Arrhythmias

ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.

A heart arrhythmia refers to a set of conditions characterized by irregular heart- beats, with an in...

Assessment of the long RR intervals using convolutional neural networks in single-lead long-term Holter electrocardiogram recordings.

Advancements in medical technology have extended long-term electrocardiogram (ECG) monitoring from t...

ECG Sensor Design Assessment with Variational Autoencoder-Based Digital Watermarking.

Designing an ECG sensor circuit requires a comprehensive approach to detect, amplify, filter, and co...

Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest...

An Energy-Efficient Configurable 1-D CNN-Based Multi-Lead ECG Classification Coprocessor for Wearable Cardiac Monitoring Devices.

Many electrocardiogram (ECG) processors have been widely used for cardiac monitoring. However, most ...

Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach.

Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular r...

Integrating deep learning with ECG, heart rate variability and demographic data for improved detection of atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is a common but often undiagnosed condition, increasing the ris...

AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analys...

Artificial-intelligence-enhanced ECG: a potential solution to limitations of ECG in detecting left ventricular dysfunction after chemotherapy.

A prediction model based on machine learning: prognosis of HBV-induced HCC male patients with smoking and drinking habits after local ablation treatment.

BACKGROUND: Liver cancer, particularly hepatocellular carcinoma (HCC), is a major health concern glo...

Synthetic ECG signal generation using generative neural networks.

Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the scarcity of abnormal cases....

Machine learning models for prediction of NPVR ≥80% with HIFU ablation for uterine fibroids.

BACKGROUND: Currently high-intensity focused ultrasound (HIFU) is widely used to treat uterine fibro...

A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods.

BACKGROUND: Artificial intelligence (AI) enabled algorithms can detect or predict cardiovascular con...

Extraction of fetal heartbeat locations in abdominal phonocardiograms using deep attention transformer.

Assessing fetal health traditionally involves techniques like echocardiography, which require skille...

The impact of training image quality with a novel protocol on artificial intelligence-based LGE-MRI image segmentation for potential atrial fibrillation management.

BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting up to 2 % of t...

Automated detection of arrhythmias using a novel interpretable feature set extracted from 12-lead electrocardiogram.

The availability of large-scale electrocardiogram (ECG) databases and advancements in machine learni...

Multimodal machine learning for deception detection using behavioral and physiological data.

Deception detection is crucial in domains like national security, privacy, judiciary, and courtroom ...

A deep Bi-CapsNet for analysing ECG signals to classify cardiac arrhythmia.

- In recent times, the electrocardiogram (ECG) has been considered as a significant and effective sc...

Reconstruction of ECG from ballistocardiogram using generative adversarial networks with attention.

Electrocardiogram (ECG) is widely used to provide early warning signals for cardiovascular diseases....

Deep learning-based prediction of atrial fibrillation from polar transformed time-frequency electrocardiogram.

Portable and wearable electrocardiogram (ECG) devices are increasingly utilized in healthcare for mo...

Integrated fusion approach for multi-class heart disease classification through ECG and PCG signals with deep hybrid neural networks.

Detection and classification of cardiovascular diseases are crucial for early diagnosis and predicti...