View clinical trials related to Electrocardiogram.
Filter by:Congenital heart disease (CHD) is the most common congenital disease in children. The early detection, diagnosis and treatment of CHD in children is of great significance to improve the prognosis and reduce the mortality of children, but the current screening methods have limitations. Electrocardiogram (ECG), as an economical and rapid means of heart disease detection, has a very important value in the auxiliary diagnosis of CHD.Big data and deep learning technologies in artificial intelligence (AI) have shown great potential in the medical field. The advent of the big data era provides rich data resources for the in-depth study of CHD ECG signals in children. The development of deep learning technology, especially the breakthrough in the field of image recognition, provides a strong technical support for the intelligent analysis of electrocardiogram. The particularity of children electrocardiogram requires the development of a special algorithm model. At present, the research on the application of deep learning models to identify children's electrocardiograms is limited, and the training and verification from large data sets are lacking. Based on the Chinese Congenital Heart Disease Collaborative Research Network, this project aims to integrate data and deep learning technology to develop a set of intelligent electrocardiogram assisted diagnosis system (CHD-ECG AI system) suitable for children with CHD, so as to improve the early detection rate of CHD and improve the efficiency of congenital heart disease screening.
The goal of this observationl study is to evaluate the possibility of building a Deep Learning (DL) model capable of analyzing electrocardiographic traces of athletes and providing information in the form of a probability stratification of cardiovascular disease. Researchers will enroll a training cohort of 455 participants, evaluated following standard clinical practice for eligibility in competitive sports. The response of the clinical evaluation and ECG traces will be recorded to build a DL model. Researchers will subsequently enroll a validation cohort of 76 participants. ECG traces will be analyzed to evaluate the accuracy of the model to discriminate participants cleared for sports eligibility versus participants who need further medical tests
LIST OF PLANNED ORIGINAL PUBLICATIONS 1. T wave inversion detection with machine learning to prevent sudden death in professional football players. 2. Machine learning applied to biological parameters for control and advisory in professional football players (Machine learning applied to biological parameters for control and advisory in professional football players.) 3. Machine learning applied to sport geolocation systems for injury prevention in professional football players.
The goal of this clinical trial is to compare the result from the a six-leads handheld electrocardiogram (ECG) recorder (KardiaMobile 6L) with those of the standard 12-leads ECG at the patients of acute inferior wall ST-elevation myocardial infarction (STEMI), then ultimately reduce the time it takes to perform re-through treatment according to the faster diagnosis. Participants with STEMI who visited the emergency room will be recorded 6-leads ECG using KardiaMobile 6L in addition to the standard 12-lads ECG, which is basically performed for all patients of acute coronary syndrome.
The purpose of this study is to evaluate the feasibility of a new method for self-recordable ECGs using a smartwatch coupled with an image processing algorithm. The long-term goal of this project is to establish such a method and to potentially integrate it into telemedical care.
This study aimed to analyze and investigate whether the use of the PMcardio clinical assistant leads to a more efficient patient management in primary care and more accessible specialised care compared to usual standards of care and to assess the reliability and safety of the PMcardio smartphone application in the primary care use environment. Additionally, to evaluate time savings and cost saving implications of increased availability of specialised care at the primary care level.
Anorexia nervosa is a serious psychiatric illness whose causes remain poorly understood, and which remains difficult to treat to this day. Many clinical manifestations of this disease can have their origin in abnormalities in the perception of signals coming from inside the body, but this remains to be demonstrated. In recent years, research in healthy subjects has shown how the brain constantly perceives the viscera (heart, lungs, stomach). The examiners will use these new, objective and validated methods to explore how the brain processes information from the viscera (interoception) in anorexic patients. In practice, they will quantify the coupling between the cardiac cycle and involuntary eye movements, as well as between the respiratory cycle and voluntary actions such as pressing a button. Finally, by simultaneously recording the electrical activity of the brain, and that of the stomach, the examiners will measure the coupling between the brain and the stomach. All these measurements, which will be compared between a population of patients and healthy subjects, will make it possible to determine whether anorexic patients have an alteration in the perception of their internal body signals and whether this damage affects several organs.
This study aims to confirm a scientific concept that superficially placed electrocardiogram (ECG) leads may be compared to one another to help determine positioning. If proven to be effective, this could offer a more non-invasive means of positioning using superficial ECG tracings.
His-Ventricle (HV) measurement is only obtained invasively using transvenous catheters. This kind of procedure is routinely performed but some risks of complication exist. HV interval prolongation is correlated with increased risk of occurrence of complete atrioventricular block which could lead to syncope or cardiac sudden death. A new non invasive, high density and high fidelity system (BioSemi, BioSemi B.V., Amsterdam, Netherlands) can theorically collect such electrophysiological signal using a signal averaging acquisition method. We want to assess the possibility of non invasive HV interval measurement using this new device.