AIMC Topic: Electrocardiography

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Robustness of convolutional neural networks to physiological electrocardiogram noise.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
The electrocardiogram (ECG) is a widespread diagnostic tool in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. Howe...

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Medical & biological engineering & computing
Computerized interpretation of electrocardiogram plays an important role in daily cardiovascular healthcare. However, inaccurate interpretations lead to misdiagnoses and delay proper treatments. In this work, we built a high-quality Chinese 12-lead r...

Model Construction of Using Physiological Signals to Detect Mental Health Status.

Journal of healthcare engineering
BACKGROUND: Mental health is a direct indicator of human mental activity, and it also affects all aspects of the human body. It plays a very important role in monitoring human mental health.

Classification of Arrhythmia in Heartbeat Detection Using Deep Learning.

Computational intelligence and neuroscience
The electrocardiogram (ECG) is one of the most widely used diagnostic instruments in medicine and healthcare. Deep learning methods have shown promise in healthcare prediction challenges involving ECG data. This paper aims to apply deep learning tech...

Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram.

JACC. Cardiovascular imaging
OBJECTIVES: This study sought to develop DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population.

VPNET: Variable Projection Networks.

International journal of neural systems
In this paper, we introduce VPNet, a novel model-driven neural network architecture based on variable projection (VP). Applying VP operators to neural networks results in learnable features, interpretable parameters, and compact network structures. T...

The BrAID study protocol: integration of machine learning and transcriptomics for brugada syndrome recognition.

BMC cardiovascular disorders
BACKGROUND: Type 1 Brugada syndrome (BrS) is a hereditary arrhythmogenic disease showing peculiar electrocardiographic (ECG) patterns, characterized by ST-segment elevation in the right precordial leads, and risk of Sudden Cardiac Death (SCD). Furthe...

ML-Net: Multi-Channel Lightweight Network for Detecting Myocardial Infarction.

IEEE journal of biomedical and health informatics
Due to the complexity of myocardial infarction (MI) waveform, most traditional automatic diagnosis models rarely detect it, while those able to detect MI often require high computing and storage capacity, rendering them unsuitable for portable device...

Natural Language Mapping of Electrocardiogram Interpretations to a Standardized Ontology.

Methods of information in medicine
BACKGROUND: Interpretations of the electrocardiogram (ECG) are often prepared using software outside the electronic health record (EHR) and imported via an interface as a narrative note. Thus, natural language processing is required to create a compu...