Arrhythmias

Electrocardiogram analysis of post-stroke elderly people using one-dimensional convolutional neural network model with gradient-weighted class activation mapping.

Stroke is the second leading cause of death globally after ischemic heart disease, also a risk factor of cardioembolic stroke. Thus, we postulate that heartbeats encapsulate vital signals related to stroke. With the rapid advancement of deep neural networks (DNNs), it emerges as a powerful tool to decipher intriguing heartbeat patterns associated with post-stroke patients. In this study, we propos...

Neural Network Detection of Pacemakers for MRI Safety.

Flagging the presence of cardiac devices such as pacemakers before an MRI scan is essential to allow appropriate safety checks. We assess the accuracy with which a machine learning model can classify the presence or absence of a pacemaker on pre-existing chest radiographs. A total of 7973 chest radiographs were collected, 3996 with pacemakers visible and 3977 without. Images were identified from i...

Artificial Intelligence-Enabled ECG: Physiologic and Pathophysiologic Insights and Implications.

Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date-...

Comparison of neural basis expansion analysis for interpretable time series (N-BEATS) and recurrent neural networks for heart dysfunction classification.

The primary purpose of this work is to analyze the ability of N-BEATS architecture for the problem of prediction and classification of electrocardiogr...

Heart age estimated using explainable advanced electrocardiography.

Electrocardiographic (ECG) Heart Age conveying cardiovascular risk has been estimated by both Bayesian and artificial intelligence approaches. We hypo...

An artificial intelligence-based risk prediction model of myocardial infarction.

BACKGROUND: Myocardial infarction can lead to malignant arrhythmia, heart failure, and sudden death. Clinical studies have shown that early identifica...

Memory-Augmented Generative Adversarial Networks for Anomaly Detection.

We propose a memory-augmented deep learning model for semisupervised anomaly detection (AD). While many traditional AD methods focus on modeling the d...

Cost-Sensitive Learning for Anomaly Detection in Imbalanced ECG Data Using Convolutional Neural Networks.

Arrhythmia detection algorithms based on deep learning are attracting considerable interest due to their vital role in the diagnosis of cardiac abnorm...

A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection.

There is a growing interest in neuromorphic hardware since it offers a more intuitive way to achieve bio-inspired algorithms. This paper presents a ne...

Deepaware: A hybrid deep learning and context-aware heuristics-based model for atrial fibrillation detection.

BACKGROUND: State-of-the-art automatic atrial fibrillation (AF) detection models trained on RR-interval (RRI) features generally produce high performa...

Artificial Intelligence in Electrocardiology for Arrhythmia Diagnosis.

ECG classification system based on multi-domain features approach coupled with least square support vector machine (LS-SVM).

Developing a robust authentication and identification method becomes an urgent demand to protect the integrity of devices data. Although the use of pa...

[Artificial intelligence-based ECG analysis: current status and future perspectives-Part 2 : Recent studies and future].

While fundamental aspects of the application of artificial intelligence (AI) to electrocardiogram (ECG) analysis were discussed in part 1 of this revi...

[Artificial intelligence-based ECG analysis: current status and future perspectives-Part 1 : Basic principles].

Even though electrocardiography is a diagnostic procedure that is now more than 100 years old, medicine cannot do without it. On the contrary, interes...

rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography.

BACKGROUND: Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with ec...

ANNet: A Lightweight Neural Network for ECG Anomaly Detection in IoT Edge Sensors.

In this paper, we propose a lightweight neural network for real-time electrocardiogram (ECG) anomaly detection and system level power reduction of wea...

Exploiting exercise electrocardiography to improve early diagnosis of atrial fibrillation with deep learning neural networks.

Atrial fibrillation (AF) is the most common type of sustained arrhythmia. It results from abnormal irregularities in the electrical performance of the...

An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.

Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare...

A deep learning approach identifies new ECG features in congenital long QT syndrome.

BACKGROUND: Congenital long QT syndrome (LQTS) is a rare heart disease caused by various underlying mutations. Most general cardiologists do not routi...

Electrocardiogram Biometrics Using Transformer's Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification.

The existing electrocardiogram (ECG) biometrics do not perform well when ECG changes after the enrollment phase because the feature extraction is not ...