Classification of 16 adult sleep patterns based on large-scale sleep
analysis
Expectations for application in sleep health checkups and sleep medicine


Date:
March 31, 2022
Source:
Japan Science and Technology Agency
Summary:
A research group found that the human sleep patterns could be
classified into 16 types by combining ACCEL, their original machine
learning algorithm for sleep-wake classification, the dimension
reduction method and the clustering method. The acceleration data
of approximately 100,000 people in the UK Biobank were analyzed
in detail, and some life-style- related patterns and insomnia-like
patterns were reported.



FULL STORY ==========================================================================
In recent years, the number of people worldwide who are dissatisfied or
anxious about their sleep has been increasing due to the diversification
of lifestyles.

Simple sleep measurement and quantitative understanding of individual
sleep patterns are very important not only in the field of healthcare
but also from the medical perspective, such as in the diagnosis of
sleep disorders.


==========================================================================
A research group of The University of Tokyo led by Professor Hiroki Ueda
(also a Riken team leader) and Machiko Katori, and Assistant Professor
Shoi Shi (RIKEN) used ACCEL(1), an original machine learning algorithm developed by their research laboratory, to determine sleep and waking
states based on arm acceleration and converted the acceleration data
of approximately 100,000 people in the UK Biobank(2) into sleep data,
which was then analyzed in detail.

They found that the sleep patterns of these 100,000 people could be
classified into 16 different types.

The research group first focused on the arm acceleration data of
approximately 100,000 people in the UK Biobank. This data was obtained
from men and women in their 30s to 60s, mainly in the UK, who were
measured for up to 7 days using wristband-type accelerometers. Using an algorithm (ACCEL) they had developed in 2022, the research group generated sleep data(3) for approximately 100,000 people from the acceleration
data. The obtained sleep data were converted into 21 sleep indicators,
and then, using dimension reduction(4) and clustering(5) methods,
the sleep patterns were classified into 8 different clusters. These
included clusters related to "social jet lag" and clusters characterized
by mid-onset awakenings and considered insomnia, enabling the extraction
of clusters related to lifestyles and to sleep disorders. Next, in order
to examine sleep patterns associated with sleep disorders in more detail,
the research group focused on 6 of the 21 sleep indicators, including
sleep duration and intermediate waking time, which are known to be
closely related to sleep disorders. By applying the same analysis to
data where one indicator deviated significantly from general sleep (data
in the upper 2.28th percentile or higher or the lower 2.28th percentile
or lower(6)in the overall distribution), they were able to classify the
data into 8 clusters. These included clusters related to morning-types
and evening-types. They also identified several clusters associated
with insomnia, and were able, along with the clustering using the entire dataset, to classify 7 types of sleep patterns associated with insomnia.

Thus, by analyzing sleep on a large scale, they have revealed the
landscape of human sleep phenotype. This study has made it possible to quantitatively classify clusters related to lifestyle such as "social
jet lag" and morning/ evening types, which are usually difficult to
determine with short-term PSG measurements(7), In addition, detailed
analysis of outlier and classification of sleep patterns revealed 7
clusters related to insomnia. These clusters are classified based on
new indicators differing from conventional methods, and are expected
to be useful in the construction of new methods in terms of diagnosing
insomnia and proposing treatment methods.

These results were obtained through the "Ueda Biological Timing
Project," ERATO Program funded by the Japan Science and Technology Agency (JST). In this project, JST develops "systems biology that contributes to understanding human beings," using sleep-wake rhythms as a model system,
and aims to understand in human sleep-wake behavior the "biological time" information that extends from molecules to individual humans living
in society.

Notes: (1) ACCEL : An original sleep determination algorithm developed
by the research team. For details, refer to the following paper. "A
jerk-based algorithm ACCEL for the accurate classification of sleep-wake
states from arm acceleration" DOI: 10.1016/j.isci.2021.103727


==========================================================================
(2) UK Biobank: A large research database containing genetic and health information on approximately 500,000 British participants. This study
uses acceleration data for approximately 100,000 people as well as the
linked gender and age data.

(3) Sleep data: Time-series data with intervals of 30 seconds labeled
as sleeping or waking. PSG measurement uses diverse data measured by
specialist technicians to create sleep data. In this study, sleep data
was obtained by applying ACCEL to accelerometers.

(4) Dimension reduction method: A method to reduce the number of
dimensions of data. This makes it possible to extract important
information from the data and to capture the characteristics of the
data. In this study, UMAP (Uniform Manifold Approximation and Projection)
is used.

(5) Clustering method: A method of classifying data into clusters
based on similarities among the data. There are two types of clustering methods: supervised clustering, which uses correct data for clustering,
and unsupervised clustering, which does not. In this study, the
unsupervised clustering method, DBSCAN (Density-Based Spatial Clustering
of Applications with Noise) is used.

(6) Upper and lower percentiles:?The value in any given percent
when the values are arranged in descending order is called the upper percentile. Conversely, the value in any given percent when the values
are listed in ascending order is called the lower percentile. For
instance, data above the upper 2.28th percentile or below the lower
2.28th percentile in normal distribution refers to data deviating from
the mean by more than twice the standard deviation (2SD).

(7) Polysomnography (PSG): In PSG measurements, multiple electrodes
and sensors are attached to the examinee to measure brain waves, eye
movements, respiratory status, and electrocardiogram status. It is
currently the most accurate measurement method used to determine human
sleep patterns. It is also used to diagnose sleep disorders.


========================================================================== Story Source: Materials provided by
Japan_Science_and_Technology_Agency. Note: Content may be edited for
style and length.


========================================================================== Journal Reference:
1. Machiko Katori, Shoi Shi, Koji L. Ode, Yasuhiro Tomita, Hiroki
R. Ueda.

The 103,200-arm acceleration dataset in the UK Biobank revealed a
landscape of human sleep phenotypes. Proceedings of the National
Academy of Sciences, 2022; 119 (12) DOI: 10.1073/pnas.2116729119 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220331101543.htm

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