Myocardial Infarction

Convolutional Neural Network for Individual Identification Using Phase Space Reconstruction of Electrocardiogram.

Electrocardiogram (ECG) biometric provides an authentication to identify an individual on the basis of specific cardiac potential measured from a living body. Convolutional neural networks (CNN) outperform traditional ECG biometrics because convolutions can produce discernible features from ECG through machine learning. Phase space reconstruction (PSR), using a time delay technique, is one of the ...

Correlation analysis of deep learning methods in S-ICD screening.

BACKGROUND: Machine learning methods are used in the classification of various cardiovascular diseases through ECG data analysis. The concept of varying subcutaneous implantable cardiac defibrillator (S-ICD) eligibility, owing to the dynamicity of ECG signals, has been introduced before. There are practical limitations to acquiring longer durations of ECG signals for S-ICD screening. This study ex...

Comparison of two artificial intelligence-augmented ECG approaches: Machine learning and deep learning.

BACKGROUND: Artificial intelligence-augmented ECG (AI-ECG) refers to the application of novel AI solutions for complex ECG interpretation tasks. A bro...

ECG signal feature extraction trends in methods and applications.

Signal analysis is a domain which is an amalgamation of different processes coming together to form robust pipelines for the automation of data analys...

Deep learning augmented ECG analysis to identify biomarker-defined myocardial injury.

Chest pain is a common clinical complaint for which myocardial injury is the primary concern and is associated with significant morbidity and mortalit...

Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models.

Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders because of its noninvasive and high temporal reso...

Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone.

In this paper, a system to assess dyspnea with the mMRC scale, on the phone, via deep learning, is proposed. The method is based on modeling the spont...

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

Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide ...

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

The objective of this study is to explore new imaging techniques with the use of the deep learning method for the identification of cardiac abnormalit...

Rams, hounds and white boxes: Investigating human-AI collaboration protocols in medical diagnosis.

In this paper, we study human-AI collaboration protocols, a design-oriented construct aimed at establishing and evaluating how humans and AI can colla...

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

Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is critical to timely medical treatment to save patients' li...

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

Generative Adversarial Networks (GANs) are a revolutionary innovation in machine learning that enables the generation of artificial data. Artificial d...

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

Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized for arrhythmia detection. Contemporary ML algorit...

Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning.

BACKGROUND: Pulmonary hypertension (PH) is life-threatening, and often diagnosed late in its course. We aimed to evaluate if a deep learning approach ...

Deep Learning Analysis of Chest Radiographs to Triage Patients with Acute Chest Pain Syndrome.

Background Patients presenting to the emergency department (ED) with acute chest pain (ACP) syndrome undergo additional testing to exclude acute coron...

Unsupervised ECG Analysis: A Review.

Electrocardiography is the gold standard technique for detecting abnormal heart conditions. Automatic detection of electrocardiogram (ECG) abnormaliti...

Smartwatch Sensors with Deep Learning to Predict the Purchase Intentions of Online Shoppers.

In the past decade, the scale of e-commerce has continued to grow. With the outbreak of the COVID-19 epidemic, brick-and-mortar businesses have been a...

Diagnosis of arrhythmias with few abnormal ECG samples using metric-based meta learning.

A major challenge in artificial intelligence based ECG diagnosis lies that it is difficult to obtain sufficient annotated training samples for each rh...

Role of deep learning methods in screening for subcutaneous implantable cardioverter defibrillator in heart failure.

INTRODUCTION: S-ICD eligibility is assessed at pre-implant screening where surface ECG traces are used as surrogates for S-ICD vectors. In heart failu...

Efficient targeted learning of heterogeneous treatment effects for multiple subgroups.

In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzin...