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Automated ECG Interpretation
Automated ECG interpretation is the use of artificial intelligence and pattern recognition software and knowledge bases to carry out automatically the interpretation, test reporting, and computer-aided diagnosis of electrocardiogram tracings obtained usually from a patient. History The first automated ECG programs were developed in the 1970s, when digital ECG machines became possible by third-generation digital signal processing boards. Commercial models, such as those developed by Hewlett-Packard, incorporated these programs into clinically used devices. During the 1980s and 1990s, extensive research was carried out by companies and by university labs in order to improve the accuracy rate, which was not very high in the first models. For this purpose, several signal databases with normal and abnormal ECGs were built by institutions such as MIT and used to test the algorithms and their accuracy. Phases # A digital representation of each recorded ECG channel is obtained, by mean ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Signal Ecg
In signal processing, a signal is a function that conveys information about a phenomenon. Any quantity that can vary over space or time can be used as a signal to share messages between observers. The ''IEEE Transactions on Signal Processing'' includes audio, video, speech, image, sonar, and radar as examples of signal. A signal may also be defined as observable change in a quantity over space or time (a time series), even if it does not carry information. In nature, signals can be actions done by an organism to alert other organisms, ranging from the release of plant chemicals to warn nearby plants of a predator, to sounds or motions made by animals to alert other animals of food. Signaling occurs in all organisms even at cellular levels, with cell signaling. Signaling theory, in evolutionary biology, proposes that a substantial driver for evolution is the ability of animals to communicate with each other by developing ways of signaling. In human engineering, signals are typi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Baselevel
In geology and geomorphology a base level is the lower limit for an erosion process. The modern term was introduced by John Wesley Powell in 1875. The term was subsequently appropriated by William Morris Davis who used it in his cycle of erosion theory. The "ultimate base level" is the plane that results from projection of the sea level under landmasses. It is to this base level that topography tends to approach due to erosion, eventually forming a peneplain close to the end of a cycle of erosion.Phillips, Jonathan D. (2002)"Erosion, isostatic response, and the missing peneplains" ''Geomorphology'', Vol. 45, No. 3-4Elsevier, 15 June 2002, pp. 225-241. . There are also lesser structural base levels where erosion is delayed by resistant rocks. Examples of this include karst regions underlain by insoluble rock. Base levels may be local when large landmasses are far from the sea or disconnected from it, as in the case of endorheic basins. An example of this is the Messinian salinity ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Medical Monitor
In medicine, monitoring is the observation of a disease, condition or one or several medical parameters over time. It can be performed by continuously measuring certain parameters by using a medical monitor (for example, by continuously measuring vital signs by a bedside monitor), and/or by repeatedly performing medical tests (such as blood glucose monitoring with a glucose meter in people with diabetes mellitus). Transmitting data from a monitor to a distant monitoring station is known as telemetry or biotelemetry. Classification by target parameter Monitoring can be classified by the target of interest, including: * Cardiac monitoring, which generally refers to continuous electrocardiography with assessment of the patients condition relative to their cardiac rhythm. A small monitor worn by an ambulatory patient for this purpose is known as a Holter monitor. Cardiac monitoring can also involve cardiac output monitoring via an invasive Swan-Ganz catheter. * Hemodynamic monitori ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cardiac Arrest
Cardiac arrest is when the heart suddenly and unexpectedly stops beating. It is a medical emergency that, without immediate medical intervention, will result in sudden cardiac death within minutes. Cardiopulmonary resuscitation (CPR) and possibly defibrillation are needed until further treatment can be provided. Cardiac arrest results in a rapid loss of consciousness, and breathing may be abnormal or absent. While cardiac arrest may be caused by heart attack or heart failure, these are not the same, and in 15 to 25% of cases, there is a non-cardiac cause. Some individuals may experience chest pain, shortness of breath, nausea, an elevated heart rate, and a light-headed feeling immediately before entering cardiac arrest. The most common cause of cardiac arrest is an underlying heart problem like coronary artery disease that decreases the amount of oxygenated blood supplying the heart muscle. This, in turn, damages the structure of the muscle, which can alter its function. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Defibrillation
Defibrillation is a treatment for life-threatening cardiac arrhythmias, specifically ventricular fibrillation (V-Fib) and non-perfusing ventricular tachycardia (V-Tach). A defibrillator delivers a dose of electric current (often called a ''counter-shock'') to the heart. Although not fully understood, this process depolarizes a large amount of the heart muscle, ending the arrhythmia. Subsequently, the body's natural pacemaker in the sinoatrial node of the heart is able to re-establish normal sinus rhythm. A heart which is in asystole (flatline) cannot be restarted by a defibrillator, but would be treated by cardiopulmonary resuscitation (CPR). In contrast to defibrillation, synchronized electrical cardioversion is an electrical shock delivered in synchrony to the cardiac cycle. Although the person may still be critically ill, cardioversion normally aims to end poorly perfusing cardiac arrhythmias, such as supraventricular tachycardia. Defibrillators can be external, transve ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic Algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc. Methodology Optimization problems In a genetic algorithm, a population of candidate solutions (called individuals, creatures, organisms, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Neural Network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called ''edges''. Neurons and edges typically have a ''weight'' that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cluster Analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistics, statistical data analysis, used in many fields, including pattern recognition, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their understanding of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small Distance function, distances between cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-object ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fuzzy Logics
Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1. The term ''fuzzy logic'' was introduced with the 1965 proposal of fuzzy set theory by Iranian Azerbaijani mathematician Lotfi Zadeh. Fuzzy logic had, however, been studied since the 1920s, as infinite-valued logic—notably by Łukasiewicz and Tarski. Fuzzy logic is based on the observation that people make decisions based on imprecise and non-numerical information. Fuzzy models or sets are mathematical means of representing vagueness and imprecise information (hence the term fuzzy). These models have the capability of recognising, representing, manipulating, interpreting, and using data and information that are vague and lack ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Analysis
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability". Introduction to Bayes' rule Formal explanation Bayesian inference derives the posterior probability as a consequence of two antecedents: a prior probability and a "likelihood function" derived from a statistical model for the observed data. Bayesian inference computes the posterior probability according to Bayes' theorem: P(H\ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Expert System
In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of artificial intelligence (AI) software. An expert system is divided into two subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities. History Early development Soon after the dawn of modern computers in the late 1940s and early 1950s, researchers started realizing the immense potential these machines had for modern society. One of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
