Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features.
The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 6; pp. 1 - 10 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts tracings Journal Article |
| Publicado: |
Springer Nature
Jun2016
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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=115925339&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925339 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jun2016 vid: 40 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925339 115925339 115925339 10.1007/s10916-016-0505-6 115925339 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features. aug: au: Tripathy, R. Dandapat, S. affil: Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039 India sug: subj: Electrocardiography Automation Cardiovascular Diseases Diagnosis Human One-Way Analysis of Variance Myocardial Infarction ST Segment P-Value ab: The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG. pubtype: Academic Journal doctype: equations & formulas research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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