AIMC Topic: Electrocardiography

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Ventricular Arrhythmia Classification Using Similarity Maps and Hierarchical Multi-Stream Deep Learning.

IEEE transactions on bio-medical engineering
OBJECTIVE: Ventricular arrhythmias are the primary arrhythmias that cause sudden cardiac death. We address the problem of classification between ventricular tachycardia (VT), ventricular fibrillation (VF) and non-ventricular rhythms (NVR).

Electrocardiographic-Driven artificial intelligence Model: A new approach to predicting One-Year mortality in heart failure with reduced ejection fraction patients.

International journal of medical informatics
BACKGROUND: Despite the proliferation of heart failure (HF) mortality prediction models, their practical utility is limited. Addressing this, we utilized a significant dataset to develop and validate a deep learning artificial intelligence (AI) model...

Artificial Intelligence ECG Diastolic Dysfunction and Survival in Cardiac Intensive Care Unit Patients.

Journal of the American Heart Association
BACKGROUND: Left ventricular diastolic dysfunction (LVDD) predicts mortality in patients in cardiac intensive care units. An artificial intelligence enhanced ECG (AIECG) algorithm can predict LVDD and mortality in general populations but has not been...

Temporal and spatial self supervised learning methods for electrocardiograms.

Scientific reports
The limited availability of labeled ECG data restricts the application of supervised deep learning methods in ECG detection. Although existing self-supervised learning approaches have been applied to ECG analysis, they are predominantly image-based, ...

Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review.

Current cardiology reports
PURPOSE OF REVIEW: This review explores the role of artificial intelligence (AI) in cardio-oncology, focusing on its latest application across problems in diagnosis, prognosis, risk stratification, and management of cardiovascular (CV) complications ...

Antifreezing Ultrathin Bioionic Gel-Based Wearable System for Artificial Intelligence-Assisted Arrhythmia Diagnosis in Hypothermia.

ACS nano
Cardiovascular disease (CAD) is a major global public health issue, with mortality rates being significantly impacted by cold temperatures. Stable and reliable electrocardiogram (ECG) monitoring in cold environments is crucial for early detection and...

Thermo-responsive and phase-separated hydrogels for cardiac arrhythmia diagnosis with deep learning algorithms.

Biosensors & bioelectronics
Adhesive epidermal hydrogel electrodes are essential for achieving robust signal transduction and cardiac arrhythmia diagnosis, but detachment of conventional adhesive dressings easily causes secondary damage to delicate wound tissues due to lack of ...

Transformer-based heart language model with electrocardiogram annotations.

Scientific reports
This paper explores the potential of transformer-based foundation models to detect Atrial Fibrillation (AFIB) in electrocardiogram (ECG) processing, an arrhythmia specified as an irregular heart rhythm without patterns. We construct a language with t...

An arrhythmia classification using a deep learning and optimisation-based methodology.

Journal of medical engineering & technology
The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transformation for the remove of baseline wandering and powerline interference. The preprocessed signals ar...

A Multimodal Deep Learning Approach to Intraoperative Nociception Monitoring: Integrating Electroencephalogram, Photoplethysmography, and Electrocardiogram.

Sensors (Basel, Switzerland)
Monitoring nociception under general anesthesia remains challenging due to the complexity of pain pathways and the limitations of single-parameter methods. In this study, we introduce a multimodal approach that integrates electroencephalogram (EEG), ...