Myocardial Infarction

A Pervasive, Efficient and Private Future: Realizing Privacy-Preserving Machine Learning Through Hybrid Homomorphic Encryption

Machine Learning (ML) has become one of the most impactful fields of data science in recent years. However, a significant concern with ML is its privacy risks due to rising attacks against ML models. Privacy-Preserving Machine Learning (PPML) methods have been proposed to mitigate the privacy and security risks of ML models. A popular approach to achieving PPML uses Homomorphic Encryption (HE). ...

Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review

The Complex Emotion Recognition System (CERS) deciphers complex emotional states by examining combinations of basic emotions expressed, their interconnections, and the dynamic variations. Through the utilization of advanced algorithms, CERS provides profound insights into emotional dynamics, facilitating a nuanced understanding and customized responses. The attainment of such a level of emotiona...

LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.

The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical sig...

Diagnostic Accuracy of AI Algorithms in Aortic Stenosis Screening: A Systematic Review and Meta-Analysis.

Aortic stenosis (AS) is frequently identified at an advanced stage after clinical symptoms appear. The aim of this systematic review and meta-analysi...

Clinical decision support using machine learning and cardiac troponin for the diagnosis of myocardial infarction.

Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease.

BACKGROUND: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to detect biventricular pathophysiology. However, AI-EC...

[Early classification and recognition algorithm for sudden cardiac arrest based on limited electrocardiogram data trained with a two-stages convolutional neural network].

Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden c...

[Detection model of atrial fibrillation based on multi-branch and multi-scale convolutional networks].

Atrial fibrillation (AF) is a life-threatening heart condition, and its early detection and treatment have garnered significant attention from physici...

A Novel Method and Python Library for ECG Signal Quality Assessment.

Electrocardiogram (ECG) is one of the reference cardiovascular diagnostic exams. However, the ECG signal is very prone to being distorted through diff...

VizECGNet: Visual ECG Image Network for Cardiovascular Diseases Classification with Multi-Modal Training and Knowledge Distillation

An electrocardiogram (ECG) captures the heart's electrical signal to assess various heart conditions. In practice, ECG data is stored as either digi...

Machine Learning Constructed Based on Patient Plaque and Clinical Features for Predicting Stent Malapposition: A Retrospective Study.

BACKGROUND: Stent malapposition (SM) following percutaneous coronary intervention (PCI) for myocardial infarction continues to present significant cli...

Quantifying variabilities in cardiac digital twin models of the electrocardiogram

Cardiac digital twins (CDTs) of human cardiac electrophysiology (EP) are digital replicas of patient hearts that match like-for-like clinical observ...

External validation of the myocardial-ischaemic-injury-index machine learning algorithm for the early diagnosis of myocardial infarction: a multicentre cohort study.

BACKGROUND: The myocardial-ischaemic-injury-index (MI) is a novel machine learning algorithm for the early diagnosis of type 1 non-ST-segment elevatio...

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal.

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is often detected at a later stage of depression wit...

Through the Looking Glass Darkly: How May AI Models Influence Future Underwriting?

Applications of Artificial Intelligence (AI) deep-learning models to screening for clinical conditions continue to evolve. Instances provided in this ...

Unlocking Hidden Risks: Harnessing Artificial Intelligence (AI) to Detect Subclinical Conditions from an Electrocardiogram (ECG).

Recent artificial intelligence (AI) advancements in cardiovascular medicine offer potential enhancements in diagnosis, prediction, treatment, and outc...

ECG-based Daily Activity Recognition Using 1D Convolutional Neural Networks.

This study presents an approach to human activity recognition (HAR) using electrocardiogram (ECG) signals. We explore the application of ECG for not o...

Federated Learning for Enhanced ECG Signal Classification with Privacy Awareness.

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked c...

Explainable Multimodal Deep Learning for Heart Sounds and Electrocardiogram Classification.

We introduce a Gradient-weighted Class Activation Mapping (Grad-CAM) methodology to assess the performance of five distinct models for binary classifi...

A Hybrid GCN-LSTM Model for Ventricular Arrhythmia Classification Based on ECG Pattern Similarity.

Accurate differentiation between Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) is essential in the field of cardiology. Recent advanc...