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Tokyo, December 26, 2021 /PRNewswire/ – The University of Electronic Communications released December 2021 The problem is UEC Electronic Bulletin
December 2021 The problem is UEC Electronic Bulletin
http://www.ru.uec.ac.jp/e-bulletin/
This December 2021 UEC e-Bulletin issues include a video introduction by UEC associate professor Nakashiki Toru Describe his recent research on “Speech Signal Processing Based on Shallow Neural Networks”.
Research highlights are “Frequency analysis helps to understand sleep disorders”, Keiki Takadama; and “Educational measurement/modeling performance evaluation”, Uto Masaki.
The topic column is right Watanabe Eriko, Associate Professor in the Department of Engineering Science, providing insights into the “charm of digital holograms and their application in imaging through translucent materials”.
Research highlights
Sleep Science: Frequency analysis helps to understand sleep disorders
http://www.ru.uec.ac.jp/e-bulletin/research-highlights/202112/a.html
Sleep apnea syndrome (SAS) is a sleep disorder characterized by apneas (apneas) during sleep. This pause usually lasts more than 10 seconds and is often accompanied by loud snoring. The brain interprets each apnea as a danger—because of a reduced oxygen supply—and lighter sleep. As a result, people with SAS accumulate sleep debt, which in turn may lead to mental health problems such as depression or dementia. In order to avoid medical complications, early detection of SAS is essential. The so-called non-contact detection method is based on monitoring chest movement, for example, through a sensor connected to a mattress sensor on which a person is sleeping; from the recorded biological vibration data, the respiratory frequency and amplitude can be derived. This method is not always effective. For example, when a person’s breathing is “forced” (breathing is accompanied by chest and abdomen movements, which is actually a symptom of SAS), it is difficult to detect sleep apnea.
The researchers analyzed biological vibration data recorded from 9 SAS patients and 9 healthy individuals, which were obtained by mattress sensors. They not only considered breathing (between 0.1 Hz and 0.2 Hz) and heartbeat (between 0.6 Hz and 1.5 Hz) frequencies, but also considered frequencies up to 8 Hz, and studied the distribution of frequencies—the frequency spectrum. When comparing spectrums, Nakari and Takadama noticed a slight increase in the frequency density of SAS patients around 3 Hz. On the logarithmic graph of the frequency spectrum, this increase appears to be convex. Based on this observation, the researchers defined a quantity called Logarithmic Spectral Convexity (DCLS).
It is worth noting that the average DCLS value of SAS patients (≈ 99 ± 10) is completely different from the average value of healthy subjects (≈ 48 ± 7). Therefore, the DCLS value may be used as an indicator of SAS-it can be obtained only by sleeping on the mattress sensor.
Further analysis shows that the increased frequency density around 3 Hz corresponds to the cumulative density in the so-called WAKE phase (the first of the six levels used to characterize “sleep depth”). Therefore, the WAKE phase of SAS patients and people without sleep apnea may be different. More importantly, the researchers believe that SAS subjects produce 3 Hz waves during the WAKE phase, and believe that this may actually be a symptom unknown to SAS so far, except for the apnea itself. However, as Nakari and Takadama pointed out, future work “should clarify the phenomenon around 3 Hz”.
refer to
Nakariyoko And Keiki Takadama, Sleep Apnea Syndrome Detection Based on Logarithmic Spectral Convexity Calculated by Mattress Sensor Overnight Biological Vibration Data, pp. 2274-2277, (2021).
The 43rd IEEE International Conference of Medical and Biological Engineering Society (EMBC2021) (2021).
URL: https://embc.embs.org/2021/
Educational Measurement: Modeling Performance Evaluation
http://www.ru.uec.ac.jp/e-bulletin/research-highlights/202112/b.html
Performance evaluations of actual tasks performed by candidates are usually scored by human evaluators for different parts of the task. Usually, the so-called scoring criteria are used for…
The full story can be found on Benzinga.com
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