Comparison of Different Data Augmentation Techniques for Improving Epileptic Seizure Detection Based on 3D Acceleration, Heart Rate and Temperature Data

Maleyka Seyidova, Maleyka Seyidova, Jasmin Henze, Arne Pelzer, Beate Rhein

2024

Abstract

Epilepsy, characterized by recurrent seizures, poses a significant risk to an individual’s safety. To mitigate these risks, one approach is to use automated seizure detection systems based on Convolutional Neural Networks (CNN), which rely on large amounts of data to train effectively. However, real-world seizure data acquisition is challenging due to the short and infrequent nature of seizures, resulting in a data imbalance which complicates accurate seizure detection. In this paper, various data augmentation techniques were utilized to increase the amount of training data for CNN, aiming to investigate the potential of these techniques to enhance the performance of the seizure detection algorithm by providing more seizure data. For this purpose, two datasets, a unimodal (3D acceleration) and a multimodal dataset (3D acceleration, heart rate and temperature), were used. To evaluate the effect of the different augmentation techniques, a CNN trained without augmented data was used as a baseline. Experiments showed that data augmentation techniques improved the seizure detection by lowering the baseline’s false alarm rate while maintaining its high sensitivity. The best results were achieved with a combination of Rotation and Permutation in the multimodal dataset and Rotation, as well as Magnitude Warping, in the unimodal dataset.

Paper Citation

in Harvard Style

Seyidova M., Henze J., Pelzer A. and Rhein B. (2024). Comparison of Different Data Augmentation Techniques for Improving Epileptic Seizure Detection Based on 3D Acceleration, Heart Rate and Temperature Data. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-688-0, SciTePress, pages 142-153. DOI: 10.5220/0012386800003657

in Bibtex Style

@conference{healthinf24,
author={Maleyka Seyidova and Jasmin Henze and Arne Pelzer and Beate Rhein},
title={Comparison of Different Data Augmentation Techniques for Improving Epileptic Seizure Detection Based on 3D Acceleration, Heart Rate and Temperature Data},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2024},
pages={142-153},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012386800003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Comparison of Different Data Augmentation Techniques for Improving Epileptic Seizure Detection Based on 3D Acceleration, Heart Rate and Temperature Data
SN - 978-989-758-688-0
AU - Seyidova M.
AU - Henze J.
AU - Pelzer A.
AU - Rhein B.
PY - 2024
SP - 142
EP - 153
DO - 10.5220/0012386800003657
PB - SciTePress