There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECG...
The utilization of unlabeled electrocardiogram (ECG) data is always a critical topic in artificial intelligence healthcare, as the manual annotation for ECG data is a time-consuming task that requires much medical expertise. The recent development of...
Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiog...
BMC medical informatics and decision making
Nov 16, 2022
BACKGROUND: Critical values are commonly used in clinical laboratory tests to define health-related conditions of varying degrees. Knowing the values, people can quickly become aware of health risks, and the health professionals can take immediate ac...
Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially convolutional deep models have shown promise in electrocardiogram (ECG) classification, but there is a l...
Circulation journal : official journal of the Japanese Circulation Society
Nov 12, 2022
BACKGROUND: Brugada syndrome is a potential cause of sudden cardiac death (SCD) and is characterized by a distinct ECG, but not all patients with A Brugada ECG develop SCD. In this study we sought to examine if an artificial intelligence (AI) model c...
According to the STEMI paradigm, only patients whose ECGs meet STEMI criteria require immediate reperfusion. This leads to reperfusion delays and significantly increases the mortality for the quarter of "non-STEMI" patients with totally occluded arte...
BACKGROUND: Mobile Cardiac Outpatient Telemetry (MCOT) can be used to screen high risk patients for atrial fibrillation (AF). These devices rely primarily on algorithmic detection of AF events, which are then stored and transmitted to a clinician for...
. In recent years, deep learning has blossomed in the field of electrocardiography (ECG) processing, outperforming traditional signal processing methods in a number of typical tasks; for example, classification, QRS detection and wave delineation. Al...
Automatic detection of arrhythmia based on electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases. With the increase in widely available digital ECG data and the development of deep learning, multi-...
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