ECG signal analysis based on variational mode decomposition and bandwidth property

Mert, Ahmet

doi:10.1109/siu.2016.7495962

Cited by 4 publications

(2 citation statements)

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“…(One exception is asystole, which has no R peaks, since the ECG is nearly a flat line.) More recent studies have focused on classification of various arrhythmia patterns [ 6 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. The extracted features for pattern classification have included: (1) time domain or morphology-based features [ 15 , 16 , 17 ], such as QRS duration and amplitude, P wave duration and amplitude, T wave amplitude, ST segment duration, or QT segment duration; (2) frequency domain features [ 6 , 18 ]; (3) nonlinearity features [ 19 , 20 , 21 , 22 , 23 ]; and (4) rhythm-based features [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, some of the issues with WT are the choice of the proper mother wavelet function, appropriate selection of the order of the filter, and the level of signal decomposition. One noteworthy recent approach is variational mode decomposition (VMD), which is based on the modification of the empirical mode decomposition method [ 13 ]. The VMD method was shown to provide good classification results, including differentiation of normal (N), premature ventricular contraction (PVC), left-bundle branch block (LBBB), right-bundle branch block (RBBB), premature beats (PB), and atrial premature contraction (APC) beats.…”

Section: Introductionmentioning

confidence: 99%

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Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals

Hajeb-Mohammadalipour

Ahmadi

Shahghadami

et al. 2018

Sensors

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We developed an automated approach to differentiate between different types of arrhythmic episodes in electrocardiogram (ECG) signals, because, in real-life scenarios, a software application does not know in advance the type of arrhythmia a patient experiences. Our approach has four main stages: (1) Classification of ventricular fibrillation (VF) versus non-VF segments—including atrial fibrillation (AF), ventricular tachycardia (VT), normal sinus rhythm (NSR), and sinus arrhythmias, such as bigeminy, trigeminy, quadrigeminy, couplet, triplet—using four image-based phase plot features, one frequency domain feature, and the Shannon entropy index. (2) Classification of AF versus non-AF segments. (3) Premature ventricular contraction (PVC) detection on every non-AF segment, using a time domain feature, a frequency domain feature, and two features that characterize the nonlinearity of the data. (4) Determination of the PVC patterns, if present, to categorize distinct types of sinus arrhythmias and NSR. We used the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, Creighton University’s VT arrhythmia database, the MIT-BIH atrial fibrillation database, and the MIT-BIH malignant ventricular arrhythmia database to test our algorithm. Binary decision tree (BDT) and support vector machine (SVM) classifiers were used in both stage 1 and stage 3. We also compared our proposed algorithm’s performance to other published algorithms. Our VF detection algorithm was accurate, as in balanced datasets (and unbalanced, in parentheses) it provided an accuracy of 95.1% (97.1%), sensitivity of 94.5% (91.1%), and specificity of 94.2% (98.2%). The AF detection was accurate, as the sensitivity and specificity in balanced datasets (and unbalanced, in parentheses) were found to be 97.8% (98.6%) and 97.21% (97.1%), respectively. Our PVC detection algorithm was also robust, as the accuracy, sensitivity, and specificity were found to be 99% (98.1%), 98.0% (96.2%), and 98.4% (99.4%), respectively, for balanced and (unbalanced) datasets.

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