Evidence map›Paper›PMID 36593282›Full record

ReviewPediatric research2023

Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring: Part 1 wearable technology.

Ethan Grooby, Chiranjibi Sitaula, T'ng Chang Kwok, Don Sharkey, Faezeh Marzbanrad, Atul Malhotra

Open access · greenAbstract readReview
PubMed Publisher
In one paragraph

Review in Pediatric research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

9 citing papers in PubMed, 21 citations in OpenAlex.

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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

6 authors at 2 institutions in 2 countries.

Ethan Grooby *Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia.
Chiranjibi Sitaula *Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia.
T'ng Chang KwokCentre for Perinatal Research, School of Medicine, University of Nottingham, Nottingham, UK.
Don SharkeyCentre for Perinatal Research, School of Medicine, University of Nottingham, Nottingham, UK.
Faezeh MarzbanradDepartment of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia.
Atul MalhotraDepartment of Paediatrics, Monash University, Melbourne, VIC, Australia. atul.malhotra@monash.edu.
Monash University · AUUniversity of Nottingham · GB

Funding

Medical Research Council MC_PC_15012
6 · The paper itself

Abstract

With the development of Artificial Intelligence techniques, smart health monitoring is becoming more popular. In this study, we investigate the trend of wearable sensors being adopted and developed in neonatal cardiorespiratory monitoring. We performed a search of papers published from the year 2000 onwards. We then reviewed the advances in sensor technologies and wearable modalities for this application. Common wearable modalities included clothing (39%); chest/abdominal belts (25%); and adhesive patches (15%). Popular singular physiological information from sensors included electrocardiogram (15%), breathing (24%), oxygen saturation and photoplethysmography (13%). Many studies (46%) incorporated a combination of these signals. There has been extensive research in neonatal cardiorespiratory monitoring using both single and multi-parameter systems. Poor data quality is a common issue and further research into combining multi-sensor information to alleviate this should be investigated. IMPACT STATEMENT: State-of-the-art review of sensor technology for wearable neonatal cardiorespiratory monitoring. Review of the designs for wearable neonatal cardiorespiratory monitoring. The use of multi-sensor information to improve physiological data quality has been limited in past research. Several sensor technologies have been implemented and tested on adults that have yet to be explored in the newborn population.

Indexed as

Artificial IntelligenceWearable Electronic DevicesAdultHumansInfant, NewbornMonitoring, PhysiologicRespiration

Identifiers

PMID36593282
OpenAlexW4313454803

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.