Ventricular fibrillation detection using a leakage/complexity measure method

Moraes, J.C.T.B.; Blechner, M.; Vilani, F.N.; Costa, E.V.

doi:10.1109/cic.2002.1166745

Cited by 17 publications

(8 citation statements)

References 4 publications

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“…For a VF comparison study, studies by Park et al [ 46 ] and Moraes et al [ 47 ] are utilized. A study by Park et al evaluated AHADB and MIT-BIH arrhythmia databases by applying decision rule-based algorithm and utilizing ANSI/AAMI:EC57.…”

Section: Discussionmentioning

confidence: 99%

Arrhythmia Evaluation in Wearable ECG Devices

Sadrawi

Lin²,

Lin³

et al. 2017

Sensors

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This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation.

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Section: Discussionmentioning

confidence: 99%

Arrhythmia Evaluation in Wearable ECG Devices

Sadrawi

Lin²,

Lin³

et al. 2017

Sensors

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“…It was expected that the combination between parameters obtained from the ECG signal and parameters derived from the binary signal would provide shockable rhythm detection improvement, as reported in ref. [13].…”

Section: Methodsmentioning

confidence: 95%

“…Moreover, in ref. [13] it was reported that the combination between the 'leakage' and the 'complexity measure' defined in ref. [7] provided significant improvement of the detection results, which were obtained by means of each of them independently.…”

Section: Methodsmentioning

confidence: 99%

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Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set

Jekova

2007

Biomedical Signal Processing and Control

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“…Therefore, the accurate and quick classification of VT, VF and NSR is a pivotal component in automated external defibrillators (AEDs). intervals (TCI) algorithm characterizes VF, VT and NSR by distributions of TCI [1] ; correlation function based methods differentiate VT and VF by quantifying regularity parameters derived from cross correlation and auto correlation [2][3] ; VF filter techniques calculate VF-filter leakage as discriminator relying on the VF signal approximating a sinusoidal waveform [4] ; spectral and time-frequency algorithms analyze the energy content in various frequency bands due to that the ECG becomes concentrating on a narrow frequency band and has different distribution in time frequency domain during VF [1,[5][6] ; symbolic dynamics approaches analyze VT,VF and NSR by measuring the complexity of ECG signals [7] ; various wavelet based algorithms are also used for this task [8] . Moreover, chaotic features, such as correlation dimension, largest lyapunov exponent, approximate entropy [9] , phase space features [10] , are extracted for classification.…”

Section: Introductionmentioning

confidence: 99%

Ventricular Fibrillation Detection Based on Empirical Mode Decomposition

Bai

Wang

2011

2011 5th International Conference on Bioinformatics and Biomedical Engineering

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Automatic detection of ventricular fibrillation (VF) is of great important for automated external defibrillators (AEDs). However, it is a difficult issue due to the similarity between ventricular fibrillation and ventricular tachycardia (VT). In this paper, a novel scheme based on empirical mode decomposition (EMD) is proposed to disclosure the underlying information of VT, VF and normal electrocardiogram (ECG). The intrinsic mode functions (IMFs), especially the first IMF, may demonstrate distinct properties of different types of ECG signals. Two efficient features derived from IMFs are used for discrimination, namely Frequency Spectrum Entropy (SpEn) and Energy Rate ER IMF . Data from the standard database of MIT-BIH and AHA are used to evaluate the method. With Bayes theory classifier, our method can successfully differentiate VF, VT and normal ECG with the accuracy of 99.78%, 99.78% and 100% respectively. Thus it may provide a new vision for understanding mechanism of cardiac activity and an effective method for VF detection.

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Ventricular fibrillation detection using a leakage/complexity measure method

Cited by 17 publications

References 4 publications

Arrhythmia Evaluation in Wearable ECG Devices

Arrhythmia Evaluation in Wearable ECG Devices

Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set

Ventricular Fibrillation Detection Based on Empirical Mode Decomposition

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