Evidence map›Paper›PMID 36229851›Full record

ArticleBiomedical engineering online2022

Sleep postures monitoring based on capacitively coupled electrodes and deep recurrent neural networks.

Shun Peng, Yang Li, Rui Cui, Ke Xu, Yonglin Wu, Ming Huang, Chenyun Dai, Toshiyo Tamur, Subhas Mukhopadhyay, Chen Chen and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical engineering online, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 49% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 5 institutions in 4 countries.

Shun Peng *Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, 200433, China.
Yang Li *Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, 200433, China.
Rui CuiCenter for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, 200433, China.
Ke XuDepartment of Infectious Disease, Imperial College London, London, UK.
Yonglin WuCenter for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, 200433, China.
Ming HuangComputational Systems Biology, Division of Information Science, Nara Institute of Science and Technology, Nara, Japan.
Chenyun DaiCenter for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, 200433, China.
Toshiyo TamurInstitute for Healthcare Robotics, Waseda University, Tokyo, 162-0041, Japan.
Subhas MukhopadhyaySchool of Engineering, Macquarie University, Sydney, NSW, 2109, Australia.
Chen ChenHuman Phenome Institute, Fudan University, Shanghai, 201203, China.
Wei ChenCenter for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, 200433, China. w_chen@fudan.edu.cn.
Fudan University · CNImperial College London · GBMacquarie University · AUNara Institute of Science and Technology · JPWaseda University · JP

Funding

National Natural Science Foundation of China 62001118Shanghai Committee of Science and Technology 20S31903900Shanghai Municipal Science and Technology International R&D Collaboration Project 20510710500
6 · The paper itself

Abstract

backgroundCapacitively coupled electrode (CC electrode), as a non-contact and unobtrusive technology for measuring physiological signals, has been widely applied in sleep monitoring scenarios. The most common implementation is capacitive electrocardiogram (cECG) that could provide useful clinical information for assessing cardiac function and detecting cardiovascular diseases. In the current study, we sought to explore another potential application of cECG in sleep monitoring, i.e., sleep postures recognition.

methodsTwo sets of experiments, the short-term experiment, and the overnight experiment, were conducted. The cECG signals were measured by a smart mattress based on flexible CC electrodes and sleep postures were recorded simultaneously. Then, a classifier model based on a deep recurrent neural network (RNN) was proposed to distinguish sleep postures (supine, left lateral and right lateral). To verify the reliability of the proposed model, leave-one-subject-out cross-validation was introduced.

resultsIn the short-term experiment, the overall accuracy of 96.2% was achieved based on 30-s segment, while the overall accuracy was 88.8% using one heart beat segment. For the unconstrained overnight experiment, the accuracy of 91.0% was achieved based on 30-s segment, while the accuracy was 81.4% using one heart beat segment.

conclusionsThe results suggest that cECG could render valuable information about sleep postures detection and potentially be helpful for sleep disorder diagnosis.

Indexed as

PostureSleepElectrocardiographyElectrodesNeural Networks, ComputerReproducibility of ResultsCapacitive electrocardiogramCapacitively coupled electrodeRecurrent neural networkSleep posture

Identifiers

PMID36229851
PMCPMC9563454
OpenAlexW4305082176

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.