This research proposes the Impulse Classification Network (ICN) using 1D Convolutional Neural Network (1D CNN) that able to detect noisy data and classify human VOR impulses. ICN is a high-performance classification method that works on a patient's video Head Impulse Test (vHIT) impulse data by identifying abnormalities and artifacts. Our ICN method found actual classes of patient’s impulses with 95% accuracy. Paper link
We provide train and test python files. We created our dataset which came from the ICS goggle device.
Lateral canal test type: left side vHIT data with a) normal and b) artifact impulses
Four type of classes
- Normal - 1081
- Abnormal - 804
- Artifact_phase_shift - 797
- Artifact_high_gain - 1115
Total 3797 impulses
python train.py -l labels.pickle
python test.py -m ./data/yourmodel.h5 -l lables.pickle -i impulse.csv
We can check the VOR gain values and predict the class of each impulse.
Artifact_high_gain – 100%
Normal class is detected 93.44%
Artifact_phase_shift – 98.72%
Dataset _labels_ready_new.csv - classes _left_ready.csv - impulses >>> row number 1:EYE_data 2:HEAD_data, 3:EYE_data 4:HEAD_data and etc ...