The mechanism of defibrillation and cardioversion

Jones, Janice L.; Tovar, Oscar H.

doi:10.1109/5.486742

Cited by 30 publications

(12 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The analysis of defibrillation threshold corresponding to the pulse duration A widely accepted viewpoint [4] about the electric defibrillation is that most of the myocardium cells can be enforced to depolarize and enter into refractory period at the same time when electrical field of the shock interacts with myocardium cells [5] [6] . In this case, any other propagation of the depolarization waves will be terminated and the ventricular fibrillation will stop also.…”

Section: Methodsmentioning

confidence: 99%

Decreasing the Defibrillation Energy by Optimizing the Pulse Duration of Electrical Shock

Song

Wang

Fang

et al. 2008

2008 2nd International Conference on Bioinformatics and Biomedical Engineering

View full text Add to dashboard Cite

Objective: Low energy defibrillation can not only save the power of the device (especially the ICD) but also lighten the damage to the patients. This paper investigates the method to decrease the defibrillation energy by optimizing the pulse duration of electrical shock. Methods: The defibrillation threshold is one of the main parameter to evaluate the defibrillation dosage. In this paper, the defibrillation threshold coresponding to the pulse duration is analysed and studied through 7 animal trails by an appropriative defibrillator designed by ourselves. Results: The defribrillation thresholds are changing along with the changing of the pulse duration and existing optimal pulse duration that coresponding to the lowest defibrillation threshold. Althrough this optimal pulse duration is not so steady under different conditions in our animal trails but it showed a nice possibility to decrease the defibrillation energy by optimizing the pulse duration.

show abstract

Section: Methodsmentioning

confidence: 99%

Decreasing the Defibrillation Energy by Optimizing the Pulse Duration of Electrical Shock

Song

Wang

Fang

et al. 2008

2008 2nd International Conference on Bioinformatics and Biomedical Engineering

View full text Add to dashboard Cite

show abstract

“…91a Experimental and clinical evidence supporting the transition to biphasic waveforms in ICDs was abundant and consistent. [92][93][94][95][96][97][98][99][100][101] The use of biphasic defibrillation waveforms permits a reduction in the size and weight of AEDs, a major consideration in many settings, such as aircraft. Recommendations for specifying algorithm performance and demonstrating the equivalence of alternative waveforms were published by the American Heart Association Subcommittee on AED Safety and Efficacy in 1997.…”

Section: History Of Aedsmentioning

confidence: 99%

Part 4: The Automated External Defibrillator : Key Link in the Chain of Survival

2000

Circulation

View full text Add to dashboard Cite

show abstract

“…Most astonishingly, whether a person suffers from SCD has little to do with their history of heart disease, although most SCD subjects did have previously diagnosed or even undiagnosed cardiac abnormality (Jones and Tovar, 2002). Coronary thrombosis, causing blockages in the walls of blood vessels, is responsible for a significant number of SCD cases.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals

Shi

Geng

et al. 2020

Front. Physiol.

View full text Add to dashboard Cite

Sudden cardiac death (SCD), which can deprive a person of life within minutes, is a destructive heart abnormality. Thus, providing early warning information for patients at risk of SCD, especially those outside hospitals, is essential. In this study, we investigated the performances of ensemble empirical mode decomposition (EEMD)-based entropy features on SCD identification. EEMD-based entropy features were obtained by using the following technology: (1) EEMD was performed on HRV beats to decompose them into intrinsic mode functions (IMFs), (2) five entropy parameters, namely Rényi entropy (RenEn), fuzzy entropy (FuEn), dispersion Entropy (DisEn), improved multiscale permutation entropy (IMPE), and Renyi distribution entropy(RdisEn), were computed from the first four IMFs obtained, which were named EEMD-based entropy features. Additionally, an automated scheme combining EEMD-based entropy and classical linear (time and frequency domains) features was proposed with the intention of detecting SCD early by analyzing 14 min (at seven successive intervals of 2 min) heart rate variability (HRV) in signals from a normal population and subjects at risk of SCD. Firstly, EEMDbased entropy and classical linear measurements were extracted from HRV beats, and then the integrated measurements were ranked by various methodologies, i.e., t-test, entropy, receiver-operating characteristics (ROC), Wilcoxon, and Bhattacharyya. Finally, these ranked features were fed into a k-Nearest Neighbor algorithm for classification. Compared with several state-of-the-art methods, the proposed scheme firstly predicted subjects at risk of SCD up to 14 min earlier with an accuracy of 96.1%, a sensitivity of 97.5%, and a specificity of 94.4% 14 min before SCD onset. The simulation results exhibited that EEMD-based entropy estimators showed significant difference between SCD patients and normal individuals and outperformed the classical linear estimators

show abstract

The mechanism of defibrillation and cardioversion

Cited by 30 publications

References 50 publications

Decreasing the Defibrillation Energy by Optimizing the Pulse Duration of Electrical Shock

Decreasing the Defibrillation Energy by Optimizing the Pulse Duration of Electrical Shock

Part 4: The Automated External Defibrillator : Key Link in the Chain of Survival

Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals

Contact Info

Product

Resources

About