Myocardial Infarction

Chronic heart failure detection based on long-term RR interval dynamics.

Chronic heart failure (CHF) is a condition affecting millions worldwide, characterized by the heart's reduced ability to pump blood efficiently. Conventional diagnostics, such as imaging and ECG assessments, can be time-consuming and expensive, often identifying CHF only after significant progression. Early detection is crucial for improving treatment options and reducing healthcare costs. Heart r...

Multi-domain ECG feature extraction enable accurate automated recognition of bipolar disorder, depression and schizophrenia.

OBJECTIVE: To develop and validate a multi-lead electrocardiogram (ECG)-based machine learning system for automated classification of major psychiatric disorders (bipolar disorder, major depressive disorder, and schizophrenia) using cardiac autonomic biomarkers, and to identify the most discriminative electrophysiological features for differential psychiatric diagnosis. METHODS: A total of 233 de-...

Precision percutaneous coronary intervention.

Precision-based percutaneous coronary intervention (PCI) integrates contemporary strategies across the pre-, intra-, and post-procedural phases to imp...

Imaging biomarkers of post-COVID dyspnea: insights from machine learning CT patterns and parametric response mapping.

The inflammation-microvascular axis in prognosis: Integrated FAI and AMR assessment for risk stratification after PCI in NSTEMI patients.

BACKGROUND: Despite successful percutaneous coronary intervention (PCI), patients with non-ST-segment elevation myocardial infarction (NSTEMI) remain ...

Transfer learning is the electrocardiogram reconstruction capstone.

Electrocardiogram (ECG) reconstruction from reduced-lead configurations is essential for improving patient comfort and enabling wearable cardiac monit...

Performance of Artificial Intelligence-Powered ECG Analysis in Suspected ST-Segment Elevation Myocardial Infarction.

BACKGROUND: Artificial intelligence (AI)-based electrocardiogram (ECG) analysis has emerged as a promising adjunct to human ECG interpretation in susp...

Electrocardiogram derived heart age models agreement, accuracy and predictive ability in the Tromsø study.

Convolutional neural networks (CNNs) can estimate electrocardiogram (ECG)-based heart age. We compared three published CNNs in the Tromsø Study cohort...

VitalDB Arrhythmia Database: An Anesthesiologist-Validated Large-scale Intraoperative Arrhythmia Dataset with Beat and Rhythm Labels.

Intraoperative cardiac arrhythmias present distinct characteristics compared to non-surgical environments, yet publicly available electrocardiogram (E...

Predicting Recurrence and Outcomes After Stressor-Associated Atrial Fibrillation Using ECG-Based Deep Learning.

BACKGROUND: Stressor-associated atrial fibrillation (AF) refers to new-onset AF that occurs with a reversible, acute stressor. Identifying individuals...

Long Short-Term Memory-GPT-4 Integration for Interpretable Biomedical Signal Classification: Proof-of-Concept Study.

BACKGROUND: Approximately 3.8 billion people lack access to essential health services, and diagnostic interpretation remains a major bottleneck in rem...

Cross-sectional evaluation of cardiovascular biological age using point-of-care ultrasound.

AIMS: Biological age is increasingly recognized as a superior predictor of morbidity, mortality, compared with chronological age. Artificial intellige...

Percutaneous coronary intervention in high bleeding risk patients: a review of contemporary management strategies.

INTRODUCTION: High bleeding risk (HBR) affects over one-third of patients undergoing percutaneous coronary intervention (PCI) and is associated with e...

Advances in Electrocardiogram-Based Non-Invasive Blood Glucose Monitoring Technology.

Blood glucose monitoring is fundamental to diabetes management, yet traditional invasive methods are limited by patient discomfort and infection risks...

Towards real-time sleep stage classification: A deep learning approach leveraging PPG and ECG.

OBJECTIVE: This work aims to enable adaptive Consumer Sleep Technologies (CSTs) for sleep intervention by developing a deep learning model for sleep s...

An attention-based multimodal deep learning framework integrating EEG and ECG for enhanced stress detection.

Chronic stress is an important threat in Public Health, as it negatively impacts both the Body and Mind. Current methods for measuring and identifying...

Artificial Intelligence Enabled Noninvasive Mapping of Cardiac Arrhythmia Origins Using the 12-Lead Electrocardiogram.

Artificial intelligence (AI) is reshaping cardiac electrophysiology by extracting information from electrocardiograms that exceeds human visual interp...

Artificial Intelligence-Driven Electrocardiogram Screening for Asymptomatic Left Ventricular Systolic Dysfunction in the General Population.

BACKGROUND: Asymptomatic left ventricular systolic dysfunction (LVSD) is a well-established precursor of overt heart failure (HF), yet it often remain...

Establishing the laboratory error database: rationale, methodology, and a practical example.

Laboratory errors represent a critical yet underestimated threat to patient safety, with 26-30 % of reported errors adversely affecting patient care. ...

Multi-Feature Fusion and Compressed Bi-LSTM for Memory-Efficient Heartbeat Classification on Wearable Devices.

In this paper, we present a memory-efficient ECG based heartbeat classification for wearable devices enabled by multi-feature fusion and compressed bi...