Artificial cardiac pacemakerAn artificial cardiac pacemaker (artificial pacemaker, and sometimes just pacemaker, although the term is also used to refer to the body's natural cardiac pacemaker) is a medical device, nowadays always implanted, that generates electrical pulses delivered by electrodes to one or more of the chambers of the heart, the upper atria or lower ventricles. Each pulse causes the targeted chamber(s) to contract and pump blood, thus regulating the function of the electrical conduction system of the heart.
Cardiac electrophysiologyCardiac electrophysiology is a branch of cardiology and basic science focusing on the electrical activities of the heart. The term is usually used in clinical context, to describe studies of such phenomena by invasive (intracardiac) catheter recording of spontaneous activity as well as of cardiac responses to programmed electrical stimulation - clinical cardiac electrophysiology. However, cardiac electrophysiology also encompasses basic research and translational research components.
Clinical cardiac electrophysiologyClinical cardiac electrophysiology (also referred to as cardiac electrophysiology, arrhythmia services, or electrophysiology), is a branch of the medical specialty of cardiology and is concerned with the study and treatment of rhythm disorders of the heart. Cardiologists with expertise in this area are usually referred to as electrophysiologists. Electrophysiologists are trained in the mechanism, function, and performance of the electrical activities of the heart.
Electrophysiology studyA cardiac electrophysiology study (EP test or EP study) is a minimally invasive procedure using catheters introduced through a vein or artery to record electrical activity from within the heart. This electrical activity is recorded when the heart is in a normal rhythm (sinus rhythm) to assess the conduction system of the heart and to look for additional electrical connections (accessory pathways), and during any abnormal heart rhythms that can be induced.
ElectrocardiographyElectrocardiography is the process of producing an electrocardiogram (ECG or EKG ), a recording of the heart's electrical activity through repeated cardiac cycles. It is an electrogram of the heart which is a graph of voltage versus time of the electrical activity of the heart using electrodes placed on the skin. These electrodes detect the small electrical changes that are a consequence of cardiac muscle depolarization followed by repolarization during each cardiac cycle (heartbeat).
Types of artificial neural networksThere are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.
Artificial neural networkArtificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting 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.
ArrhythmiaArrhythmias, also known as cardiac arrhythmias, heart arrhythmias, or dysrhythmias, are irregularities in the heartbeat, including when it is too fast or too slow. A resting heart rate that is too fast – above 100 beats per minute in adults – is called tachycardia, and a resting heart rate that is too slow – below 60 beats per minute – is called bradycardia. Some types of arrhythmias have no symptoms. Symptoms, when present, may include palpitations or feeling a pause between heartbeats.
CardiologyCardiology () is the study of the heart. Cardiology is a branch of medicine that deals with disorders of the heart and the cardiovascular system. The field includes medical diagnosis and treatment of congenital heart defects, coronary artery disease, heart failure, valvular heart disease, and electrophysiology. Physicians who specialize in this field of medicine are called cardiologists, a specialty of internal medicine. Pediatric cardiologists are pediatricians who specialize in cardiology.
Computer simulationComputer simulation is the process of mathematical modelling, performed on a computer, which is designed to predict the behaviour of, or the outcome of, a real-world or physical system. The reliability of some mathematical models can be determined by comparing their results to the real-world outcomes they aim to predict. Computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics (computational physics), astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care and engineering.
Cardiac arrestCardiac arrest occurs when the heart stops beating. It is defined as cessation of normal circulation of blood due to failure of the heart to pump effectively. It is a medical emergency that, without immediate medical intervention, will result in cardiac death within minutes. When it happens suddenly, it is called sudden cardiac arrest. 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.
AutoencoderAn autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction. Variants exist, aiming to force the learned representations to assume useful properties.
Recurrent neural networkA recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.
Real-time computingReal-time computing (RTC) is the computer science term for hardware and software systems subject to a "real-time constraint", for example from event to system response. Real-time programs must guarantee response within specified time constraints, often referred to as "deadlines". Real-time responses are often understood to be in the order of milliseconds, and sometimes microseconds. A system not specified as operating in real time cannot usually guarantee a response within any timeframe, although typical or expected response times may be given.
Variational autoencoderIn machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. It is part of the families of probabilistic graphical models and variational Bayesian methods. Variational autoencoders are often associated with the autoencoder model because of its architectural affinity, but with significant differences in the goal and mathematical formulation. Variational autoencoders are probabilistic generative models that require neural networks as only a part of their overall structure.
SimulationA simulation is the imitation of the operation of a real-world process or system over time. Simulations require the use of models; the model represents the key characteristics or behaviors of the selected system or process, whereas the simulation represents the evolution of the model over time. Often, computers are used to execute the simulation. Simulation is used in many contexts, such as simulation of technology for performance tuning or optimizing, safety engineering, testing, training, education, and video games.
Generative adversarial networkA generative adversarial network (GAN) is a class of machine learning framework and a prominent framework for approaching generative AI. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. Given a training set, this technique learns to generate new data with the same statistics as the training set.
Long QT syndromeLong QT syndrome (LQTS) is a condition affecting repolarization (relaxing) of the heart after a heartbeat, giving rise to an abnormally lengthy QT interval. It results in an increased risk of an irregular heartbeat which can result in fainting, drowning, seizures, or sudden death. These episodes can be triggered by exercise or stress. Some rare forms of LQTS are associated with other symptoms and signs including deafness and periods of muscle weakness. Long QT syndrome may be present at birth or develop later in life.
Cardiac catheterizationCardiac catheterization (heart cath) is the insertion of a catheter into a chamber or vessel of the heart. This is done both for diagnostic and interventional purposes. A common example of cardiac catheterization is coronary catheterization that involves catheterization of the coronary arteries for coronary artery disease and myocardial infarctions ("heart attacks"). Catheterization is most often performed in special laboratories with fluoroscopy and highly maneuverable tables.
Neural networkA neural network can refer to a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed.
