Abstract
Autoencoder (AE) is known as an artificial intelligence (AI), which is considered to be useful to analyze the bio-signal (BS) and/or conduct simulations of the BS. We can show examples to study Electrogastrograms (EGGs) and Electroencephalograms (EEGs) as a BS. In previous study, we have analyzed the EGGs by using the AE and have compared mathematical models of EGGs in the seated posture with those in the supine. The EEGs of normal subjects and patients with Meniere’s disease were herein converted to lower dimensions using Variational AE (VAE). The existence of characteristic differences was verified.
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Acknowledgments
This work was supported in part by the Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (C) Number 20K12528, 22K12141 and 23H03678.
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Nakane, K., Sugie, R., Nakayama, M., Matsuura, Y., Shiozawa, T., Takada, H. (2023). A Study on Variational Autoencoder to Extract Characteristic Patterns from Electroencephalograms and Electrogastrograms. In: Kurosu, M., et al. HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14054. Springer, Cham. https://doi.org/10.1007/978-3-031-48038-6_11
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