Evidence map›Paper›PMID 32213969›Full record

ReviewSensors (Basel, Switzerland)2020

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges.

Mohamed Adel Serhani, Hadeel T El Kassabi, Heba Ismail, Alramzana Nujum Navaz

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing papers in PubMed, 1 pooled it
–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

77 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Observational
  6. Review
  7. Article
  8. Sparse Temporal AutoEncoder for ECG Anomaly Detection.Sensors (Basel, Switzerland) · 2026
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Article

17 more citing papers are in PubMed but not listed here.

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.

Mohamed Adel SerhaniDepartment of Information Systems and Security, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates.ORCID 0000-0001-7001-3710
Hadeel T El KassabiDepartment of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates.
Heba IsmailDepartment of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates.ORCID 0000-0002-7891-9872
Alramzana Nujum NavazDepartment of Information Systems and Security, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates.ORCID 0000-0003-2787-523X

Funding

United Arab Emirates University 31R227
6 · The paper itself

Abstract

Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems' components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems' value chain is conducted, and a thorough review of the relevant literature, classified against the experts' taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

Indexed as

ElectrocardiographyHome Care ServicesHospitalsHumansMonitoring, PhysiologicSignal Processing, Computer-Assistedcardiovascular diseasesECGECG monitoring systemheart diseasesIoTsensorssmart monitoring

Identifiers

PMID32213969
PMCPMC7147367

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.