(waveforms, sampling_frequency)
Time between trough and peak. If the peak precedes the trough,
peak_to_valley is negative.
Parameters
----------
waveforms : numpy.ndarray (num_waveforms x num_samples)
waveforms to compute feature for
sampling_frequency : float
rate at which the waveforms are sampled (Hz)
Returns
-------
np.ndarray (num_waveforms)
peak_to_valley in seconds
Let’s get the test MEA data and calculate the extracellular features.
waveforms_fpath = "../../tests/testdata/extrafeats/mean_waveforms.dat"
waveforms = np.loadtxt(waveforms_fpath)
waveform = np.array([waveforms[0]])
sampling_freq = 10000
extrafeats.peak_to_valley(waveforms=waveform, sampling_frequency=sampling_freq)
array([0.0013])
The waveform should have a structure similar to the one in
eFEL/tests/testdata/extrafeats/mean_waveforms.datmean_waveforms.dat. We
will use the calculate_features function from extrafeats to
calculate all the features.
feats = extrafeats.calculate_features(
waveforms, sampling_freq
)
for feature_name in extrafeats.all_1D_features:
print(feature_name, feats[feature_name])
peak_to_valley [ 0.0013 -0.0001 -0.0016 -0.0013 -0.0011 -0.0013 -0.0012 0.0009 -0.0011
-0.0002 -0.0004 -0.0008 0.0031 -0.0018 0.0016 -0.0007 -0.0009 0.0009
-0.0007 -0.0018 -0.0013 -0.0005 -0.0015 -0.0011 0.0013 -0.0034 0.0018
0.001 -0.0019 -0.0015 -0.0014 0.0002 -0.0014 -0.0012 -0.0016 0.0008
0.0014 -0.0005 -0.0025 0.0009 0.0014 -0.0016 -0.0013 0.0009 -0.0008
-0.0016 -0.0016 -0.0019 -0.0013 -0.0014 -0.0011 0.0003 -0.0014 -0.0014
-0.001 -0.0013 -0.0006 -0.0024 0.0018 -0.0035 -0.0012 -0.0012 0.0002
-0.0013 -0.0023 -0.0002 -0.0001 0.0032 0.0024 -0.0013 0.0029 -0.0006
0.001 -0.001 0.0013 0.0022 0.0009 -0.0014 -0.0014 0.0018 0.0007
0.0008 -0.0017 0.0014 0.0007 -0.0016 -0.0008 0.0027 -0.0021 -0.0012
-0.0015 -0.0001 -0.0001 0.0009 0.0016 -0.0001 0.0006 0.0015 0.0011
-0.0009 0.0012 -0.0012 0.0013 -0.0012 0.0007 -0.0023 -0.0009 -0.0013
-0.0002 -0.001 -0.0011 -0.0012 -0.0015 -0.0011 -0.0012 -0.0014 0.0003
0.0009 0.0009 0.0006 0.0002 0.0008 -0.0006 0.0034 0.0014 -0.0013
-0.0011 -0.0008 0.0007 -0.0011 -0.0013 0.0009 -0.0013 0.0009 0.0009
-0.0018 -0.0012 0.0002 -0.0013 -0.0019 -0.0002 -0.0041 0.0009 -0.0009
-0.0006 -0.001 -0.0015 0.0009 -0.0012 -0.002 -0.0008 -0.0032 0.0015
0.0021 -0.0002 -0.0005 -0.0009 -0.0008 -0.001 -0.0013 0.0014 -0.001
0.0009 -0.0013 -0.0017 0.0021 -0.0016 -0.0011 -0.0014 -0.0019 0.0011
-0.0016 -0.0002 -0.001 -0.0018 -0.0015 -0.0001 0.002 0.0009 -0.0015
-0.0014 -0.0017 0.0009 -0.0007 0.0013 -0.0024 -0.0013 -0.0037 -0.0002
-0.0007 -0.0006 -0.0011 -0.0016 -0.0013 -0.0001 -0.001 -0.0014 -0.0015
-0.0005 -0.0004 -0.0015 -0.0001 -0.0015 -0.0011 0.0002 -0.0013 -0.0012
-0.0011 -0.0013]
halfwidth [ 0.0015 nan -0.0028 -0.0093 -0.0025 -0.0017 -0.0018 0.0003 -0.0024
nan nan -0.0055 0.0017 -0.003 0.0006 -0.0017 -0.0014 nan
-0.0011 nan -0.0025 -0.0068 -0.003 -0.0024 0.0007 -0.0054 0.0007
