Myocardial Infarction

Improving Deep Learning-based Cardiac Abnormality Detection in 12-Lead ECG with Data Augmentation.

Automated Electrocardiogram (ECG) classification using deep neural networks requires large datasets annotated by medical professionals, which is time-consuming and expensive. This work examines ECG augmentation as a method for enriching existing datasets at low cost. First, we introduce three novel augmentations: Limb Electrode Move and Chest Electrode Move both simulate a minor electrode mislocat...

A U - Net Deep Learning Model for Infant Heart Rate Estimation from Ballistography.

Ballistography(BSG) is a non-intrusive and low- cost alternative to electrocardiography (ECG) for heart rate (HR) monitoring in infants. Due to the inter-patient variance and susceptibility to noise, heartbeat detection in the BSG waveform remains a challenge. The aim of this study was to estimate HR from a bed-based pressure mat BSG signal using a deep learning approach. We trained a U-Net deep n...

A Deep Learning Scheme for Detecting Atrial Fibrillation Based on Fusion of Raw and Discrete Wavelet Transformed ECG Features.

Atrial fibrillation is the most common sustained cardiac arrhythmia and the electrocardiogram (ECG) is a powerful non-invasive tool for its clinical d...

Care Models for Acute Chest Pain That Improve Outcomes and Efficiency: JACC State-of-the-Art Review.

Existing assessment pathways for acute chest pain are often resource-intensive, prolonged, and expensive. In this review, the authors describe existin...

[Transoral robotic surgery for treatment of lingual thyroglossal duct cyst].

To investigate the feasibility, safety and efficacy of transoral robotic surgery (TORS) in the treatment of lingual thyroglossal duct cyst (LTGDC). ...

Comparison of surgical outcomes after robot-assisted laparoscopic partial nephrectomy between patients continuing and discontinuing aspirin therapy: a Japanese single-centre study.

PURPOSE: To investigate the feasibility of continuing aspirin therapy in patients with renal tumours undergoing robot-assisted laparoscopic partial ne...

[Automatic detection model of hypertrophic cardiomyopathy based on deep convolutional neural network].

The diagnosis of hypertrophic cardiomyopathy (HCM) is of great significance for the early risk classification of sudden cardiac death and the screenin...

[Electrocardiogram signal classification algorithm of nested long short-term memory network based on focal loss function].

Electrocardiogram (ECG) can visually reflect the physiological electrical activity of human heart, which is important in the field of arrhythmia detec...

Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence.

Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and...

Development of the AI-Cirrhosis-ECG Score: An Electrocardiogram-Based Deep Learning Model in Cirrhosis.

INTRODUCTION: Cirrhosis is associated with cardiac dysfunction and distinct electrocardiogram (ECG) abnormalities. This study aimed to develop a proof...

[Robotic percutaneous coronary intervention: current evidence, practical aspects and future perspectives].

Robotic percutaneous coronary intervention (R-PCI) is an emerging technology designed to improve operator safety and procedural precision. The interve...

[Design and Implementation of Software Platform for AI-ECG Algorithm Research].

A software platform for AI-ECG algorithm research is designed and implemented to better serve the research of ECG artificial intelligence classificati...

ECG-based Biometric Recognition without QRS Segmentation: A Deep Learning-Based Approach.

Electrocardiogram (ECG)-based identification systems have been widely studied in the literature. Usually, an ECG trace needs to be segmented according...

An Approach for Deep Learning in ECG Classification Tasks in the Presence of Noisy Labels.

Cardiovascular disease (CVD) is a serial of diseases with global leading causes of death. Electrocardiogram (ECG) is the most commonly used basis for ...

Dual Attention Convolutional Neural Network Based on Adaptive Parametric ReLU for Denoising ECG Signals with Strong Noise.

Electrocardiogram (ECG) signal is one of the most important methods for diagnosing cardiovascular diseases but is usually affected by noises. Denoisin...

Improving Automatic Detection of ECG Abnormality with Less Manual Annotations using Siamese Network.

Electrocardiography is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by automatic interpretation a...

Signal Quality Assessment of PPG Signals using STFT Time-Frequency Spectra and Deep Learning Approaches.

Photoplethysmography (PPG) is an important signal which contains much physiological information like heart rate and cardiovascular health etc. However...

Increased Risks of Re-identification For Patients Posed by Deep Learning-Based ECG Identification Algorithms.

ECGs analysis is an important tool in cardiac diagnosis. ECG data also have the potential to be used as a biometric source that allows precise person ...

A comparative study of AI systems for epileptic seizure recognition based on EEG or ECG.

The majority of studies for automatic epileptic seizure (ictal) detection are based on electroencephalogram (EEG) data, but electrocardiogram (ECG) pr...

An Improved Networked Predictive Controller for Vascular Robot Using 5G Networks.

Percutaneous coronary intervention (PCI) has gradually become the most common treatment of coronary artery disease (CAD) in clinical practice due to i...