437 resources related to Cardiac arrest
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The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore
2019 IEEE 58th Conference on Decision and Control (CDC)
The CDC is recognized as the premier scientific and engineering conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, systems and control, and related areas.The 58th CDC will feature contributed and invited papers, as well as workshops and may include tutorial sessions.The IEEE CDC is hosted by the IEEE Control Systems Society (CSS) in cooperation with the Society for Industrial and Applied Mathematics (SIAM), the Institute for Operations Research and the Management Sciences (INFORMS), the Japanese Society for Instrument and Control Engineers (SICE), and the European Union Control Association (EUCA).
The Annual Meeting is a gathering of experts who work and conduct research in the industrial applications of electrical systems.
The aim of this conference is to allow participants an opportunity to discuss the recent developments in the field of computing, electronics, mechatronics and biomedical engineering.
IEEE Antennas and Wireless Propagation Letters (AWP Letters) will be devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation.
The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...
Physics, medicine, astronomy—these and other hard sciences share a common need for efficient algorithms, system software, and computer architecture to address large computational problems. And yet, useful advances in computational techniques that could benefit many researchers are rarely shared. To meet that need, Computing in Science & Engineering (CiSE) presents scientific and computational contributions in a clear and accessible format. ...
IEEE Spectrum, 2014
A poet might say that each human being's heart is a unique mystery. Those of us working in the brand new field of computational medicine, however, can now model each of those unique hearts with marvelous accuracy and reveal their secrets. In my laboratory at Johns Hopkins University, my team creates computer models to simulate individual patients' hearts, which can ...
2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2006
The objective of this paper is to examine the knowledge management (KM) paradigm in the context of UK paramedics' assessment and treatment of patients with suspected acute myocardial infarction (MI) or 'heart attack'. We outline the role of thrombolytic therapy and other aspects of emergency cardiac care and discuss how contemporary KM tools and techniques can be used to support ...
2016 28th International Conference on Microelectronics (ICM), 2016
Heart disease is the number one cause of death for both men and women worldwide. Heart attacks represent 30% of global fatalities and are classified as high medical emergencies. Unfortunately, almost half of mortal sudden cardiac arrest occurs outside a hospital. In an attempt to decrease this rate and thus, be able to save lives, it is decisive to anticipate ...
IEEE Women in Engineering Magazine, 2009
Features profiles of three women in engineering: Karen Christman, Paulette January and Catherine Klapperich.
2006 Computers in Cardiology, 2006
Possible clinical states of a cardiac arrest patient are ventricular fibrillation/tachycardia (VF/VT), asystole (ASY) or pulseless electrical activity (PEA), and the treatment goals are return of spontaneous circulation (ROSC) and neurologic ally intact survival. Waveform analysis has been used in VF to predict treatment outcomes and we hypothesised that similar analysis in PEA could predict transformation to ROSC. We analysed ...
A poet might say that each human being's heart is a unique mystery. Those of us working in the brand new field of computational medicine, however, can now model each of those unique hearts with marvelous accuracy and reveal their secrets. In my laboratory at Johns Hopkins University, my team creates computer models to simulate individual patients' hearts, which can help cardiologists carry out lifesaving treatments. Such models may soon transform medicine, ushering in a new kind of personalized health care with radically improved outcomes. . Biomedical engineers have learned how to use numerical models to generate increasingly sophisticated “virtual organs” over the past decade, and rapid developments in cardiac simulation have made the virtual heart the most complete model of all. It's a complex replica, as it must mimic the heart's workings at the molecular scale, through the cellular scale, and up to the level of the whole organ, where muscle tissue expands and contracts with every heartbeat. What's more, the modeling at these different scales must be tightly integrated to accurately render the constant feedback interactions that govern the functions of the heart.Such models have already proved their value for basic cardiac research, allowing scientists to plug in experimental data and study what goes on in both normal and diseased hearts. Now, virtual hearts are poised to deliver breakthroughs at the bedside. Starting with a patient's MRI scans, specialists in computational cardiology can create a personalized model of the patient's heart to study his or her unique ailment. Doctors can then poke and prod the computerized organ in ways that simply aren't possible with a flesh-andblood heart. With these models at their disposal, cardiologists should be able to improve therapies, minimize the invasiveness of diagnostic procedures, and reduce health-care costs. While this simulation-based medicine is still in the experimental stages, I believe upcoming clinical trials will show the real value of virtual hearts.
The objective of this paper is to examine the knowledge management (KM) paradigm in the context of UK paramedics' assessment and treatment of patients with suspected acute myocardial infarction (MI) or 'heart attack'. We outline the role of thrombolytic therapy and other aspects of emergency cardiac care and discuss how contemporary KM tools and techniques can be used to support the development and retention of key clinical skills and knowledge in this emerging field of practice
Heart disease is the number one cause of death for both men and women worldwide. Heart attacks represent 30% of global fatalities and are classified as high medical emergencies. Unfortunately, almost half of mortal sudden cardiac arrest occurs outside a hospital. In an attempt to decrease this rate and thus, be able to save lives, it is decisive to anticipate a heart attack by early detection of concomitant signs. Furthermore, if associated symptoms of heart abnormalities such as arrhythmias could be foreseen, unaware patients could be alerted beforehand thus, granting them precious time to seek effective medical assistance. Finally, patients with heart conditions are at high risk of running irreversible incidents when left alone at home without close surveillance or monitoring. The objective of this paper is to present a comprehensive platform with a threefold objective aiming at contributing towards a vital solution to the aforementioned unfortunate encounters. The platform consists of an electronic device interfaced with a smart phone that will acquire electrocardiogram (ECG) signals around the clock, and using inter-beat (R-R) interval analysis, alert the patient at once, and instantly contact a preprogrammed emergency service number when a risk threshold is gauged.