nan -0.0035 nan -0.0023 nan -0.0026 -0.0019 -0.0029 nan
nan nan -0.0039 nan nan -0.0029 -0.0025 nan -0.0017
-0.0026 nan -0.0035 -0.0023 -0.0018 -0.0017 nan -0.0028 nan
-0.0016 -0.0023 nan -0.0042 0.0008 -0.0045 -0.0022 -0.0019 nan
-0.0027 -0.0026 -0.0008 nan 0.0009 0.0008 nan 0.0014 nan
0.0005 -0.0017 0.0007 0.0015 0.0012 -0.002 -0.0019 0.0012 0.0004
0.0009 -0.0033 0.0006 0.0005 -0.0031 -0.0018 0.0006 -0.0038 -0.0024
-0.0025 nan nan 0.002 0.0006 nan nan 0.0005 0.0006
-0.0022 nan -0.0026 nan -0.0025 nan -0.0039 -0.0019 -0.0018
nan -0.0016 -0.0015 -0.0024 -0.003 -0.0023 nan -0.002 nan
nan nan nan nan nan nan 0.0019 nan -0.0018
-0.0017 -0.002 0.0005 -0.0017 -0.0023 0.002 -0.0067 0.0005 nan
nan -0.0019 nan -0.0022 -0.0036 nan -0.0063 0.0005 -0.0015
-0.0018 -0.0023 -0.0019 nan -0.0026 -0.0037 -0.0019 -0.0051 0.0006
0.0008 nan nan -0.0019 -0.002 -0.0016 -0.0027 0.0007 -0.0024
nan -0.002 -0.003 0.0007 -0.0031 -0.0024 nan -0.0036 0.0014
nan nan -0.0016 -0.0035 -0.0081 nan 0.0005 0.0003 -0.0024
-0.0028 nan 0.0007 -0.0018 nan -0.0046 -0.0024 -0.0059 nan
-0.0019 -0.0009 -0.0018 -0.003 -0.0024 nan -0.0016 -0.0028 -0.0029
-0.0073 -0.0044 -0.0025 nan -0.003 nan nan -0.0019 -0.0018
-0.0048 -0.0023]
peak_trough_ratio [ 0.53804035 1.21802672 2.36845269 0.13818893 2.01288236 0.67247375
0.71054337 0.51431686 1.2270848 6.12281799 0.16201052 0.52271024
0.29541054 2.10863154 0.16400563 0.71021801 0.82259676 0.80606722
0.59820375 1.67405533 1.22329924 0.19102443 2.6186373 1.35055906
0.42408852 6.55820515 0.16284988 0.93212375 3.95194239 0.61651983
4.12676067 0.7973423 0.82446904 0.63280487 0.94402227 0.83385778
0.31889983 0.40030984 13.01325066 0.41722616 1.08734046 1.28142004
1.31052961 0.65915915 0.59007358 8.24318384 1.05782205 3.37112047
0.54651881 0.73548706 1.13247942 2.20866198 2.01767528 0.97482789
0.47452749 0.6706796 6.99816194 5.11126778 0.18733708 6.95707896
0.48524825 0.60336188 0.9870102 2.79221104 9.70193971 0.82660372
1.19167412 0.21163596 0.20311777 0.72466183 0.14251774 1.44321506
0.53334603 0.90387643 0.41733713 0.24190003 0.53594822 0.73317705
0.69903345 0.31630318 0.64635683 0.97632033 2.84277294 0.57842661
0.67426552 3.12048394 0.65460616 0.18484049 4.40800015 3.50836316
2.37435616 2.72846297 0.81115791 0.57493701 0.39853579 1.71386828
5.21433688 0.16672223 0.49929396 1.67457084 1.5233474 2.58614124
0.66188939 2.5763163 1.11925818 4.77781519 0.53758802 0.36358919
3.00864117 0.96411726 0.45938812 2.93000602 2.33622968 1.09317424
0.49026222 1.13618701 1.50261237 1.095218 0.38294177 0.8881845
3.26153958 0.8232666 0.5709424 0.22638494 2.34391786 0.59851957
0.40645699 1.3843255 0.75217496 0.58369778 0.85124889 0.5570616
0.33895087 0.48671009 0.6832888 2.75731074 0.58309425 7.37870261
0.62792104 4.58416164 6.30390847 4.72034757 0.59310884 0.67547227
0.22271289 1.72432913 0.28557396 0.69169406 1.80447415 4.37127311
0.96403529 9.13984054 0.36332231 0.18233296 0.81281747 0.39035498
