Evidence map›Paper›PMID 33624603›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2021

[A new lossy compression method for fetal heart rate signals-Convolutional Codec Network].

Y Que, D Chen, L Tong, C Chen

Abstract read
In one paragraph

Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2021. 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
–field-weighted citation impact
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.

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

4 authors.

Y QueCollege of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
D ChenCollege of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
L TongGuangdong Vocational College of Mechanical and Electrical Technology, Guangzhou 510550, China.
C ChenCollege of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to reduce the energy loss during data transmission and storage in the Internet of Things system and improve the transmission efficiency of fetal heart rate data to allow real-time monitoring of the fetus, we used a convolutional codec network (CC-Net) to compress the data. The network has two modules: the encoding and decoding modules. The original data are compressed in the encoding module and reconstructed in the decoding module. The internal parameters are continuously updated using the mean square error of the original and the reconstructed signals to minimize the error to obtain effectively compressed data in the encoding module. In this study, the compression ratio of fetal heart rate signals using this method reached 12.07%, and the error between the reconstructed and original signals was 0.03. The proposed CC-Net can achieve a very low compression ratio for fetal heart rate compression while ensuring a high similarity between the reconstructed and the original signals to retain important information in fetal heart rate signals.

Indexed as

Data CompressionHeart Rate, FetalAlgorithmsFemaleHumansPregnancySignal Processing, Computer-Assistedconvolutional codec networkdata compressionfetal heart rateInternet of Thingsmaternal and infant monitoring system

Identifiers

PMID33624603
PMCPMC7905255

What OpenQuestion holds

Textmetadata
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.