5.4. MEA: Microelectrode Array Processing

Note

• User Guide for MEA to explain each function and steps is in progress. For now, please check the APIs: API Reference - MEA , which has the most detailed explanantion as documentation of all the functions.

• Also, check example code and try them with online - Binder with one click, to explore the functionalities with an example file - Examples: Micro-Electrode-Array (MEA)

Check examples here: Micro-Electrode-Array (MEA)

#TODO

Micro-Electrode Arrays System utilies an array of electrodes mounted on a small plate as a grid electrodes (e.g. 60) evenly spaced (700mm apart). It is used to analyse the eletrophysiology of cells/tissues under different clinical conditions by stimulating with certain voltage on a regular intervals. As shown in figure below, a plate of MEA system of 60 electrodes (source: https://www.multichannelsystems.com/products/meas-60-electrodes). One of the commonly used research field is the cardiac electrophysiology.

This python library analyse the recorded signal file, by extracting the electrograms (EGMs) from signal recoding of each eletrodes, and extracting the features of each EGM.

#TODO

5.4.1. Complete Analysis of a recording

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One of the simple function to provide complete analysis of recorded file is to use `spkit.mea.analyse_mea_file` function. This uses the default settings of all the paramters for extracting electrograms, identifying bad eletrodes, extracting features and plotting figures.

`spkit.mea.analyse_mea_file` needs two essential inputs, `files_name` : a full path of recoding file in ‘.h5’ format and `stim_fhz` frequency of stimulus in Hz.

An example with minimal settings can be found here

An example with minimal settings<auto_examples/multielectrode-array/plot_mea_minimal_setting_example.html>

Examples:

• MEA: Minimal Setting: Example

import spkit as sp
Features_df,Features_ch,Features_mat, Data = sp.mea.analyse_mea_file(files_name,stim_fhz=1)

5.4.2. Step-wise Analysis

Examples:

• MEA: Step-wise Analysis: Example

#TODO

There are 13 steps to analyse a recording file, which are as follow

1. Read HDF File

2. Stim loc

3. Align Cycles

4. Average Cycles/Select one

5. Activation Time

6. Activation & Repolarisation Time

7. APD

8. Extract EGM

9. EGM Feature Extraction

10. BAD Channels

11. Feature Matrix

12. Interpolation

13. Conduction Velocity

5.4.3. 1. Read HDF File

#TODO

spkit.io.read_hdf

5.4.4. 2. Stim Localisation

#TODO

spkit.mea.get_stim_loc

5.4.5. 3. Alignment of Stim Cycles

#TODO

spkit.mea.align_cycles

5.4.6. 4. Averaging Cycles or Selecting one

#TODO

5.4.7. 5. Activation Time

#TODO

spkit.mea.activation_time_loc

5.4.8. 6. Repolarisation Time (optional)

#TODO

spkit.mea.activation_repol_time_loc

5.4.9. 7. APD (if RT is computed)

#TODO

apd_ms = rt_loc_ms-at_loc_ms

5.4.10. 8. Extracting EGM

#TODO

spkit.mea.extract_egm

5.4.11. 9. EGM Feature Extraction

#TODO

spkit.mea.egm_features

5.4.12. 10. Identifying BAD Channels/electrodes

#TODO

spkit.mea.find_bad_channels_idx

5.4.13. 11. Creating Feature Matrix

#TODO

spkit.mea.mea_feature_map

5.4.14. 12. Interpolation

#TODO

spkit.fill_nans_2d

5.4.15. 13. Conduction Velocity

#TODO

spkit.mea.compute_cv

5.4.16. Plots and Figures

#TODO

• spkit.mea.plot_mea_grid

• spkit.mea.mea_feature_map

• spkit.mea.mat_list_show

• spkit.direction_flow_map

5.4.17. Extracting EGM

5.4.18. EGM Processing & Feature Extractions

5.4.19. Conduction and Activation Map