Automated electrocardiogram signal quality assessment based on Fourier analysis and template matching.
AI
ECG
Electrocardiogram
Fourier
ML
Journal
Journal of clinical monitoring and computing
ISSN: 1573-2614
Titre abrégé: J Clin Monit Comput
Pays: Netherlands
ID NLM: 9806357
Informations de publication
Date de publication:
06 2023
06 2023
Historique:
received:
07
07
2022
accepted:
10
11
2022
medline:
15
5
2023
pubmed:
5
12
2022
entrez:
4
12
2022
Statut:
ppublish
Résumé
We developed and tested a novel template matching approach for signal quality assessment on electrocardiogram (ECG) data. A computational method was developed that uses a sinusoidal approximation to the QRS complex to generate a correlation value at every point of an ECG. The strength of this correlation can be numerically adapted into a 'score' for each segment of an ECG, which can be used to stratify signal quality. The algorithm was tested on lead II ECGs of intensive care unit (ICU) patients admitted to the Mount Sinai Hospital (MSH) from January to July 2020 and on records from the MIT BIH arrhythmia database. The algorithm was found to be 98.9% specific and 99% sensitive on test data from the MSH ICU patients. The routine performs in linear O(n) time and occupies O(1) heap space in runtime. This approach can be used to lower the burden of pre-processing in ECG signal analysis. Given its runtime (O(n)) and memory (O(1)) complexity, there are potential applications for signal quality stratification and arrhythmia detection in wearable devices or smartphones.
Identifiants
pubmed: 36464761
doi: 10.1007/s10877-022-00948-5
pii: 10.1007/s10877-022-00948-5
pmc: PMC9734499
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
829-837Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR004419
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM013766
Pays : United States
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature B.V.
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