CARDIAC DEFIBRILLATION – PREDICTION, PREVENTION AND MANAGEMENT OF CARDIOVASCULAR ARRHYTHMIC EVENTS pptx

CARDIAC DEFIBRILLATION – PREDICTION, PREVENTION AND MANAGEMENT OF CARDIOVASCULAR ARRHYTHMIC EVENTS Edited by Joyelle J. Harris Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular Arrhythmic Events Edited by Joyelle J. Harris Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. 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Used under license from Shutterstock.com First published October, 2011 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular Arrhythmic Events, Edited by Joyelle J. Harris p. cm. ISBN 978-953-307-692-8 free online editions of InTech Books and Journals can be found at www.intechopen.com Contents Preface IX Part 1 Defibrillation Introduction and Assessments 1 Chapter 1 Implantable Cardioverter Defibrillators 3 Behzad Ghanavati Chapter 2 Defibrillation Shock Amplitude, Location and Timing 19 Shimon Rosenheck Chapter 3 Prognostic Significance of Implantable Cardioverter-Defibrillator Shocks 41 Dan Blendea, Razvan Dadu and Craig McPherson Chapter 4 Ventricular Tachyarrhythmias in Implantable Cardioverter Defibrillator Recipients: Differences Between Ischemic and Dilated Cardiomyopathies 53 Aldo Casaleggio, Tiziana Guidotto, Vincenzo Malavasi and Paolo Rossi Part 2 Prediction, Prevention, and Management of Cardiovascular Events 67 Chapter 5 Prediction of Ventricular Arrhythmias in Patients at Risk of Sudden Cardiac Death 69 K.H. Haugaa, J.P. Amlie and T. Edvardsen Chapter 6 Prevention of Sudden Death – Implantable Cardioverter Defibrillator and/or Ventricular Radiofrequency Ablation 83 Andrea Colella, Marzia Giaccardi, Antonella Sabatini, Alfredo Zuppiroli and Gian Franco Gensini Chapter 7 Just in Time Support to Aide Cardio-Pulmonary Resuscitation 105 Frank A. Drews and Paul M. Picciano VI Contents Chapter 8 Electrical Storm in the Era of Implantable Cardioverter Defibrillators 131 David T. Huang and Darren Traub Part 3 Applications and Clinical Relevance 147 Chapter 9 Ventricular Arrhythmias Due to a Transient of Correctable Cause in MADIT-II Patients: Prevalence and Clinical Relevance 149 Michela Casella, Pasquale Santangeli, Ghaliah Al-Mohani, Antonio Dello Russo, Francesco Perna, Stefano Bartoletti, Joseph Gallinghouse, Luigi Di Biase, Andrea Natale and Claudio Tondo Chapter 10 ICDs in Clinical Trials: Assessment of the Effects of Omega-3 Polyunsaturated Fatty Acids from Fish Oils on Ventricular Tachycardia and Ventricular Fibrillation 155 A. Mirrahimi, L. Chiavaroli, K. Srichaikul, J.L. Sievenpiper, C.W.C. Kendall and D.J.A. Jenkins Chapter 11 Application of the Bispectral Index (BIS) During Deep Sedation for Patients with ICD Testing 163 Małgorzata Kuc, Maciej Kempa, Magdalena A.Wujtewicz, Radosław Owczuk and Maria Wujtewicz Preface Throughout the world, approximately 23 million people suffer from heart failure each year. Of all patients who experience heart failure, the mortality rate due to sudden cardiac death (SCD) is between 28% and 68%. SCD is most often due to ventricular tachycardia (VT) or ventricular fibrillation (VF). To alleviate SCD by VT and VF, one effective and common treatment is the use of implanted cardioverter-defibrillators (ICDs). An ICD is a device that monitors the electrical activity of the user’s heart and delivers an electrical shock upon detecting certain arrhythmias. While ICDs have decreased the number of deaths due to sudden cardiac arrhythmic events, studies suggests that patients with ICDs who receive shocks have a worse prognosis than similar patients who do not receive shocks. These poor projections for patients who receive shocks are spurred by several factors. While the electrical shocks from ICDs save lives, they are also reported to be quite painful. Therefore, patients who have experienced a shock tend to suffer from psychological stress and a lower quality of life afterwards. Furthermore, about one third of ICD patients will experience inappropriate or unnecessary shocks from their ICDs. Inappropriate shocks have been linked to higher mortality rates in ICD patients. Other research suggests that after patients receive a shock, heart failure tends to progress more rapidly. Each of these factors highlights a gap in the body of knowledge relevant to ICDs. Therefore, a significant amount of research and clinical studies are needed to guarantee continued improvement for these devices. One topic worthy of further exploration is the manner in which ICDs detect arrhythmias. At present, many ICDs use timing to classify heart rhythms. As a result, some safe rhythms are indistinguishable from life-threatening rhythms that have the same timing. For example, sinus tachycardia (ST) arrhythmia is safe and occurs during exercises in which the heart rate rises to about 120 beats per minute. On the other hand, ventricular tachycardia (VT) arrhythmia is fatal and occurs at the same heart rate. In spite of similar timing, ST and VT have different morphologies, suggesting that additional detection mechanisms could help avoid unnecessary shocks in ICD patients. X Preface Another question to consider is how defibrillators can be employed to protect low risk patients from SCD. Patients who have a high risk of suffering from SCD are most likely identified by healthcare professionals, and then they receive life-saving ICDs. As a result, the majority of SCD victims are now low-risk patients because they remain unidentified and thus unprotected. A suggested answer to this quandary is to increase public access to