Fig 1.
Epileptiform patterns recorded from 4AP-treated hippocampus-CTX slices via MEA.
(A) Mouse brain slice placed on a planar 6 x 10 MEA grid (grid separation of 500 μm). Electrodes are grouped by their position in the brain structures comprised within the brain slice preparation. (B) Representative MEA recordings from the electrodes marked as CTX in panel A. The epileptiform pattern comprises both interictal and ictal events. The inserts show representative instances of each event type at an expanded time scale.
Fig 2.
Experiment design description.
A) Compendium of MEA recording datasets. B) Annotated signals showing the three different patterns, highlighted in different colors: baseline (green), interictal (yellow) and ictal (red). C) Test/train split of the dataset. D) Experiment design block diagram with the sequence of stages chosen for the experimental stage.
Table 1.
Brain slice MEA electrophysiology dataset description.
Table 2.
Algorithm optimization experiment matrix.
Optimization matrix for the two algorithms. Combinations for each of the parameters of the algorithm and experiments.
Fig 3.
ZDensityRODE algorithm flowchart.
Block diagram with the workflow of the Z-score Density-based Robust Outlier Detection Estimation algorithm.
Fig 4.
AMPD enhanced algorithm flowchart.
Block diagram with the workflow of the Automatic multiscale-based peak detection (AMPDE) algorithm. In the first stage, a bandpass filter between 0.5 and 50 Hz is set. Then an LMS calculation is performed for each window in the signal. A summation of each row in the LMS returns the vector γ, which after a rescaling obtains the final vector of peaks candidate, filtered with an evaluation method to identify the peaks on the vector.
Fig 5.
Features of epileptiform activity recorded from the entorhinal cortex.
A) Raw signal showing recurrent ictal and interictal events. B) Signal Kurtosis computed in 5 s windows. C) Shannon entropy, highlighting the variance and complexity of the signal over time. D) Spectrogram highlighting the wide range of frequency components in epileptiform events. E) Signal after high-pass filtering at ≥ 300Hz. F) Signal after low-pass filtering at ≤ 300Hz.
Fig 6.
Distinctive features of ictal and interictal events.
A) Overlayed ictal and ictal discharges and average signal (black). B) Violin plots of a selected number of features, highlighting distinctive characteristics of ictal and interictal events.
Fig 7.
Algorithms example of segmentation task through ZdensityRODE and AMPDE.
Output comparison for ZdensityRode and AMPDE algorithms against reference annotations for two representative signals, depicting high and low SNR scenarios. A) Representative signal with SNR ≥ 20 dB. B) Representative signal with SNR ≤ 20 dB. (0: baseline, 1: interictal event, 2: ictal event).
Fig 8.
A) Distribution of scores for each experimental session in the training stage; B) Distribution of SNR for each experimental session in the training stage; C) Distribution of scores for the validation dataset; D) Distribution of SNR for the validation dataset; E) Distribution of scores for classifier ZdensityRODE; F) Distribution of scores for classifier AMPD; G) Scores for each metric used in the experiment for ZdensityRODE algorithm and H) Scores for each metric used for AMPD algorithm.
Table 3.
This table shows the averaged results for each experimental session in the validation dataset. On the top, are the results for the ZdensityRODE algorithm, and on the bottom, are for the AMPD algorithm.
Table 4.
Comparative analysis of MEA event detection algorithms.