The Effect of Maxblur-pooling in Neural Networks on Shift-invariance Issue in Various Biological Signal Classification Tasks

Xianyin Hu, Shangyin Zou, Yuki Ban, Shin’ichi Warisawa

Abstract

Modern neural networks are widely employed in bio-signal processing due to their effectiveness. However, recent research showed that neural networks for image recognition is not shift-invariant as it was assumed, while it is an important property in bio-signal processing. Fortunately, a simple methodology was proposed referred to as Maxblur-pooling to improve the shift-invariance of neural networks for image recognition. However, the corresponding issue in the domain of bio-signal processing remains untouched. To verify the shift-invariance of neural networks when applied to bio-signal processing, we performed two experiments across different tasks and types of bio-signals. One is Atrial Fibrillation (AF) detection from R-R interval and the other is emotion recognition from multi-channel EEG. We were able to show that the lack of shift-invariance also happens in temporal bio-signal classification. In the AF detection task, we succeed to validate the effectiveness of Maxblur-pooling, which demonstrating improvements in both accuracy (2%-13%) and consistency (8%-15%) compared to the baseline. While for the emotion recognition task, we did not observe any improvements using Maxblur-pooling. Our research provided empirical knowledge for developing real-time diagnose systems that is stable to temporal shifts.

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Paper Citation


in Harvard Style

Hu X., Zou S., Ban Y. and Warisawa S. (2020). The Effect of Maxblur-pooling in Neural Networks on Shift-invariance Issue in Various Biological Signal Classification Tasks.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, ISBN 978-989-758-398-8, pages 49-59. DOI: 10.5220/0008879900490059


in Bibtex Style

@conference{biosignals20,
author={Xianyin Hu and Shangyin Zou and Yuki Ban and Shin’ichi Warisawa},
title={The Effect of Maxblur-pooling in Neural Networks on Shift-invariance Issue in Various Biological Signal Classification Tasks},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,},
year={2020},
pages={49-59},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008879900490059},
isbn={978-989-758-398-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,
TI - The Effect of Maxblur-pooling in Neural Networks on Shift-invariance Issue in Various Biological Signal Classification Tasks
SN - 978-989-758-398-8
AU - Hu X.
AU - Zou S.
AU - Ban Y.
AU - Warisawa S.
PY - 2020
SP - 49
EP - 59
DO - 10.5220/0008879900490059