defibrillators and to improve real-time, automated training on how to use such devices. More research could also be done to determine ways of preventing shocks in ICD patients. For instance, examining the connection between diet, lifestyle, and ventricular arrhythmias could lead to such prevention. Diets rich in fish oils have shown promise in decreasing the risk of arrhythmatic cardiovascular events. However, to date, only a few studies have examined the use of fish oils to prevent ICD discharge. More studies could be done to increase this body of knowledge. The chapters in this book explore the issues posed above in addition to discussing methods for improving ICDs and answering critical questions about ICD technology. The authors examine determinants for successful defibrillation and assess patients who receive defibrillation. Special cases in ICD patients are also considered along with clinical trials involving patients with defibrillators. The chapters presented herein contain a comprehensive overview of prediction, prevention, and management of cardiovascular events. Joyelle J. Harris, Ph.D. Exponent Failure Analysis Associates Phoenix, Arizona USA [...]... decrease battery weight and size and to extend battery life time which required for portable and modern wireless equipment  Hamming network did not need to have a training system and the reference vectors determine the weights Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular Arrhythmic Events 4  Although temperature variation is a major source of drift problems, no... formed by voltage follower Mai, and current sensing transistor Mci Fig 15 2-input max circuit 14 Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular Arrhythmic Events Transistor Mai in an FVF performs as an improved voltage follower and the Gate-Source voltage drop of this transistor is constant (neglecting second-order effect) and independent of the load Transistor Mci operates... amplitude, location and timing may be modified These factors belong to the physical properties of the defibrillators A computerized automatic defibrillator has to be flexible, and capable to deliver the most effective defibrillation shock 20 Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular Arrhythmic Events when needed These 3 properties may be integrated and personalized... defibrillation and to achieve this gradient there was need of higher shock energy (Niemann et al, 2010) After 6 minutes of ventricular 24 Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular Arrhythmic Events fibrillation, the first shock defibrillated the heart in 82% of the cases with 360 Joules biphasic shocks and only in 25% of the cases with 150 Joules biphasic shocks (Walcott... the width of QRS complex This feedback is also enter to controller and processed by fuzzy controller Finally, the output of fuzzy controller goes to VCO circuit and makes the output pulse of VCO to be synchronized with QRS complex (fig.3) 6 Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular Arrhythmic Events Fig 3 QRS detection algorithms that is used in this project... signal of the 1st stage (c) CK1 (d) CK2 (e) CK3 In Fig 8, the input rhythm of ST is applied to the S/H circuit and output of 1st stage is shown 5.2 Mapping circuit Before applying sampled data to Neural Network We have to map them into unit length [-1 1] Figure 9 shows schematic of mapping circuit Fig 9 Mapping circuit 10 Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular Arrhythmic. .. (9) 12 Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular Arrhythmic Events Fig 12 Multiplier as a synapse According to Fig 12, IO1 is the combination of IC1 and IC2 while IO2 is the combination of IC3 and IC4 IO1 and IO2 can be shown as IO 1  k[2c 2  2 a 2 ( I X  IY )2 ] (10) IO 2  k[2c 2  2 a 2 ( I X  IY )2 ] (11) The output current of the four quadrant current... A competitive cell Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular Arrhythmic Events 8 a  comp( W P ) (1) 1 , i  i * ai   0 , i  i * (2) i* is number of the cell that has the highest ni.for our application i=1, 2 In the Hamming Network, reference vector determines the weights (w) of the network.The hamming distance between input vector (P) and reference vector... attempts will be successful Per definition only 50% of the attempts will be defibrillated successfully at the threshold value With a defibrillation energy twice higher than the threshold value the success rate is above 90% 22 Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular Arrhythmic Events Fig 2.2 The desired clinical defibrillation threshold testing The immediately... current of the voltage inverter, which is correspond to minimum current, is positive and all the other currents are negative Thus the digital voltage outputs of the circuit will be at logic Vo1  'one'  Vo2  'zero' Fig.17 shows the block diagram of the proposed QRS classifier Fig 17 Block diagram of the proposed QRS classifier (19) 16 Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular . CARDIAC DEFIBRILLATION – PREDICTION, PREVENTION AND MANAGEMENT OF CARDIOVASCULAR ARRHYTHMIC EVENTS Edited by Joyelle J. Harris Cardiac Defibrillation – Prediction, Prevention. circuit and makes the output pulse of VCO to be synchronized with QRS complex (fig.3). Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular Arrhythmic Events . determine the weights. Cardiac Defibrillation – Prediction, Prevention and Management of Cardiovascular Arrhythmic Events 4  Although temperature variation is a major source of drift problems,