Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
BioX, SIP, IE, ITE-IST, ITE-ME [detail] |
2023-05-19 10:00 |
Mie |
Sansui Hall, Mie University (Primary: On-site, Secondary: Online) |
EEG-Based Sleep Stage Classification Using a Self-Attention Model Aozora Ito, Kosuke Fukumori, Toshihisa Tanaka (TUAT) SIP2023-9 BioX2023-9 IE2023-9 |
EEG-based sleep stage classification is clinically significant, and deep learning is the dominant method for automating ... [more] |
SIP2023-9 BioX2023-9 IE2023-9 pp.35-40 |
SP, IPSJ-SLP, EA, SIP [detail] |
2023-02-28 15:05 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Identification of Seizure Onset Zone from Intracranial EEG Using Source Selection-Based Domain Adaptation Keisuke Matsubayashi (TUAT), Yasushi Iimura, Takumi Mitsuhashi, Hidenori Sugano (Juntendo Univ.), Kosuke Fukumori, Toshihisa Tanaka (TUAT) |
[more] |
|
SIP, CAS, VLD, MSS |
2021-07-05 13:00 |
Online |
Online |
Epileptic Spike Detection from Electroencephalogram with Self-Attention Mechanism Kosuke Fukumori (TUAT), Noboru Yoshida, Hidenori Sugano, Madoka Nakajima (Juntendo Univ.), Toshihisa Tanaka (TUAT) CAS2021-3 VLD2021-3 SIP2021-13 MSS2021-3 |
Automated identification of epileptiform discharges for the diagnosis of epilepsy can mitigate the burden of the exhaust... [more] |
CAS2021-3 VLD2021-3 SIP2021-13 MSS2021-3 pp.11-15 |
SP, EA, SIP |
2020-03-03 09:00 |
Okinawa |
Okinawa Industry Support Center (Cancelled but technical report was issued) |
[Poster Presentation]
Comparison of Neural Network Models for Detection of Spatiotemporal Abnormal Intervals in Epileptic EEG Kosuke Fukumori (TUAT), Noboru Yoshida (Juntendo Univ.), Toshihisa Tanaka (TUAT) EA2019-156 SIP2019-158 SP2019-105 |
Epilepsy is a chronic brain disease, and the detection of abnormal waveforms by scalp electroencephalography (EEG) is an... [more] |
EA2019-156 SIP2019-158 SP2019-105 pp.319-323 |
SP, EA, SIP |
2020-03-03 09:00 |
Okinawa |
Okinawa Industry Support Center (Cancelled but technical report was issued) |
[Poster Presentation]
EpiNet: Convolutional Neural Network for Epileptic Seizure Localization from Interictal Intracranial EEG Kosuke Mori, Kosuke Fukumori, Toshihisa Tanaka (TUAT), Yasushi Iimura, Takumi Mitsuhashi, Hidenori Sugano (Juntendo Univ.) EA2019-157 SIP2019-159 SP2019-106 |
The electroencephalogram (EEG) recording is necessary for epileptic diagnosis. In particular, the intracranial EEG (iEEG... [more] |
EA2019-157 SIP2019-159 SP2019-106 pp.325-330 |
EA, SIP, SP |
2019-03-15 13:30 |
Nagasaki |
i+Land nagasaki (Nagasaki-shi) |
EA2018-136 SIP2018-142 SP2018-98 |
Epilepsy is chronic brain disorder that affects 50 million people in the world. To diagnose epilepsy, specialists manual... [more] |
EA2018-136 SIP2018-142 SP2018-98 pp.217-222 |
EA, SIP, SP |
2019-03-15 13:30 |
Nagasaki |
i+Land nagasaki (Nagasaki-shi) |
[Poster Presentation]
Epileptic Focus Detection from Interictal Electroencephalogram using RNN Byambadorj Nyamradnaa, Kosuke Fukumori, Toshihisa Tanaka (TAT), Yasushi Iimura, Takumi Mitsuhashi, Hidenori Sugano (Juntendo Univ.) EA2018-138 SIP2018-144 SP2018-100 |
Epilepsy is a neurological disorder characterized by recurrent seizures. An option of treatment is surgical resection of... [more] |
EA2018-138 SIP2018-144 SP2018-100 pp.229-231 |
EA, SIP, SP |
2019-03-15 13:30 |
Nagasaki |
i+Land nagasaki (Nagasaki-shi) |
[Poster Presentation]
Epileptic Spike Detection and Identification of Effective Frequency Band with Neural Networks Kosuke Fukumori (TUAT), Noboru Yoshida (Juntendo Univ.), Toshihisa Tanaka (TUAT) EA2018-139 SIP2018-145 SP2018-101 |
Epilepsy is a complex neurological disorder and can lead to an adverse impact on an individual's cognitive functions.
I... [more] |
EA2018-139 SIP2018-145 SP2018-101 pp.233-235 |
SIP, EA, SP, MI (Joint) [detail] |
2018-03-19 14:45 |
Okinawa |
|
Optimization of Gaussian Kernel Parameters for Kernel Logistic Regression Kosuke Fukumori, Tomoya Wada, Toshihisa Tanaka (TUAT) EA2017-135 SIP2017-144 SP2017-118 |
The kernel logistic regression is a nonlinear classification model that effectively uses kernel methods, which are one o... [more] |
EA2017-135 SIP2017-144 SP2017-118 pp.185-190 |