Cardiovascular

Myocardial Infarction

Latest AI and machine learning research in myocardial infarction for healthcare professionals.

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Showing 1282-1302 of 6,892 articles
RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG.

Automatic detection of R-peaks in an Electrocardiogram signal is crucial in a multitude of applicati...

Arrhythmias Classification Using Short-Time Fourier Transform and GAN Based Data Augmentation.

Lacking sufficient training samples of different heart rhythms is a common bottleneck to obtain arrh...

Arrhythmia Classification using Deep Learning and Machine Learning with Features Extracted from Waveform-based Signal Processing.

Arrhythmia is a serious cardiovascular disease, and early diagnosis of arrhythmia is critical. In th...

Machine Learning on High-Dimensional Data to Predict Bleeding Post Percutaneous Coronary Intervention.

INTRODUCTION: The purpose of the current study is to determine the accuracy of machine learning in p...

Deep learning for comprehensive ECG annotation.

BACKGROUND: Increasing utilization of long-term outpatient ambulatory electrocardiographic (ECG) mon...

[Detection of inferior myocardial infarction based on densely connected convolutional neural network].

Inferior myocardial infarction is an acute ischemic heart disease with high mortality, which is easy...

Autonomic perspiration in 3D-printed hydrogel actuators.

In both biological and engineered systems, functioning at peak power output for prolonged periods of...

Contribution of neural networks in the diagnosis and treatment of cardiac arrhythmia.

Arrhythmia is a dangerous disease in which the heart rhythm varies and it may be very fast or very s...

Machine learning versus traditional risk stratification methods in acute coronary syndrome: a pooled randomized clinical trial analysis.

Traditional statistical models allow population based inferences and comparisons. Machine learning (...

Successful Resuscitation of a Young Girl Who Drank Rivastigmine With Respiratory Failure.

Rivastigmine is a non-competitive reversible inhibitor of acetylcholinesterase which is approved as ...

Relationship of soluble ST2 to pulmonary hypertension severity in patients undergoing cardiac resynchronization therapy.

BACKGROUND: Pulmonary hypertension (PH) is an adverse prognostic marker in patients undergoing cardi...

Heterogeneity of perception of symptoms in patients with asthma.

BACKGROUND: Cough-dominant or cough-variant asthma is common in Japan. However, it is unclear whethe...

Recognition of Endovascular Manipulations using Recurrent Neural Networks.

The ability to accurately recognize elementary surgical gestures is a stepping stone to automated su...

Spectro-Temporal Feature Based Multi-Channel Convolutional Neural Network for ECG Beat Classification.

Automatic classification of abnormal beats in ECG signals is crucial for monitoring cardiac conditio...

RespNet: A deep learning model for extraction of respiration from photoplethysmogram.

Respiratory ailments afflict a wide range of people and manifests itself through conditions like ast...

Optimizing Probability Threshold of Convolution Neural Network to Improve HRV-based Acute Stress Detection Performance.

As stress is linked to numerous emotional and physical conditions, its timely detection and proper m...

Clustering Continuous Wavelet Transform Characteristics of Heart Rate Variability through Unsupervised Learning.

The analysis and interpretation of physiological signals acquired non-invasively are increasingly im...

Cardiovascular disease diagnosis using cross-domain transfer learning.

While cardiovascular diseases (CVDs) are commonly diagnosed by cardiologists via inspecting electroc...

The Feasibility of Arrhythmias Detection from A Capacitive ECG Measurement Using Convolutional Neural Network.

Capacitive ECG (cECG) can measure the cardiac electrical signal via capacitive coupling between elec...

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