1.56471095 1.34022733 0.79437089 4.12955594 0.39527652 2.11227852
0.73075634 1.26226321 1.79751798 0.1394111 2.89838121 1.93914669
0.69080368 3.72053666 0.32107693 1.08166173 1.62173755 0.83715129
3.76741793 0.19781369 1.34602015 0.22818912 0.85238754 5.67776085
7.99932939 2.45151786 0.3190137 1.05551016 1.1821116 3.85214816
1.04723524 4.71443887 0.30886562 0.39164463 0.41260801 0.51914942
3.12387873 1.49623728 1.28426178 0.87490264 2.41248035 2.18445792
0.24783548 0.2482083 3.15501273 1.11067998 2.65673549 0.37181242
6.12408649 0.64401332 1.50091732 0.48633446 0.72905253]
repolarization_slope [7.31257213e+01 nan 6.50182478e-01 4.99764537e+01
1.01266031e+00 1.22505405e+01 3.66794226e+00 3.96887490e+01
2.08781290e+00 nan 2.66397081e+01 3.32739630e+01
3.99171396e+01 1.18709472e+00 1.89150163e+02 5.33856699e+00
4.44666871e+00 nan 6.59520145e+00 1.35164366e+00
2.82988737e+00 2.03543510e+01 4.85872501e-01 1.38560311e+00
8.93961074e+01 1.15821548e-01 9.74608965e+01 4.35300936e+01
2.48036396e-01 5.35377718e+00 4.19533385e-01 nan
3.87647107e+00 2.49081159e+01 2.53626726e+00 3.08366795e+02
1.12289446e+02 7.72393222e+00 1.02580692e-01 nan
5.83851918e+00 1.57677921e+00 1.92203478e+00 6.57320993e+01
1.54010808e+01 1.55766806e-01 2.41955804e+00 3.11922659e-01
1.52125356e+01 3.20115632e+00 4.41163627e+00 nan
7.79157923e-01 2.62026144e+00 6.73181141e+01 7.50472345e+00
nan 1.36751428e-01 5.34852086e+01 2.24802060e-01
5.15398199e+01 2.30980102e+01 nan 5.65438604e-01
3.84641022e-01 nan nan 1.90051711e+01
5.68442402e+01 6.28630703e+00 1.20718467e+01 nan
8.79612883e+01 3.09045447e+00 1.36219748e+02 1.07188996e+01
1.19465006e+02 3.74992327e+00 5.11446110e+00 3.21324376e+01
2.96762385e+02 2.87781921e+02 4.56945190e-01 1.36938154e+02
2.44544112e+02 3.82173278e-01 4.28763535e+00 1.25128169e+01
1.69948984e-01 4.05290769e-01 7.46124701e-01 nan
nan 6.40825688e+01 7.90614702e+01 nan
nan 5.41251231e+02 5.78885301e+01 1.34395745e+00
nan 7.11621115e-01 4.54818048e+01 6.68880108e-01
nan 1.63878756e-01 9.48684456e+00 1.27452569e+01
nan 7.83978570e+00 2.70935240e+01 5.88313421e-01
6.93437974e-01 2.65871059e+00 2.13100092e+01 2.53094985e+00
nan nan 1.06479171e+02 nan
nan 1.97160273e+02 6.90330690e+00 4.15423798e+00
nan 7.32781106e+00 9.63477588e+01 1.89820897e+00
2.54461786e+02 6.19672821e+01 4.78810459e+00 1.05531879e+02
1.04548966e+01 1.24175790e+02 6.02423181e+01 9.37029728e-01
3.43643506e+01 nan 1.24964841e+01 1.56175850e-01
nan 1.77657282e-01 1.04539302e+02 2.10076218e+01
1.22608385e+01 1.41715843e+00 6.93772653e+00 1.63626246e+01
1.10691890e+00 1.69822463e-01 2.90615132e+00 9.16422355e-02
8.81610344e+01 3.49923843e+01 nan 1.45976613e+01
9.41618161e-01 1.76125600e+00 1.09905341e+01 3.63785992e-01
3.26088462e+01 9.29907719e-01 7.64039846e+01 2.86223544e+00
8.74881544e-01 4.62083858e+01 4.79718173e-01 1.10037652e+00
