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研究開発

Sigronは、順天堂大学・東京農工大学との産学連携により、脳波解析・てんかん診断・AI自動判読・多施設研究データ基盤などの研究開発を進めています。研究で培った知見を医療サービスへ還元し、医療格差の解消をめざします。
以下に主な研究業績を掲載します。

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てんかん性棘波・突発性異常波の検出

  • N. Yoshida, K. Fukumori, A. Kurita, J. Hirota, H. Sugano, Y. Ohtomo, T. Shimizu, and T. Tanaka, “A possibility of machine learning as a learning tool to evaluate paroxysmal discharge in interictal EEG of childhood focal epilepsy,” Epilepsy & Seizure, vol. 16, no. 1, pp. 60–70, 2024. DOI

  • K. Fukumori, N. Yoshida, H. Sugano, M. Nakajima, and T. Tanaka, “Satelight: Self-attention-based model for epileptic spike detection from multi-electrode EEG,” Journal of Neural Engineering, vol. 19, no. 5, Art. no. 055007, 2022. DOI

  • K. Fukumori, N. Yoshida, H. Sugano, M. Nakajima, and T. Tanaka, “Epileptic spike detection using neural networks with linear-phase convolutions,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 3, pp. 1045–1056, 2022. DOI

てんかん発作の検出

  • K. M. Hassan, X. Zhao, H. Sugano, and T. Tanaka, “MR-EEGWaveNet: Multiresolutional EEGWaveNet for seizure detection from long EEG recordings,” APSIPA Transactions on Signal and Information Processing, vol. 14, no. 1, Art. no. e32, 2025. DOI

  • K. M. Hassan, X. Zhao, H. Sugano, and T. Tanaka, “Riemannian manifold-based epileptic seizure detection using transfer learning and artifact rejection techniques,” APSIPA Transactions on Signal and Information Processing, vol. 13, no. 1, 2024. DOI

  • X. Zhao, N. Yoshida, T. Ueda, H. Sugano, and T. Tanaka, “Epileptic seizure detection by using interpretable machine learning models,” Journal of Neural Engineering, 2023. DOI

  • T. Shoji, N. Yoshida, and T. Tanaka, “Automated detection of abnormalities from an EEG recording of epilepsy patients with a compact convolutional neural network,” Biomedical Signal Processing and Control, vol. 70, 2021. DOI

発作起始領域・てんかん焦点の推定

  • X. Zhao, Q. Zhao, A. Wang, H. Sugano, and T. Tanaka, “Classification of epileptic seizure onset zone from iEEG by reweighting augmented samples,” Biomedical Signal Processing and Control, vol. 120, no. PB, Art. no. 110105, 2026. DOI

  • Y. Miao, Y. Iimura, H. Sugano, and T. Tanaka, “Seizure onset zone identification using phase-amplitude coupling and multiple machine learning approaches for interictal electrocorticogram,” Cognitive Neurodynamics, 2022. DOI

  • X. Zhao, J. Solé-Casals, H. Sugano, and T. Tanaka, “Seizure onset zone classification based on imbalanced iEEG with data augmentation,” Journal of Neural Engineering, 2022. DOI

  • X. Zhao, Q. Zhao, T. Tanaka, J. Solé-Casals, G. Zhou, T. Mitsuhashi, H. Sugano, N. Yoshida, and J. Cao, “Classification of the epileptic seizure onset zone based on partial annotation,” Cognitive Neurodynamics, 2022. DOI

  • M. R. Islam, X. Zhao, Y. Miao, H. Sugano, and T. Tanaka, “Epileptic seizure focus detection from interictal electroencephalogram: A survey,” Cognitive Neurodynamics, 2022. DOI

  • M. S. Akter, M. R. Islam, T. Tanaka, Y. Iimura, T. Mitsuhashi, H. Sugano, D. Wang, and M. K. I. Molla, “Statistical features in high-frequency bands of interictal iEEG work efficiently in identifying the seizure onset zone in patients with focal epilepsy,” Entropy, vol. 22, no. 12, Art. no. 1415, 2020. DOI

  • M. S. Akter, M. R. Islam, Y. Iimura, H. Sugano, K. Fukumori, D. Wang, T. Tanaka, and A. Cichocki, “Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG,” Scientific Reports, vol. 10, no. 1, Art. no. 7044, 2020. DOI

てんかん脳波の生理学的・ネットワーク解析

  • Y. Miao, H. Suzuki, H. Sugano, T. Ueda, Y. Iimura, R. Matsui, and T. Tanaka, “Causal connectivity network analysis of ictal electrocorticogram with temporal lobe epilepsy based on dynamic phase transfer entropy,” IEEE Transactions on Biomedical Engineering, vol. 71, no. 2, pp. 531–541, 2024. DOI

  • T. Ueda, Y. Iimura, T. Mitsuhashi, H. Suzuki, Y. Miao, K. Nishioka, S. Tamrakar, R. Matsui, T. Tanaka, H. Otsubo, H. Sugano, and A. Kondo, “Chronological changes in phase-amplitude coupling during epileptic seizures in temporal lobe epilepsy,” Clinical Neurophysiology, vol. 148, pp. 44–51, 2023. DOI

  • S. Tamrakar, Y. Iimura, H. Suzuki, T. Mitsuhashi, T. Ueda, K. Nishioka, K. Karagiozov, M. Nakajima, Y. Miao, T. Tanaka, and H. Sugano, “Higher phase-amplitude coupling between ripple and slow oscillations indicates the distribution of epileptogenicity in temporal lobe epilepsy with hippocampal sclerosis,” Seizure, vol. 100, pp. 1–7, 2022. DOI

ECoGデコーディング・脳機能解析

  • S. Komeiji, T. Mitsuhashi, Y. Iimura, H. Suzuki, H. Sugano, K. Shinoda, and T. Tanaka, “Feasibility of decoding covert speech in ECoG with a Transformer trained on overt speech,” Scientific Reports, vol. 14, Art. no. 11491, 2024. DOI

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