Features profiles of three women in engineering: Karen Christman, Paulette January and Catherine Klapperich.
Possible clinical states of a cardiac arrest patient are ventricular fibrillation/tachycardia (VF/VT), asystole (ASY) or pulseless electrical activity (PEA), and the treatment goals are return of spontaneous circulation (ROSC) and neurologic ally intact survival. Waveform analysis has been used in VF to predict treatment outcomes and we hypothesised that similar analysis in PEA could predict transformation to ROSC. We analysed 120 and 83 PEA segments prior to transitions to ROSC and ASY, respectively, to investigate the ability often electrocardiograph (ECG) features to predict transitions to ROSC or ASY using neural networks. The feature combination that yielded the best discrimination had a meanplusmnSD area under the receiver operating characteristics curve of 0.88plusmn0.02. The results suggest that the ECG contains information regarding the dynamics of PEA which can be used to study effects of therapies in cardiac arrest patients.
Presents a new method for noninvasive assessment of baroreceptor sensitivity (BRS). Using this new method, BRS is estimated by linear regression analysis of the instantaneous values of BRS reordered into a logical, ascending sequence. The performance of the new method was compared with measurements of BRS by the traditional bolus phenylephrine method and other well-known non- invasive BRS assessment techniques, such as the "spontaneous sequences" and the "correlated modulus" methods, in 19 subjects. The BRS of the entire population was 8.31/spl plusmn/3.90 ms/mmHg for the phenylephrine method, 8.39/spl plusmn/4.21 ms/mmHg for the new method, and 12.6/spl plusmn/6.72 ms/mmHg for the spontaneous sequences method. However, estimation of BRS using the modulus method was only valid for five subjects (weighted coherence >0.5) with a BRS of 10.41/spl plusmn/4.18 ms/mmHg. The estimates of BRS derived from the four methods were significantly correlated for these five subjects. This result suggests that, with further refinements, the new method may be used for reliable non-invasive estimation of BRS.
Cardiovascular diseases in the world are the most common cause of death. Our study aims to predict the rate of heart attack risk for individuals using the Bagging Method, an ensemble Machine Learning classification algorithm. For this reason, a questionnaire has been prepared to collect the relevant data. After obtaining the official permissions, the questionnaires are applied to the patients who have had a heart attack. By this way a predefined dataset is created to be used in the classification algorithms. In the applications, heart attack risk can be detected for an individual by using powerful ensemble classifiers. Additionally, in cross validation process the proposed model shows a high-performance in regression. Therefore, this suggested Clinical Decision Support System (CDSS) enables to take some precautions before a heart attack.
Capnography is often used for the guidance on ventilation rate during cardiopulmonary resuscitation (CPR). However, capnogram waveform frequently presents oscillations induced by chest compressions (CC), affecting the reliability of ventilation detection. The aim of the work was to evaluate the performance of an open-loop adaptive filter in the cancellation of CC oscillations in the capnogram during CPR. For that purpose, we analyzed 60 episodes from an out-of-hospital (OOH) cardiac arrest registry maintained by TVF&R agency (USA). In 50% of the episodes the capnogram was corrupted by CC oscillations. The goodness of the filtering scheme was assessed by comparing the sensitivity (Se) and the positive predictive value (PPV) of an automated ventilation detector before and after filtering. A fixed-coefficient low-pass filter was also designed for comparison. The results showed that both filters reported a good performance although the adaptive scheme presented a slightly higher PPV (+1.2 points globally). The simpler fixed-coefficient scheme avoids the reference signal, but requires validation with larger datasets to ensure stability.
Myocardial infarction, commonly known as heart attack, is one of the major causes of death around the world. For many, heart attacks are unexpected and can occur at any time, especially if a person previously had a heart attack or any type of heart disease. The suddenness of a heart attack makes it difficult to detect and prevent it from occurring, resulting in death or irreversible injury to the heart. Finding a method of detecting a heart attack even five minutes before the attack occurs can be the time between life and death. My research aims to use a machine learning algorithm incorporated into a noninvasive biosensor for early detection of heart attacks. Users first enter factors such as biometrics, history of cardiac diseases, and habits. The biosensor will have a live feed of ECG data from the user. The neural network algorithm will take these initial factors as well as the ECG data to determine whether or not a user is experiencing a myocardial infarction. The neural network is trained by data from the PTB Diagnostic ECG Database from PhysioNet. This project will allow early detection of a heart attack, thus early treatment and a decreased possibility of death and long term tissue damage, and can also be used to track user heart health over a period of time.
The advent of implantable cardioverter defibrillators (ICDs) has resulted in significant reductions in mortality in patients at high risk for sudden cardiac death. Extensive related basic research and clinical investigation continue. ICDs typically record intracardiac electrograms and inter-beat intervals along with device settings during episodes of device delivery of therapy. Researchers wishing to study these data further have until now been limited to viewing paper plots. In support of multi-center clinical studies of patients with ICDs, we have developed a web based searchable ICD data archiving system, which allows users to use a web browser to upload ICD data from diskettes to a server where the data are automatically processed and archived. Users can view and download the archived ICD data directly via the web. The entire system is built from open source software. At present more than 500 patient ICD data sets have been uploaded to and archived in the system. This project will be of value not only to those who wish to conduct research using ICD data, but also to clinicians who need to archive and review ICD data collected from their patients.
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