7.23525875e+00 2.33396674e-01 6.76706409e+01 2.92417470e+00
nan 1.39168207e+01 2.47310145e-01 2.75639683e+01
nan 1.20943344e+02 1.26141937e+01 3.12423625e-01
1.69656396e-01 1.80390321e+00 2.74981969e+01 2.43878552e+00
nan 2.14533667e-01 2.97934250e+00 1.55177673e-01
nan 6.74687462e+00 1.16212026e+01 6.70505162e+01
4.12664511e-01 2.44764617e+00 nan 9.28700444e+00
8.35177563e-01 7.86531878e-01 7.90299729e+00 2.12171085e+01
5.12327842e-01 nan 5.99487790e-01 2.87914657e+01
nan 5.07647764e+00 3.12094982e+00 5.37494106e+01
8.01730656e+00]
recovery_slope [-3.63355521e+00 4.54342435e+00 -2.10494514e+00 -3.21612844e+01
-1.59355549e+01 -2.91941227e+01 -4.48096741e+00 -7.79253365e+00
-1.93142445e+01 -5.43590968e+00 -7.84073969e+01 -4.67504152e+01
-1.24537129e+00 9.04851649e-01 -9.23155491e+00 -5.20729298e+01
-2.88969544e+01 -1.92130265e+01 -4.31626734e+01 2.16413225e+00
-1.04642376e+01 -8.08408718e+01 -5.87632497e+00 -1.09565866e+01
-5.85332555e+00 -1.16608216e+00 -3.21579887e+00 -2.17739809e+01
-5.07126055e+00 -1.02362027e+01 -2.03299056e+00 -7.71686527e-01
-2.51163221e+00 -2.95649418e+01 -8.15932912e-01 -5.13599982e+01
-9.32113618e+00 -4.50290554e+01 -3.80578627e+00 -1.24285027e+01
-9.27802447e-01 -2.00340018e+00 -6.20759074e+00 -2.66042436e+01
-5.09533788e+01 -3.97681438e+00 -1.18503344e+00 -4.60929399e+00
-6.92211691e+00 -6.16165160e+00 -1.12842409e+01 -1.75193125e+00
-6.96007348e+00 -4.96107984e+00 -3.46614424e+01 -5.24159703e+00
-8.08372841e+00 -2.71314048e+00 -2.65011956e+00 5.62134860e-01
-1.78506949e+01 -1.81238510e+01 2.00340882e+00 -1.06992198e+01
-3.91580166e+00 -4.59364500e-01 2.75542585e+00 -3.77812716e-01
-1.39327173e+00 -1.32048883e+01 -1.86680577e-01 -3.93456185e+00
-7.64656083e+00 -1.68954306e+01 -4.34881443e+00 -8.69229998e-01
-1.19791230e+01 -6.67007742e+00 -1.23338715e+01 -3.92168564e+00
-3.26267482e+01 -4.89466829e+01 -5.50323478e+00 -4.06361556e+01
-2.04860872e+01 -6.46205930e+00 -2.42733005e+01 -3.29076079e-01
-2.18624572e+00 -8.64471967e+00 -2.57717988e+00 -4.83779739e-01
5.09670643e+00 -2.81096643e+01 -1.24310724e+01 -2.41915951e+00
-1.51126413e+00 -2.88979477e+01 -3.37945486e+00 -2.08947684e+01
-1.87378608e+01 -1.32479872e+01 -8.10451785e+00 -7.21654548e+00
-7.39351372e+00 -2.88267735e+00 -2.66157324e+01 -2.01121237e+01
-3.67737909e+00 -2.19363285e+01 -1.54238008e+01 -6.18020049e+00
-8.82350024e+00 -1.78646598e+01 1.80605389e+00 -4.14716330e+00
-5.43485978e+00 -1.06769351e+01 -5.30541741e+01 -2.97564361e+00
-3.98414646e-01 -2.97626033e+01 -4.44445283e+01 -7.16824627e-02
-7.93094190e+00 -1.32033245e+01 -2.81984189e+01 -3.04757744e+01
-3.08283335e+01 -4.18498964e+01 -6.57149163e+00 -4.39076727e+01
-1.37147948e+01 -1.12276678e+01 -2.03882091e+01 1.19113164e+00
-1.87193042e+01 -4.29831701e+00 -1.10030127e+01 -2.22290578e+00
-5.84355534e+00 1.97905425e+00 -1.04236166e+01 -3.58815562e+01
-4.53173319e+01 -2.11364258e+01 -1.20790837e+01 -1.46042168e+01
