Ventricular Fibrillation Detection Based on Empirical Mode Decomposition

Bai, Baodan; Wang, Yuanyuan

doi:10.1109/icbbe.2011.5780451

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…During the long-term analysis, a high classification accuracy of 88.5% was achieved for VT vs. VF classification. This was in alignment with previous works that attempted to solve the same problem [8,15]. Unlike previous approaches that utilized decomposition techniques predicated on the choice of a pre-defined basis function, the EMD algorithm decomposes the data into IMFs based on the inherent properties of the data itself.…”

Section: Discussionsupporting

confidence: 62%

“…Bai et al also performed classification of NSR, VT, and VF using and EMD based approach [15]. They were also able to achieve a high classification accuracy for discriminating those 3 rhythms.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, Tripathy et al utilized variational mode decomposition to analyze VAs and classify them as shockable and non-shockable rhythms. Empirical mode decomposition (EMD) has also been used to classify VAs in various applications [14,15]. Another use of EMD was to construct the Hilbert energy spectrum (HS) from its intrinsic mode functions (IMF).…”

Section: Existing Workmentioning

confidence: 99%

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Classification of Ventricular Arrhythmias Based on a Data Driven Approach

Hotradat¹

2021

Preprint

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Ventricular arrhythmias (VA) are dangerous pathophysiological conditions affecting the heart which evolve over time resulting in different manifestations such as ventricular tachycardia (VT), organized VF (OVF), and disorganized VF (DVF). Success of resuscitation for patients is greatly impacted by the type of VA and swift administration of appropriate therapy options. This thesis attempts to arrive at computationally efficient, data driven approaches for classifying and tracking VAs over time for two purposes: (1) ‘in-hospital’ scenarios for planning long-term therapy options, and (2) ‘out-of-hospital’ scenarios for tracking progression/segregation of VAs in near real-time. Using a database of 61 60-s ECG VA segments, maximum classification accuracies of 96.7% (AUC=0.993) and 87% (AUC=0.968) were achieved for VT vs. VF and OVF vs. DVF classification for ‘in-hospital’/offline analysis. Two near real-time approaches were also developed for ‘out-of-hospital’ VA incidents with results demonstrating the high potential to track VA progression and segregation over time.

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Section: Discussionsupporting

confidence: 62%

Section: Discussionmentioning

confidence: 99%

Section: Existing Workmentioning

confidence: 99%

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Classification of Ventricular Arrhythmias Based on a Data Driven Approach

Hotradat¹

2021

Preprint

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“…Elias Ebrahimzadeh et.al [4] had shown that better accuracy was obtained by using both the time-frequency and nonlinear features. Hence in this paper, two non-linear features are chosen, namely 'Spectral Entropy' [17] and 'Lyapunov Exponent' [18] to extract the most distinctive properties of the signal. A positive lyapunov exponent points that the system could be chaotic [8].…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

Application of Empirical Mode Decomposition And Support Vector Machine Classifiers for Prediction of Sudden Cardiac Arrest in Susceptible Cases

D.¹,

Venkatesh²

2016

IJERT

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One of the primary causes of Sudden CardiacArrest (SCA) is Ventricular Fibrillation (V.F.). It is a serious arrhythmic condition wherein the heart beats chaotically rather than pumping blood. It is essential for the subject to get instant medical attention when ventricular fibrillation occurs. In this paper, a novel approach is presented where the Hilbert Huang Transform is used for prediction of such an event. The ECG signals are pre-processed for removal of noises, artifacts. The empirical mode decomposition (EMD), which is a part of Hilbert Huang Transform is applied to the de-noised ECG signal. Three features were extracted from the first IMF (Intrinsic Mode Function) obtained after EMD, viz; spectral entropy, lyapunov exponent and weighted frequency. Support Vector Machine (SVM) classifier was used to classify and predict the normal people and patients prone to sudden cardiac arrest, based on the extracted features from both the cases. By applying this technique, the results show that average classification accuracy of 95% was achieved in predicting an unknown signal, labelled as 'Normal' or 'Abnormal ECG signal'. A second SVM classifier was used for predicting the occurrence of V.F. in ECG signals prone to cardiac arrest within 15 minutes, which resulted in an accuracy of 55.5 %.

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“…The standard deviations of the peak amplitudes and peak distances in a cross correlation between a window of ECG and a short window immediately prior is used to classify VT from VF in [9]. An algorithm for VF detection based on empirical mode decomposition (EMD), which creates a lower dimensional representation of ECG waveforms based on the energy in the first EMD component and its spectral entropy, was proposed in [10]. Counting the time between ECG turning points was proposed [11] to differentiate fast-VT, slow-VT and VF for use in automatic implantable cardiovertor defibrillators (AICDs).…”

Section: Introductionmentioning

confidence: 99%

Classification of Human Ventricular Arrhythmia in High Dimensional Representation Spaces

Alwan,

Cvetkovic,

Curtis

2013

Preprint

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We studied classification of human ECGs labelled as normal sinus rhythm, ventricular fibrillation and ventricular tachycardia by means of support vector machines in different representation spaces, using different observation lengths. ECG waveform segments of duration 0.5-4 s, their Fourier magnitude spectra, and lower dimensional projections of Fourier magnitude spectra were used for classification. All considered representations were of much higher dimension than in published studies. Classification accuracy improved with segment duration up to 2 s, with 4 s providing little improvement.We found that it is possible to discriminate between ventricular tachycardia and ventricular fibrillation by the present approach with much shorter runs of ECG (2 s, minimum 86% sensitivity per class) than previously imagined. Ensembles of classifiers acting on 1 s segments taken over 5 s observation windows gave best results, with sensitivities of detection for all classes exceeding 93%.

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Ventricular Fibrillation Detection Based on Empirical Mode Decomposition

Cited by 4 publications

References 13 publications

Classification of Ventricular Arrhythmias Based on a Data Driven Approach

Classification of Ventricular Arrhythmias Based on a Data Driven Approach

Application of Empirical Mode Decomposition And Support Vector Machine Classifiers for Prediction of Sudden Cardiac Arrest in Susceptible Cases

Classification of Human Ventricular Arrhythmia in High Dimensional Representation Spaces

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