Researchers at the University of Tsukuba develop a machine-learning algorithm for automatically classifying the sleep stages of lab mice. Combining two techniques, they achieve 96.6% accuracy, which may help accelerate sleep research
Tsukuba, Japan – Researchers at the University of Tsukuba have created a new artificial intelligence program for automatically classifying the sleep stages of mice that combines two popular machine learning methods. Dubbed “MC-SleepNet,” the algorithm achieved accuracy rates exceeding 96% and high robustness against noise in the biological signals. The use of this system for automatically annotating data can significantly assist sleep researchers when analyzing the results of their experiments.
Scientists who study sleep often use mice as animal models to better understand the ways the activity in the brain changes during the various phases. These phases can be classified as awake, REM (rapid eye movement) sleep, and non-REM sleep. Previously, researchers who monitored the brainwaves of sleeping mice ended up with mountains of data that needed to be laboriously labeled by hand, often by teams of students. This represented a major bottleneck in the research.
Now, researchers at the University of Tsukuba have introduced a program for automatically classifying the stage of sleep that a mouse experienced based on its electroencephalogram (EEG) and electromyogram (EMG) signals, which record electrical activity in the brain and body, respectively. They combined two machine learning techniques, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent neural networks, to achieve accuracies that surpass those of the best existing automatic methods.
“Machine learning is an exciting new field of research with important applications that combine medicine with computer science. It allows us to automatically classify new data based on labeled examples,” corresponding author Kazumasa Horie explains. This is especially valuable when the patterns to look for are not well known, as with sleep stages. In this way, the algorithm can ‘learn” how to make complex decisions without being explicitly programed. In this project, the accuracy was very high because of the large dataset used. With over 4,200 biological signals, it was the biggest dataset of any sleep research so far. Also, by implementing a CNN, the algorithm showed high robustness against individual differences and noise.
The main advance in this work was to divide the task between the two machine learning methods. First a CNN was used to extract features of interest from the recordings of the electrical activity in the brain and body. These data were then passed to an LSTM to determine which features were most indicative of the sleep phase the mouse was experiencing. “We are optimistic that we can translate this work into classifying sleep stages in humans”, senior author Hiroyuki Kitagawa says. In the meantime, this program can already speed up the work of researchers in the field of sleep, which may lead to a much clearer understanding of how sleep operates.
Learn more: Machine Learning That Works Like a Dream
Go deeper with Bing News on:
Sleep research
- Is It Bad to Sleep With Wet Hair?
Going to bed with wet hair is probably not the end of the world if you do it occasionally. Learn about hair and scalp risks from sleeping on wet hair.
- Amazon, Target stop selling weighted baby blankets, sleep sacks and swaddles amid safety concerns
Amazon and Target are among retailers now ending their sales of weighted baby blankets, sleep sacks and swaddles as calls mount for more research and oversight of the products’ safety, according to ...
- There’s nothing woke about ruling against sleep deprivation in prison
Prisons play a vital role in our system of justice, but the punishment they deliver should be limited to loss of liberty, not loss of humanity.
- ‘Sleep disorder drove my son to suicide,’ New York mother says: ‘Broke my heart’
Derek McFadden was 23 when he took his own life on August 17, 2018, in Tucson, Arizona. His mother, Robin McFadden, said she believes that her son’s insomnia was the “driver" of his suicide.
- How America Lost Sleep
Many Americans are reporting that they’d feel better if they slept more, but finding the right remedy isn’t always simple.
Go deeper with Google Headlines on:
Sleep research
[google_news title=”” keyword=”sleep research” num_posts=”5″ blurb_length=”0″ show_thumb=”left”]
Go deeper with Bing News on:
Sleep research and machine learning
- Machine learning-powered robot streamlines genetic research process
University of Minnesota Twin Cities researchers have constructed a robot that uses machine learning to fully automate a complicated microinjection process used in genetic research.
- Artificial intelligence, machine learning boost animal science research
Science X is a network of high quality websites with most complete and comprehensive daily coverage of the full sweep of science, technology, and medicine news ...
- Many men have trouble falling asleep, but exercising can help
People who work out twice each week are less likely to experience insomnia symptoms and more likely to get a good night's sleep, a new study shows.
- Studying spaceflight atrophy with machine learning
Even intense exercise by astronauts cannot compensate for muscle atrophy caused by microgravity. Atrophy occurs, in part, by way of an underlying mechanism that regulates calcium uptake.
- Decoding spontaneous thoughts from the brain via machine learning
A team of researchers led by Kim Hong Ji and Woo Choong-Wan at the Center for Neuroscience Imaging Research (CNIR) within the Institute for Basic Science (IBS), in collaboration with Emily FINN at ...
Go deeper with Google Headlines on:
Sleep research and machine learning
[google_news title=”” keyword=”sleep research and machine learning” num_posts=”5″ blurb_length=”0″ show_thumb=”left”]