Myocardial Infarction

Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal.

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recentl...

Climate-induced thermoregulatory responses in a non-linear thermal environment: investigating the inter-dependencies using a facile artificial neural network-based predictive strategy.

. Given the burgeoning impacts of climatic variability on human health, suitable computational paradigms are used to explore the subsequent ergonomic repercussions. The artificial neural network (ANN), in particular, exhibits near-accurate input-output mapping. However, employment of the ANN to trace the inter-dependencies between the climatic and human thermoregulatory parameters in real-world fu...

Deep learning models for electrocardiograms are susceptible to adversarial attack.

Electrocardiogram (ECG) acquisition is increasingly widespread in medical and commercial devices, necessitating the development of automated interpret...

Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms.

BACKGROUND: Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify different types of cardiac arrhythmias with the use of a singl...

A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications.

This paper reviews the state of the arts and trends of the AI-Based biomedical processing algorithms and hardware. The algorithms and hardware for dif...

ECG Authentication Hardware Design With Low-Power Signal Processing and Neural Network Optimization With Low Precision and Structured Compression.

Biometrics such as facial features, fingerprint, and iris are being used increasingly in modern authentication systems. These methods are now popular ...

Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis.

BACKGROUND: Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and...

Simultaneous multiple features tracking of beats: A representation learning approach to reduce false alarm rates in ICUs.

The high rate of false alarms is a key challenge related to patient care in intensive care units (ICUs) that can result in delayed responses of the me...

End-to-end trained encoder-decoder convolutional neural network for fetal electrocardiogram signal denoising.

OBJECTIVE: Non-invasive fetal electrocardiography has the potential to provide vital information for evaluating the health status of the fetus. Howeve...

Machine Learning Approach to Identify Stroke Within 4.5 Hours.

Background and Purpose- We aimed to investigate the ability of machine learning (ML) techniques analyzing diffusion-weighted imaging (DWI) and fluid-a...

Machine Learning for Detecting Early Infarction in Acute Stroke with Non-Contrast-enhanced CT.

Background Identifying the presence and extent of infarcted brain tissue at baseline plays a crucial role in the treatment of patients with acute isch...

Comprehensive electrocardiographic diagnosis based on deep learning.

Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can pr...

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG.

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces th...

Transfer Learning in ECG Classification from Human to Horse Using a Novel Parallel Neural Network Architecture.

Automatic or semi-automatic analysis of the equine electrocardiogram (eECG) is currently not possible because human or small animal ECG analysis softw...

Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering.

Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified wi...

Correlation of Troponin Level (Troponin T, Troponin I) With PELOD-2 Score in Sepsis as a Predictive Factor of Mortality.

BACKGROUND: Sepsis in children with cardiovascular involvement can increase mortality. Recently, many studies have been conducted to investigate tropo...

Secretion of equine chorionic gonadotropin and its association with supplementary corpus luteum formation and progesterone concentration in Hokkaido native pony recipient mares.

The objectives of this study were to determine the plasma profile of equine chorionic gonadotropin (eCG) and its association with the formation of sup...

A 13.34 μW Event-Driven Patient-Specific ANN Cardiac Arrhythmia Classifier for Wearable ECG Sensors.

Artificial neural network (ANN) and its variants are favored algorithm in designing cardiac arrhythmia classifier (CAC) for its high accuracy. However...

[Predicting atrial fibrillation through a sinus-rhythm electrocardiogram; useful or not?].

In patients with cryptogenic stroke, the detection of atrial fibrillation (AF) is important, since it is an indication for the prescription of oral an...

Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram.

BACKGROUND: The electrocardiogram (ECG) has been widely used in the diagnosis of heart disease such as arrhythmia due to its simplicity and non-invasi...