AI Medical Compendium Topic

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Arrhythmias, Cardiac

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An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction.

Physical and engineering sciences in medicine
This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to address the problems in arrhythmia diagnosis. The model performs pre-processing of the heartbeat signal by automatically and efficiently extracting tim...

The use of digital health in heart rhythm care.

Expert review of cardiovascular therapy
INTRODUCTION: Digital health is a broad term that includes telecommunication technologies to collect, share and manipulate health information to improve patient health and health care services. With the growing use of wearables, artificial intelligen...

A neuromorphic physiological signal processing system based on VO memristor for next-generation human-machine interface.

Nature communications
Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and health-related information. However, the explosion of physiological signal data presents challenges for ...

Leveling Up: A Review of Machine Learning Models in the Cardiac ICU.

The American journal of medicine
Machine learning has emerged as a significant tool to augment the medical decision-making process. Studies have steadily accrued detailing algorithms and models designed using machine learning to predict and anticipate pathologic states. The cardiac ...

Transformer-based temporal sequence learners for arrhythmia classification.

Medical & biological engineering & computing
An electrocardiogram (ECG) plays a crucial role in identifying and classifying cardiac arrhythmia. Traditional methods employ handcrafted features, and more recently, deep learning methods use convolution and recursive structures to classify heart si...

NSICA: Multi-objective imperialist competitive algorithm for feature selection in arrhythmia diagnosis.

Computers in biology and medicine
This study proposes a multi-objective, non-dominated, imperialist competitive algorithm (NSICA) to solve optimal feature selection problems. The NSICA is a multi-objective and discrete version of the original Imperialist Competitive Algorithm (ICA) t...

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...

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...

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...