Myocardial Infarction

Deep Learning Approach for Highly Specific Atrial Fibrillation and Flutter Detection based on RR Intervals.

Atrial fibrillation (AF) and atrial flutter (AFL) represent atrial arrhythmias closely related to increasing risk for embolic stroke, and therefore being in the focus of cardiologists. While the reported methods for AF detection exhibit high performances, little attention has been given to distinguishing these two arrhythmias. In this study, we propose a deep neural network architecture, which com...

A Robust Machine Learning Architecture for a Reliable ECG Rhythm Analysis during CPR.

Chest compressions delivered during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may make the shock advice algorithms (SAA) of defibrillators inaccurate. There is evidence that methods consisting of adaptive filters that remove the CPR artifact followed by machine learning (ML) based algorithms are able to make reliable shock/no-shock decisions during compressions. However,...

A Deep Learning Method to Detect Atrial Fibrillation Based on Continuous Wavelet Transform.

Atrial fibrillation (AF) is one of the most common arrhythmias. The automatic AF detection is of great clinical significance but at the same time it r...

An Electrocardiogram Delineator via Deep Segmentation Network.

Electrocardiogram (ECG) delineation is a process to detect multiple characteristic points, which contain critical diagnostic information about cardiac...

Myocardial Infarction Detection Based on Multi-lead Ensemble Neural Network.

Automatic myocardial infarction (MI) detection using an electrocardiogram (ECG) is of great significance for improving the survival rate of patients. ...

Phase-domain Deep Patient-ECG Image Learning for Zero-effort Smart Health Security.

Smart health is quickly boosted by technological advancements: smart sensors, body sensor network, internet of medical things and big data. Vast amoun...

ECG Biometric Recognition: Template-Free Approaches Based on Deep Learning.

Biometric technologies offer much convenience over the conventional approaches to identity recognition, but security and privacy concerns also accompa...

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 electrodes and skin. This unconstrained measurement is...

Cardiovascular disease diagnosis using cross-domain transfer learning.

While cardiovascular diseases (CVDs) are commonly diagnosed by cardiologists via inspecting electrocardiogram (ECG) waveforms, these decisions can be ...

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

The analysis and interpretation of physiological signals acquired non-invasively are increasingly important in Smart Health, precision medicine, and m...

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 management is important for our health. Convolution...

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 asthma and sleep apnea. Continuous monitoring of chro...

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 conditions and the performance of the classification will ...

Recognition of Endovascular Manipulations using Recurrent Neural Networks.

The ability to accurately recognize elementary surgical gestures is a stepping stone to automated surgical assessment and surgical training. In this p...

[Automatic classification method of arrhythmia based on discriminative deep belief networks].

Existing arrhythmia classification methods usually use manual selection of electrocardiogram (ECG) signal features, so that the feature selection is s...

[Deep residual convolutional neural network for recognition of electrocardiogram signal arrhythmias].

Electrocardiogram (ECG) signals are easily disturbed by internal and external noise, and its morphological characteristics show significant variations...

[Automatic Identifcation of Heart Block Precise Location Based on Sparse Connection Residual Network].

OBJECTIVE: To classify Right Bundle Branch Block (RBBB),Left Bundle Branch Block (LBBB) and normal ECG signals automatically.

A new deep learning model for assisted diagnosis on electrocardiogram.

In order to enhance the accuracy of computer aided electrocardiogram analysis, we propose a deep learning model called CBRNN to assist diagnosis on el...

Detection of Left Ventricular Hypertrophy Using Bayesian Additive Regression Trees: The MESA.

Background We developed a new left ventricular hypertrophy ( LVH ) criterion using a machine-learning technique called Bayesian Additive Regression Tr...

Robotic PCI: Evolving from novel toward non-inferior.

Robotic-assisted PCI appears to be safe and feasible in both simple and complex lesions. In this small cohort study, analysis of manual versus robotic...