AIMC Topic: Electrocardiography

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Deep Learning Strategy for Sliding ECG Analysis during Cardiopulmonary Resuscitation: Influence of the Hands-Off Time on Accuracy.

Sensors (Basel, Switzerland)
This study aims to present a novel deep learning algorithm for a sliding shock advisory decision during cardiopulmonary resuscitation (CPR) and its performance evaluation as a function of the cumulative hands-off time. We retrospectively used 13,570 ...

Flamingo-Optimization-Based Deep Convolutional Neural Network for IoT-Based Arrhythmia Classification.

Sensors (Basel, Switzerland)
Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. An abnormal signal in the heart causing arrhythmia can be detected at an earlier stage when the heal...

A new deep convolutional neural network incorporating attentional mechanisms for ECG emotion recognition.

Computers in biology and medicine
Using ECG signals captured by wearable devices for emotion recognition is a feasible solution. We propose a deep convolutional neural network incorporating attentional mechanisms for ECG emotion recognition. In order to address the problem of individ...

Automated inter-patient arrhythmia classification with dual attention neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Arrhythmia classification based on electrocardiograms (ECG) can enhance clinical diagnostic efficiency. However, due to the significant differences in the number of different categories of heartbeats, the performance of cla...

Using artificial intelligence to enhance the diagnostic accuracy of the 12-lead electrocardiogram in patients with suspected acute pulmonary embolism - Can you teach an old dog new tricks?

Revista portuguesa de cardiologia : orgao oficial da Sociedade Portuguesa de Cardiologia = Portuguese journal of cardiology : an official journal of the Portuguese Society of Cardiology

Characterization of noise in long-term ECG monitoring with machine learning based on clinical criteria.

Medical & biological engineering & computing
Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the ...

Artificial intelligence-based diagnosis of acute pulmonary embolism: Development of a machine learning model using 12-lead electrocardiogram.

Revista portuguesa de cardiologia : orgao oficial da Sociedade Portuguesa de Cardiologia = Portuguese journal of cardiology : an official journal of the Portuguese Society of Cardiology
INTRODUCTION: Pulmonary embolism (PE) is a life-threatening condition, in which diagnostic uncertainty remains high given the lack of specificity in clinical presentation. It requires confirmation by computed tomography pulmonary angiography (CTPA). ...

Novel AI-based HRV analysis (NAIHA) in healthcare automation and related applications.

Journal of electrocardiology
BACKGROUND: Heart rate variability (HRV) analysis computed on R-R interval series of ECG records with heavy burden of ectopic beats or non-sinus rhythm can significantly distort HRV parameters and hence clinically ineligible for HRV analysis. Yet, ex...

An ECG Stitching Scheme for Driver Arrhythmia Classification Based on Deep Learning.

Sensors (Basel, Switzerland)
This study proposes an electrocardiogram (ECG) signal stitching scheme to detect arrhythmias in drivers during driving. When the ECG is measured through the steering wheel during driving, the data are always exposed to noise caused by vehicle vibrati...