A High Precision Real-time Premature Ventricular Contraction Assessment Method based on the Complex Feature Set.
This paper presents a high precision and low computational complexity premature ventricular contraction (PVC) assessment method for the ECG human-machine interface device. The original signals are preprocessed by integrated filters. Then, R points and surrounding feature points are determined by cor...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 1; pp. 1 - 17 |
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| Autores principales: | , , , , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts tracings Journal Article |
| Publicado: |
Springer Nature
Jan2020
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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=141026225&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141026225 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jan2020 vid: 44 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141026225 141026225 141026225 10.1007/s10916-019-1443-x 141026225 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A High Precision Real-time Premature Ventricular Contraction Assessment Method based on the Complex Feature Set. aug: au: Wang, Haoren Shi, Haotian Chen, Xiaojun Zhao, Liqun Huang, Yixiang Liu, Chengliang affil: School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, People's Republic of China sug: subj: Premature Ventricular Contractions Electrocardiography Methods Signal Processing, Computer Assisted Human Funding Source Algorithms Descriptive Statistics QRS Complex Female Male Adult Middle Age Aged Aged, 80 and Over Arrhythmia Classification User-Computer Interface Individualized Medicine Inpatients Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Female Male ab: This paper presents a high precision and low computational complexity premature ventricular contraction (PVC) assessment method for the ECG human-machine interface device. The original signals are preprocessed by integrated filters. Then, R points and surrounding feature points are determined by corresponding detection algorithms. On this basis, a complex feature set and feature matrices are obtained according to the position feature points. Finally, an exponential Minkowski distance method is proposed for PVC recognition. Both public dataset and clinical experiments were utilized to verify the effectiveness and superiority of the proposed method. The results show that our R peak detection algorithm can substantially reduce the error rate, and obtained 98.97% accuracy for QRS complexes. Meanwhile, the accuracy of PVC recognition was 98.69% for the MIT-BIH database and 98.49% for clinical tests. Moreover, benefiting from the lightweight of our model, it can be easily applied to portable healthcare devices for human-computer interaction. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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