-1.16542322e+01 -2.16928796e+00 -3.84443397e+01 -1.10930702e+00
-1.35325880e+01 -1.02588094e+00 4.08781011e+00 -6.78991393e+01
-9.65564853e+00 -3.02388143e+01 -2.85310794e+01 -5.89109388e+00
-1.32740623e+00 -1.60552191e+01 -3.65615502e+01 -1.23223306e+01
-1.19126869e+00 -1.38971257e+00 -7.08911891e+00 -1.72027697e+01
-1.10750593e+01 -2.40240059e+00 -3.18275518e+00 -2.23994382e+00
-2.46730697e+00 -2.53630321e+01 -4.32395820e+00 -3.46648203e+01
8.31871590e+00 -2.99608434e+00 -1.09693318e+01 -1.76399365e+00
-5.12560876e+00 -3.74529408e+00 -2.74762612e+01 -3.48078286e+01
-1.38484473e+01 -3.84538670e+00 -1.56589216e+00 -1.16947634e+00
1.22042755e+01 -3.35964001e+01 -6.04604562e+01 -3.74568162e+01
-7.22145075e+00 -7.70012328e+00 6.40803600e+00 -1.85702048e+01
-1.07774241e+01 -1.07239397e+01 -3.73939408e+01 -7.05625361e+01
-2.21224102e+00 8.72531968e-01 -8.22518343e+00 -5.93215146e+00
3.04381793e+00 -9.72311749e+00 -1.89302412e+01 -3.75853073e+01
-8.14213856e+00]
neg_peak_relative [9.51339213e-02 1.00388035e-02 3.28724224e-03 8.63910846e-02
1.23287678e-02 2.81335375e-02 1.35270547e-02 3.26901910e-02
1.75885984e-02 5.17095565e-03 1.10390041e-01 5.58803425e-02
8.93325636e-02 5.76913099e-03 4.77727744e-01 4.79924335e-02
2.80982170e-02 2.47240713e-02 4.01139776e-02 6.51939114e-03
2.71661034e-02 8.94666451e-02 4.32888964e-03 1.12655112e-02
1.44575946e-01 8.34988705e-04 2.75001421e-01 2.65226106e-02
2.80205172e-03 1.80731068e-02 1.04085591e-03 1.01575434e-02
2.66661308e-02 5.82650282e-02 1.86321664e-02 2.87389870e-01
5.76186146e-02 4.13010277e-02 6.84346712e-04 1.97046304e-02
3.54932940e-03 9.85326097e-03 1.91838933e-02 4.03909653e-02
5.65890648e-02 1.05026994e-03 1.00670272e-02 3.24793977e-03
7.57699886e-02 1.20001955e-02 1.74041886e-02 4.45264419e-03
6.35282086e-03 1.33618267e-02 1.03643170e-01 4.93752872e-02
3.23877510e-03 1.29991624e-03 1.79916050e-01 2.00675228e-03
9.85910621e-02 5.49954957e-02 1.24116089e-02 6.46973356e-03
1.52057421e-03 1.29378784e-02 1.64824978e-02 6.58365941e-02
1.47911176e-01 2.02344756e-02 5.88078649e-02 8.70456343e-03
1.11993825e-01 1.82114418e-02 1.15731751e-01 6.45806158e-02
1.49667367e-01 1.48037917e-02 1.83947157e-02 1.26785426e-01
2.93172551e-01 2.44271425e-01 4.66676594e-03 3.49910014e-01
2.49263853e-01 4.24394443e-03 2.39841963e-02 6.20606547e-02
1.40386954e-03 3.65911205e-03 2.80730871e-03 8.65798745e-03
1.67705222e-02 4.02185452e-02 2.58374175e-01 1.24129259e-02
1.19555561e-03 1.00000000e+00 6.84316052e-02 1.45336614e-02
1.38767710e-02 8.06665123e-03 2.41699581e-02 4.09648680e-03
1.80323783e-02 1.67985703e-03 3.35210216e-02 2.76382021e-02
7.01153240e-03 3.53932427e-02 7.93280312e-02 3.32079102e-03
6.57178672e-03 1.74822659e-02 7.32635742e-02 1.02818073e-02
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Another example of extraction of these features from the experimental data can be found in the multimodalfitting repository.