JACIII Vol.28 No.2 pp. 444-453
doi: 10.20965/jaciii.2024.p0444

Research Paper:

Non-Contact Sleep Stage Estimation by Updating its Prediction Probabilities According to Ultradian Rhythm

Iko Nakari ORCID Icon and Keiki Takadama ORCID Icon

The University of Electro-Communications
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan

October 18, 2023
November 29, 2023
March 20, 2024
Widrow–Hoff learning rule, sleep stage estimation, ultradian rhythm

To increase an accuracy of the sleep stage estimation without connecting any devices/electrodes to the body, this paper proposes the updating method for its estimation according to an ultradian rhythm as one of the biological rhythms of humans. In the proposed method, the prediction probability of the sleep stage is updated by the Widrow–Hoff learning rule which is generally employed in the update of reinforcement learning. Through the human subject experiment which acquired the biological vibration data from the mattress sensor during sleep, the following implications have been revealed: (1) the accuracy and the quadratic weighted kappa (QWK) of the sleep stage estimation updated by the proposed method are higher than those of random forest (RF) as the conventional method; (2) the multiple update of the probability of the sleep stage according to the ultradian rhythm is significantly important to improve its accuracy and QWK; and (3) compared with RF which over-estimated the NR2 stage while less-estimated the NR1 and NR3 stages, the proposed method contributes to correctly estimating the NR1–3 stages thank to the follow of the ultradian rhythm.

Update estimation with the Monte-Carlo method

Update estimation with the Monte-Carlo method

Cite this article as:
I. Nakari and K. Takadama, “Non-Contact Sleep Stage Estimation by Updating its Prediction Probabilities According to Ultradian Rhythm,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.2, pp. 444-453, 2024.
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Last updated on Jul. 12, 2024