Detection of ventricular fibrillation using empirical mode decomposition and Bayes decision theory

Arafat, Muhammad Abdullah; Sieed, Jubair; Hasan, Md. Kamrul

doi:10.1016/j.compbiomed.2009.08.007

Cited by 35 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Though a significant number of works have been published on this topic, the scope for development of more accurate and reliable techniques relaxing assumptions of certain previous works and incorporating features from diverse nature of the cardiographic signals is yet open. Based on separation capability, the algorithms available in the literature can be classified into categories such as, separating VF from VT [4,7,8], VF from normal sinus rhythm (NSR) [9], VF plus VT from nonVTVF [10], shockable rhythms from other ECG pathologies [5,11,12], VF from nonVF [1-4,13-24]. Comprehensively, the last two categories [25] are the most realistic for fruitful hospital management of cardiac abnormalities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions

2010

View full text Add to dashboard Cite

BackgroundVentricular tachycardia (VT) and ventricular fibrillation (VF) are the most serious cardiac arrhythmias that require quick and accurate detection to save lives. Automated external defibrillators (AEDs) have been developed to recognize these severe cardiac arrhythmias using complex algorithms inside it and determine if an electric shock should in fact be delivered to reset the cardiac rhythm and restore spontaneous circulation. Improving AED safety and efficacy by devising new algorithms which can more accurately distinguish shockable from non-shockable rhythms is a requirement of the present-day because of their uses in public places.MethodIn this paper, we propose a sequential detection algorithm to separate these severe cardiac pathologies from other arrhythmias based on the mean absolute value of the signal, certain low-order intrinsic mode functions (IMFs) of the Empirical Mode Decomposition (EMD) analysis of the signal and a heart rate determination technique. First, we propose a direct waveform quantification based approach to separate VT plus VF from other arrhythmias. The quantification of the electrocardiographic waveforms is made by calculating the mean absolute value of the signal, called the mean signal strength. Then we use the IMFs, which have higher degree of similarity with the VF in comparison to VT, to separate VF from VTVF signals. At the last stage, a simple rate determination technique is used to calculate the heart rate of VT signals and the amplitude of the VF signals is measured to separate the coarse VF from VF. After these three stages of sequential detection procedure, we recognize the two components of shockable rhythms separately.ResultsThe efficacy of the proposed algorithm has been verified and compared with other existing algorithms, e.g., HILB [1], PSR [2], SPEC [3], TCI [4], Count [5], using the MIT-BIH Arrhythmia Database, Creighton University Ventricular Tachyarrhythmia Database and MIT-BIH Malignant Ventricular Arrhythmia Database. Four quality parameters (e.g., sensitivity, specificity, positive predictivity, and accuracy) were calculated to ascertain the quality of the proposed and other comparing algorithms. Comparative results have been presented on the identification of VTVF, VF and shockable rhythms (VF + VT above 180 bpm).ConclusionsThe results show significantly improved performance of the proposed EMD-based novel method as compared to other reported techniques in detecting the life threatening cardiac arrhythmias from a set of large databases.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Because, in real life problems, other types of abnormalities are also present. A recent work is presented in [9] using the EMD technique to separate VF from NSR which shows almost 100% accuracy. But, when other types of pathology except the NSR and VF are present, poor accuracy is obtained.…”

Section: Introductionmentioning

confidence: 99%

Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions

2010

View full text Add to dashboard Cite

show abstract

“…1. Some researchers made a pre-selection of ECG signals by hand [4,5,6,7,8,48,49,9,10]. This resulted in better performance of their algorithms, but the accuracy drastically falls when tested on the entire dataset [13].…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Other algorithms in the literature that use only Signal Processing techniques also show a selectively high but overall poor performance. Arafat et al [9] presented a method based on EMD and Bayes Decision Theory, which shows a sensitivity (Se) of 99.00% , specificity(Sp) of 99.88% and accuracy(Ac) of 99.78%, with only 'VF' and 'NSR' (Normal Sinus Rhythm) signals in the dataset. But the performance falls drastically when other beats and rhythms are considered.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

See 1 more Smart Citation

VFPred: A fusion of signal processing and machine learning techniques in detecting ventricular fibrillation from ECG signals

Ibtehaz

Rahman

2019

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, is responsible for sudden cardiac arrests. Thus, various algorithms have been developed to predict VF from Electrocardiogram (ECG), which is a binary classification problem. In the literature, we find a number of algorithms based on signal processing, where, after some robust mathematical operations the decision is given based on a predefined threshold over a single value. On the other hand, some machine learning based algorithms are also reported in the literature; however, these algorithms merely combine some parameters and make a prediction using those as features. Both the approaches have their perks and pitfalls; thus our motivation was to coalesce them to get the best out of the both worlds. Hence we have developed, VFPred that, in addition to employing a signal processing pipeline, namely, Empirical Mode Decomposition and Discrete Fourier Transform for useful feature extraction, uses a Support Vector Machine for efficient classification. VFPred turns out to be a robust algorithm as it is able to successfully segregate the two classes with equal confidence (Sensitivity = 99.99%, Specificity = 98.40%) even from a short signal of 5 seconds long, whereas existing works though requires longer signals, flourishes in one but fails in the other.

show abstract

A Novel Detection of Ventricular Tachycardia and Fibrillation Based on Degree Centrality of Complex Network

Liu

Meng

Zhang³

et al. 2017

Intelligent Computing Theories and Application

View full text Add to dashboard Cite

Detection of ventricular fibrillation using empirical mode decomposition and Bayes decision theory

Cited by 35 publications

References 17 publications

Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions

Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions

VFPred: A fusion of signal processing and machine learning techniques in detecting ventricular fibrillation from ECG signals

A Novel Detection of Ventricular Tachycardia and Fibrillation Based on Degree Centrality of Complex Network

Contact Info

Product

Resources

About