Shankar-16 / Epilepsy Detection using TinyML Public
Target: Arduino Nano 33 BLE Sense (Cortex-M4F 64MHz) Clone this project

Test data

Set thresholds
Model variants
Test 'Unoptimized (float32)' model Test 'Quantized (int8)' model
Set the 'expected outcome' for each sample to the desired outcome to automatically score the impulse.

Apply filters

Sample name Expected outcome Length Accuracy Result
2.2c0emino 2 10s 59% 10 2, 5 1, 2 uncertain
Show classification Show in data acquisition Show in feature explorer
2.2c0f4tjq 2 10s 59% 10 2, 6 1, 1 uncertain
Show classification Show in data acquisition Show in feature explorer
2.2c0eu6ch 2 10s 71% 12 2, 3 uncertain, 2 1
Show classification Show in data acquisition Show in feature explorer
2.2c0iun07 2 10s 100% 17 2
Show classification Show in data acquisition Show in feature explorer
2.2c0ipvjg 2 10s 100% 17 2
Show classification Show in data acquisition Show in feature explorer
2.2c0irmj1 2 10s 82% 14 2, 2 1, 1 uncertain
Show classification Show in data acquisition Show in feature explorer
1.2c0iai37 1 10s 65% 11 1, 3 2, 3 uncertain
Show classification Show in data acquisition Show in feature explorer
1.2c0i75h0 1 10s 100% 17 1
Show classification Show in data acquisition Show in feature explorer
1.2c0i6m08 1 10s 53% 9 1, 4 2, 4 uncertain
Show classification Show in data acquisition Show in feature explorer
n1.2c0dv8su 1 10s 94% 16 1, 1 uncertain
Show classification Show in data acquisition Show in feature explorer
n1.2c0dtr9g 1 10s 100% 17 1
Show classification Show in data acquisition Show in feature explorer
n1.2c0dqfs0 1 10s 100% 17 1
Show classification Show in data acquisition Show in feature explorer
Filter yielded no results, click to adjust the filter

Model testing output

Results

Model version:
Accuracy
81.86%

Confusion matrix

Loading...

Feature explorer

Loading...
Help & documentation Get help on the forum Send feedback Contact sales
Join us
Terms of Service Privacy policy