Characterization of Ventricular Arrhythmias in Electrocardiogram Signal Using Semantic Mining Algorithm

Othman, Mohd Afzan; Safri, Norlaili Mat; Sudirman, Rubita

doi:10.1109/ams.2010.68

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Previously, we have proposed a semantic mining technique that can characterize ventricular arrhythmia (V) from normal sinus rhythm (Othman et al, 2010;Othman et al, 2012). However, the previous study only concentrated on discriminating between N and V. Because of this reason, in this study, we expand the technique instead of only characterize V, we try to characterize VT and VF as well.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Ventricular Tachycardia and Fibrillation Using Semantic Mining

Othman¹,

Safri²,

Ghani³

et al. 2012

CIS

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Ventricular tachycardia (VT) and ventricular fibrillation (VF) are potentially life-threatening forms of cardiac arrhythmia. Fast and accurate detection of these conditions can save lives. We used semantic mining to characterize VT and VF episodes by extracting three significant parameters (frequency, damping coefficient and input signal) from electrocardiogram (ECG) signal. This method was used to analyze four-second ECG signals from a widely recognized database at the Massachusetts Institute of Technology (MIT). The method achieved a high sensitivity and specificity of 96.7% and 98.3%, respectively, and was capable of detecting normal sinus rhythm (N) from VT and VF signals without false detection, with a sensitivity of 100%. VT and VF signals were recognized from each other, with a recognition sensitivity of 96% and 94%, respectively. This newly proposed method using semantic mining shows strong potential for clinical applications because it is able to recognize VT and VF signals with higher accuracy and faster recognition times compare to existing methods.

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Section: Introductionmentioning

confidence: 99%

Characterization of Ventricular Tachycardia and Fibrillation Using Semantic Mining

Othman¹,

Safri²,

Ghani³

et al. 2012

CIS

Self Cite

View full text Add to dashboard Cite

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Estimation of arrhythmia episode using variational mode decomposition technique

Maji

Pal

Majumder

2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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Detection of life threatening arrhythmia like ventricular flutter (VFL) or ventricular tachycardia (VT) and atrial flutter (AFL) at the earlier stage may save life by defibrillation therapy. Different type of mechanism has been proposed earlier in time domain or by spectral analysis of ECG signal. In this work a frequency domain approach is proposed by spectral decomposition of ECG signal. Spectral decomposition of signal is done with the help of variational mode decomposition (VMD) technique. VMD model is used to obtain the required number of spectral mode of the test signal and their central mode of oscillation. This central frequency of decomposed signal and maximum phase change within a specified window are used to characterize ventricular tachycardia and atrial flutter and compared to normal rhythms by K-near neighbour (KNN) classification method. Accuracy of 98.6% is obtained for VT classification. The proposed method eliminates the requirement of detecting fiducial points of ECG signal as necessary in conventional classification methods.

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