Modified Kohonen's self-organizing feature map and its application to automatic sleep cycle recognition

Max Blanchet; Shuji Yoshizawa; Shun ichi Amari

Modified Kohonen's self-organizing feature map and its application to automatic sleep cycle recognition

Max Blanchet
, Shuji Yoshizawa
, Shun ichi Amari

The University of Tokyo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In order to increase the adaptive ability of the Kohonen's self-organizing feature map algorithm, we modify the updating rule of its partition boundary. The modified algorithm, together with the Genetic Algorithm, is applied to the detection of the REM (Rapid Eye Movement) cycle of the sleep, which is said to be related to comfortableness of the sleep. We used the heart-rate alone (not relying on complicated EEG analysis) and succeeded to achieve a high detection accuracy.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Publisher	Publ by IEEE
Pages	2476-2479
Number of pages	4
ISBN (Print)	0780314212, 9780780314214
State	Published - 1993
Externally published	Yes
Event	Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Jpn Duration: 25 Oct 1993 → 29 Oct 1993

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	3

Conference

Conference	Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3)
City	Nagoya, Jpn
Period	25/10/93 → 29/10/93

Cite this

@inproceedings{3cb73a2716f34330a3e5b8b205924e29,

title = "Modified Kohonen's self-organizing feature map and its application to automatic sleep cycle recognition",

abstract = "In order to increase the adaptive ability of the Kohonen's self-organizing feature map algorithm, we modify the updating rule of its partition boundary. The modified algorithm, together with the Genetic Algorithm, is applied to the detection of the REM (Rapid Eye Movement) cycle of the sleep, which is said to be related to comfortableness of the sleep. We used the heart-rate alone (not relying on complicated EEG analysis) and succeeded to achieve a high detection accuracy.",

author = "Max Blanchet and Shuji Yoshizawa and Amari, \{Shun ichi\}",

year = "1993",

language = "英語",

isbn = "0780314212",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Publ by IEEE",

pages = "2476--2479",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

note = "Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) ; Conference date: 25-10-1993 Through 29-10-1993",

}

Blanchet, M, Yoshizawa, S & Amari, SI 1993, Modified Kohonen's self-organizing feature map and its application to automatic sleep cycle recognition. in Proceedings of the International Joint Conference on Neural Networks. Proceedings of the International Joint Conference on Neural Networks, vol. 3, Publ by IEEE, pp. 2476-2479, Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3), Nagoya, Jpn, 25/10/93.

Modified Kohonen's self-organizing feature map and its application to automatic sleep cycle recognition. / Blanchet, Max; Yoshizawa, Shuji; Amari, Shun ichi.
Proceedings of the International Joint Conference on Neural Networks. Publ by IEEE, 1993. p. 2476-2479 (Proceedings of the International Joint Conference on Neural Networks; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Modified Kohonen's self-organizing feature map and its application to automatic sleep cycle recognition

AU - Blanchet, Max

AU - Yoshizawa, Shuji

AU - Amari, Shun ichi

PY - 1993

Y1 - 1993

N2 - In order to increase the adaptive ability of the Kohonen's self-organizing feature map algorithm, we modify the updating rule of its partition boundary. The modified algorithm, together with the Genetic Algorithm, is applied to the detection of the REM (Rapid Eye Movement) cycle of the sleep, which is said to be related to comfortableness of the sleep. We used the heart-rate alone (not relying on complicated EEG analysis) and succeeded to achieve a high detection accuracy.

AB - In order to increase the adaptive ability of the Kohonen's self-organizing feature map algorithm, we modify the updating rule of its partition boundary. The modified algorithm, together with the Genetic Algorithm, is applied to the detection of the REM (Rapid Eye Movement) cycle of the sleep, which is said to be related to comfortableness of the sleep. We used the heart-rate alone (not relying on complicated EEG analysis) and succeeded to achieve a high detection accuracy.

UR - https://www.scopus.com/pages/publications/0027812448

M3 - 会議への寄与

AN - SCOPUS:0027812448

SN - 0780314212

SN - 9780780314214

T3 - Proceedings of the International Joint Conference on Neural Networks

SP - 2476

EP - 2479

BT - Proceedings of the International Joint Conference on Neural Networks

PB - Publ by IEEE

T2 - Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3)

Y2 - 25 October 1993 through 29 October 1993

ER -

Modified Kohonen's self-organizing feature map and its application to automatic sleep cycle recognition

Abstract

Publication series

Conference

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