Myocardial Infarction

Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time.

With an aging population and increased chronic diseases, remote health monitoring has become critical to improving patient care and reducing healthcare costs. The Internet of Things (IoT) has recently drawn much interest as a potential remote health monitoring remedy. IoT-based systems can gather and analyze a wide range of physiological data, including blood oxygen levels, heart rates, body tempe...

Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.

OBJECTIVES: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed frequently and multifocally to assess the spatially heterogenous tumor tissue. Therefore, the goal of this study was to establish an automated fra...

Coronary Computed Tomography Angiography with Deep Learning Image Reconstruction: A Preliminary Study to Evaluate Radiation Exposure Reduction.

Coronary computed tomography angiography (CCTA) is a medical imaging technique that produces detailed images of the coronary arteries. Our work focuse...

Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea.

An increasing number of patients and a lack of awareness about obstructive sleep apnea is a point of concern for the healthcare industry. Polysomnogra...

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching.

Cardiac monitoring can be performed by means of an accelerometer attached to a subject's chest, which produces the Seismocardiography (SCG) signal. De...

In-Sensor Artificial Intelligence and Fusion With Electronic Medical Records for At-Home Monitoring.

This work presents an artificial intelligence (AI) framework for real-time, personalized sepsis prediction four hours before onset through fusion of e...

Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review.

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor s...

Deep Learning Strategy for Sliding ECG Analysis during Cardiopulmonary Resuscitation: Influence of the Hands-Off Time on Accuracy.

This study aims to present a novel deep learning algorithm for a sliding shock advisory decision during cardiopulmonary resuscitation (CPR) and its pe...

Flamingo-Optimization-Based Deep Convolutional Neural Network for IoT-Based Arrhythmia Classification.

Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. ...

A new deep convolutional neural network incorporating attentional mechanisms for ECG emotion recognition.

Using ECG signals captured by wearable devices for emotion recognition is a feasible solution. We propose a deep convolutional neural network incorpor...

Classification of pulmonary sounds through deep learning for the diagnosis of interstitial lung diseases secondary to connective tissue diseases.

Early diagnosis of interstitial lung diseases secondary to connective tissue diseases is critical for the treatment and survival of patients. The symp...

Robotic percutaneous coronary intervention (R-PCI): Time to focus on the pros and cons.

AIM: To assess the safety, efficiency, and device compatibility of the Second Generation Robotic System.

Coupling analysis of heart rate variability and cortical arousal using a deep learning algorithm.

Frequent cortical arousal is associated with cardiovascular dysfunction among people with sleep-disordered breathing. Changes in heart rate variabilit...

Characterization of noise in long-term ECG monitoring with machine learning based on clinical criteria.

Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical...

Stretchable and All-Directional Strain-Insensitive Electronic Glove for Robotic Skins and Human-Machine Interfacing.

Electronic gloves (e-gloves), with their multifunctional sensing capability, hold a promising application in robotic skin and human-machine interfaces...

Artificial intelligence-based diagnosis of acute pulmonary embolism: Development of a machine learning model using 12-lead electrocardiogram.

INTRODUCTION: Pulmonary embolism (PE) is a life-threatening condition, in which diagnostic uncertainty remains high given the lack of specificity in c...

Identifying Reasons for Statin Nonuse in Patients With Diabetes Using Deep Learning of Electronic Health Records.

Background Statins are guideline-recommended medications that reduce cardiovascular events in patients with diabetes. Yet, statin use is concerningly ...

Novel AI-based HRV analysis (NAIHA) in healthcare automation and related applications.

BACKGROUND: Heart rate variability (HRV) analysis computed on R-R interval series of ECG records with heavy burden of ectopic beats or non-sinus rhyth...

An ECG Stitching Scheme for Driver Arrhythmia Classification Based on Deep Learning.

This study proposes an electrocardiogram (ECG) signal stitching scheme to detect arrhythmias in drivers during driving. When the ECG is measured throu...

CVD22: Explainable artificial intelligence determination of the relationship of troponin to D-Dimer, mortality, and CK-MB in COVID-19 patients.

BACKGROUND AND PURPOSE: COVID-19, which emerged in Wuhan (China), is one of the deadliest and fastest-spreading pandemics as of the end of 2019. Accor...