AI-Enabled Electrocardiogram Analysis for Disease Diagnosis
MMR Khan Mamun, T Elfouly - Applied System Innovation, 2023 - mdpi.com
Contemporary methods used to interpret the electrocardiogram (ECG) signal for diagnosis
or monitoring are based on expert knowledge and rule-centered algorithms. In recent years …
or monitoring are based on expert knowledge and rule-centered algorithms. In recent years …
Machine Learning for Predicting Postoperative Atrial Fibrillation After Cardiac Surgery: A Scoping Review of Current Literature
AH El-Sherbini, A Shah, R Cheng, A Elsebaie… - The American Journal of …, 2023 - Elsevier
Postoperative atrial fibrillation (POAF) occurs in up to 20% to 55% of patients who
underwent cardiac surgery. Machine learning (ML) has been increasingly employed in …
underwent cardiac surgery. Machine learning (ML) has been increasingly employed in …
Diagnostic and Prognostic Models Based on Electrocardiograms for Rapid Clinical Applications
Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECG) has the
potential to transform diagnosis and estimate the prognosis of not only cardiac but …
potential to transform diagnosis and estimate the prognosis of not only cardiac but …
Artificial intelligence-based patient selection for deep inspiration breath-hold breast radiotherapy from respiratory signals
A Vendrame, C Cappelletto, P Chiovati, L Vinante… - Applied Sciences, 2023 - mdpi.com
Purpose: to predict eligibility for deep inspiration breath-hold (DIBH) radiotherapy (RT)
treatment of patients with left breast cancer from analysis of respiratory signal, using Deep …
treatment of patients with left breast cancer from analysis of respiratory signal, using Deep …
Early heart disease prediction using feature engineering and machine learning algorithms
Heart disease is one of the most widespread global health issues, it is the reason behind
around 32% of deaths worldwide every year. The early prediction and diagnosis of heart …
around 32% of deaths worldwide every year. The early prediction and diagnosis of heart …
BioSignal Copilot: Leveraging the power of LLMs in drafting reports for biomedical signals
Recent advances in Large Language Models (LLMs) have shown great potential in various
domains, particularly in processing text-based data. However, their applicability to …
domains, particularly in processing text-based data. However, their applicability to …
Robust QRS detection based on simulated degenerate optical parametric oscillator-assisted neural network
Accurately detecting the depolarization QRS complex in the ventricles is a fundamental
requirement for cardiovascular disease detection using electrocardiography (ECG). In …
requirement for cardiovascular disease detection using electrocardiography (ECG). In …
SAR model for accurate detection of multi-label arrhythmias from electrocardiograms
L Yang, Y Zheng, Z Liu, R Tang, L Ma, Y Chen… - Heliyon, 2023 - cell.com
Objective Arrhythmias are prevalent symptoms of cardiovascular disease, necessitating
accurate and timely detection to mitigate associated risks. Detecting arrhythmias from ECGs …
accurate and timely detection to mitigate associated risks. Detecting arrhythmias from ECGs …
Enhancing Electrocardiogram (ECG) Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification
A Bleich, A Linnemann, B Jaidi, BH Diem… - Machine Learning and …, 2023 - mdpi.com
Implantable Cardiac Monitor (ICM) devices are demonstrating, as of today, the fastest-
growing market for implantable cardiac devices. As such, they are becoming increasingly …
growing market for implantable cardiac devices. As such, they are becoming increasingly …
ECG Forecasting System Based on Long Short-Term Memory
Worldwide, cardiovascular diseases are some of the primary causes of death; yet the early
detection and diagnosis of such diseases have the potential to save many lives …
detection and diagnosis of such diseases have the potential to save many lives …