A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment
Abstract
Electrocardiogram (ECG) signal quality assessment (SQA) plays a vital role in significantly improving the diagnostic accuracy and reliability of unsupervised ECG analysis systems. In practice, the ECG signal is often corrupted with different kinds of noises and artifacts. Therefore, numerous SQA methods were presented based on the ECG signal and/or noise features and the machine learning classifiers and/or heuristic decision rules. This paper presents an overview of current state-of-the-art SQA methods and highlights the practical limitations of the existing SQA methods. Based upon past and our studies, it is noticed that a lightweight ECG noise analysis framework is highly demanded for real-time detection, localization, and classification of single and combined ECG noises within the context of wearable ECG monitoring devices which are often resource constrained.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Several sources of noise exist in wearable data, arising from factors such as poor electrode contact with the skin, movement and muscle contraction during the ECG, and external electrical interference.
“Indeed, several sources of noise exist in wearable data, arising from factors such as poor electrode contact with the skin, movement and muscle contraction during the ECG, and external electrical interference16–19.”
From Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices (2023) · cites this paper as [16]
Cited by
Sorted by citations
Wearable Devices for Ambulatory Cardiac Monitoring
Sana, F., Isselbacher, E. M., Singh, J. P., Heist, E. K., Pathik, B., & Armoundas, A. A. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.01.046 · Cited by 362 · see citing works
Khunte, A., Sangha, V., Oikonomou, E. K., Dhingra, L. S., Aminorroaya, A., Mortazavi, B. J., Coppi, A., Brandt, C. A., Krumholz, H. M., & Khera, R. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00869-w · Cited by 95 · see citing works
- Authors
- Satija, U., Ramkumar, B., & Manikandan, M. S.
- Affiliations
- Indian Institute of Technology Bhubaneswar (IN)
- Venue
- IEEE Reviews in Biomedical Engineering
- Published
- 2018
- Volume
- 11
- Pages
- 36-52
- ISSN
- 1937-3333 (ISSN-L)
- 1941-1189
- OpenAlex
- W2791450091
- Cited by
- 295
- References
- 0
- Citations by year
- 2026: 27 · 2025: 47 · 2024: 41 · 2023: 51 · 2022: 52 · 2021: 34 · 2020: 26 · 2019: 13 · 2018: 4
- FWCI
- 3.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Reviews in Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Keywords
- electrocardiogram, ECG signal quality assessment, ECG noise, ECG artifacts, noise detection, noise localization, noise classification, wearable ECG monitoring, resource-constrained devices, real-time signal processing
- MeSH terms
- Algorithms, Arrhythmias, Cardiac, Electrocardiography, Humans, Signal Processing, Computer-Assisted, Reproducibility of Results
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
