An effective frequency-domain feature of atrial fibrillation based on time-frequency analysis.
Background: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various in...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 20; no. 1 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
11/25/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=147198151&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147198151 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 11/25/2020 vid: 20 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 147198151 147198151 NLM33239025 147198151 10.1186/s12911-020-01337-1 NLM33239025 147198151 ppct: 1 formats: tig: atl: An effective frequency-domain feature of atrial fibrillation based on time-frequency analysis. aug: au: Hu, Yusong Zhao, Yantao Liu, Jihong Pang, Jin Zhang, Chen Li, Peizhe affil: College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China sug: subj: Electrocardiography Decision Trees Atrial Fibrillation Sensitivity and Specificity Resource Databases Algorithms Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Background: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect.Methods: This paper proposed a high distinguishable frequency feature-the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R-R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features-the maximum amplitude in the frequency spectrum and R-R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm.Results: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%.Conclusions: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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