Evidence map›Paper›PMID 37430719›Full record

ReviewSensors (Basel, Switzerland)2023

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review.

Luca Neri, Matt T Oberdier, Kirsten C J van Abeelen, Luca Menghini, Ethan Tumarkin, Hemantkumar Tripathi, Sujai Jaipalli, Alessandro Orro, Nazareno Paolocci, Ilaria Gallelli and 5 more

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

Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 3 pooled it
22.8field-weighted citation impact, top 1% 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

49 citing papers in PubMed, 3 syntheses or guidelines pooled it, 104 citations in OpenAlex.

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  15. Comparing subset selection methods in multi-lead electrocardiogram data.Network modeling and analysis in health informatics and bioinformatics · 2026
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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

15 authors at 5 institutions in 3 countries.

Luca NeriDepartment of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0003-3971-9533
Matt T OberdierDepartment of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA.
Kirsten C J van AbeelenDepartment of Informatics, Systems, and Communication, University of Milano-Bicocca, 20126 Milan, Italy.ORCID 0000-0002-8102-7859
Luca MenghiniDepartment of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy.
Ethan TumarkinDepartment of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA.
Hemantkumar TripathiDepartment of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA.
Sujai JaipalliDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0003-4052-2551
Alessandro OrroInstitute of Biomedical Technologies, National Research Council, 20054 Segrate, Italy.
Nazareno PaolocciDepartment of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA.
Ilaria GallelliDepartment of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy.
Massimo Dall'OlioDepartment of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy.
Amir BekerAccYouRate Group S.p.A., 67100 L'Aquila, Italy.
Richard T CarrickDepartment of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA.
Claudio BorghiDepartment of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy.ORCID 0000-0001-8039-8781
Henry R HalperinDepartment of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA.
Johns Hopkins University · USUniversity of Bologna · ITInstitute of Biomedical Technologies · ITRadboud University Nijmegen · NLUniversity of Trento · IT

Funding

PATHOPHYSIOLOGY OF MYOCARDIAL DISEASEST32HL007227 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Chulan Kwon, WENDY S POST · 1985 to 2026
$21.2M
NHLBI NIH HHS T32 HL007227
6 · The paper itself

Abstract

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

Indexed as

Sleep Apnea SyndromesWearable Electronic DevicesArtificial IntelligenceElectrocardiographyHumansIntelligencedeep learningECGmachine learningm-healthwearable technology

Identifiers

PMID37430719
PMCPMC10223364
OpenAlexW4377041326

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.