AIMC Topic: Electrocardiography

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A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision.

Sensors (Basel, Switzerland)
Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical ...

3D ECG display with deep learning approach for identification of cardiac abnormalities from a variable number of leads.

Physiological measurement
The objective of this study is to explore new imaging techniques with the use of the deep learning method for the identification of cardiac abnormalities present in electrocardiogram (ECG) signals with 2, 3, 4, 6 and 12-lead in the framework of the P...

Accelerated Aging in LMNA Mutations Detected by Artificial Intelligence ECG-Derived Age.

Mayo Clinic proceedings
OBJECTIVE: To demonstrate early aging in patients with lamin A/C (LMNA) gene mutations after hypothesizing that they have a biological age older than chronological age, as such a finding impacts care.

Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals.

Computers in biology and medicine
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is critical to timely medical treatment to save patients' lives. Routine use of the electrocardiogram (ECG) is the most common method for physicians to assess t...

Generalized Generative Deep Learning Models for Biosignal Synthesis and Modality Transfer.

IEEE journal of biomedical and health informatics
Generative Adversarial Networks (GANs) are a revolutionary innovation in machine learning that enables the generation of artificial data. Artificial data synthesis is valuable especially in the medical field where it is difficult to collect and annot...

Improving Generalization by Learning Geometry-Dependent and Physics-Based Reconstruction of Image Sequences.

IEEE transactions on medical imaging
Deep neural networks have shown promise in image reconstruction tasks, although often on the premise of large amounts of training data. In this paper, we present a new approach to exploit the geometry and physics underlying electrocardiographic imagi...

Artificial Intelligence-Augmented Electrocardiogram in Determining Sex: Correlation with Sex Hormone Levels.

Mayo Clinic proceedings
OBJECTIVE: To study the relationship between the sex probability derived from the artificial intelligence (AI)-augmented electrocardiogram (ECG) and sex hormone levels.

A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge.

Sensors (Basel, Switzerland)
Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically deployed on the cloud, which may not always meet the availability and privacy requ...

Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning.

Journal of cardiac failure
BACKGROUND: Pulmonary hypertension (PH) is life-threatening, and often diagnosed late in its course. We aimed to evaluate if a deep learning approach using electrocardiogram (ECG) data alone can detect PH and clinically important subtypes. We asked: ...