AI Article Synopsis
- Electrocardiography (ECG) remains a vital diagnostic tool in medicine, with renewed interest in its clinical importance, driven in part by advancements in artificial intelligence (AI).
- The use of machine learning and deep learning techniques in analyzing ECGs is providing innovative ways to evaluate and interpret these readings, potentially overcoming limitations of traditional methods.
- This overview is divided into two parts: Part 1 focuses on the fundamental aspects of AI-based ECG analysis, while Part 2, which will be published later, reviews current research and future applications of AI in this field.
Article Abstract
Even though electrocardiography is a diagnostic procedure that is now more than 100 years old, medicine cannot do without it. On the contrary, interest in the procedure and its clinical significance is even increasing again. Reports on the evaluation of electrocardiograms (ECGs) with the aid of artificial intelligence (AI) are also responsible for this. Using machine learning and in particular deep learning, both AI subfields, completely new perspectives of ECG evaluation and interpretation arise. The weaknesses inherent in classical computer-assisted ECG evaluation appear to be overcome. This two-part overview deals with AI-based ECG analysis. Part 1 introduces basic aspects of the procedure. Part 2, which is published separately, is devoted to the current state of research and discusses the available studies. In addition, possible scenarios of future application of AI in ECG analysis are discussed.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9177483 | PMC |
| http://dx.doi.org/10.1007/s00399-022-00854-y | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Comparison of P-wave parameter changes after radiofrequency ablation and cryoballoon ablation for paroxysmal atrial fibrillation: An observational cohort study.
J Electrocardiol
January 2025
Department of Cardiology, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Leicester, UK; Department of Cardiovascular Sciences, Clinical Science Wing, University of Leicester, Glenfield Hospital, Leicester, UK; National Institute for Health Research Leicester Research Biomedical Centre, Leicester, UK.
Background: Pulmonary vein isolation (PVI) for paroxysmal atrial fibrillation (PAF) can be performed using one-shot cryoballoon ablation (cryo) or point-by-point radiofrequency ablation (RF). This study compares the changes in P-wave parameters between both ablation methods.
Methods: This single-centre retrospective study included contact force RF and second-generation cryo for PAF between 2018 and 2019.
Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database.
Comput Biol Med
January 2025
Department of Automation, Tsinghua University, Beijing, China. Electronic address:
Background: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiological scoring systems based on clinical indicators at admission. Electrocardiogram (ECG) provides easily accessible information, with heart rate variability (HRV) derived from ECG showing prognostic value. However, few studies have conducted a comprehensive analysis of HRV-based prognostic model against established standards, which limits the application of HRV's prognostic value in clinical settings.
View Article and Find Full Text PDFData analysis protocol for early autonomic dysfunction characterization after severe traumatic brain injury.
Front Neurol
December 2024
Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States.
Background: Traumatic brain injury (TBI) disrupts normal brain tissue and functions, leading to high mortality and disability. Severe TBI (sTBI) causes prolonged cognitive, functional, and multi-organ dysfunction. Dysfunction of the autonomic nervous system (ANS) after sTBI can induce abnormalities in multiple organ systems, contributing to cardiovascular dysregulation and increased mortality.
View Article and Find Full Text PDFSex-specific differences in mortality and neurocardiac interactions in the Kv1.1 knockout mouse model of sudden unexpected death in epilepsy (SUDEP).
J Physiol
January 2025
Department of Biological Sciences, Southern Methodist University, Dallas, TX, USA.
Sudden unexpected death in epilepsy (SUDEP) is a devastating complication of epilepsy with possible sex-specific risk factors, although the exact relationship between sex and SUDEP remains unclear. To investigate this, we studied Kcna1 knockout (Kcna1) mice, which lack voltage-gated Kv1.1 channel subunits and are widely used as a SUDEP model that mirrors key features in humans.
View Article and Find Full Text PDFAllergic rhinitis may attenuate the sympathovagal imbalances in patients with severe obstructive sleep apnea: pilot study using a heart rate variability analysis.
Sleep Breath
January 2025
Departments of Otolaryngology, Kangwon National University College of Medicine, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon-Si, Gangwon-Do, Chuncheon, 24289, Republic of Korea.
Purpose: The effect of allergic rhinitis (AR) on autonomic nervous system in patients with obstructive sleep apnea (OSA) remains unclear. We utilized heart rate variability (HRV) analysis to assess cardiac autonomic activity in patients with OSA, comparing those with and without allergic rhinitis (AR).
Methods: We enrolled 182 patients who visited our sleep clinic complaining of habitual snoring or apnea during sleep.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!