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Electrocardiography

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MIMIC-BP: A curated dataset for blood pressure estimation.

Scientific data
Blood pressure (BP) is one of the most prominent indicators of potential cardiovascular disorders. Traditionally, BP measurement relies on inflatable cuffs, which is inconvenient and limit the acquisition of such important health-related information ...

Exploring ChatGPT's potential in ECG interpretation and outcome prediction in emergency department.

The American journal of emergency medicine
BACKGROUND: Approximately 20 % of emergency department (ED) visits involve cardiovascular symptoms. While ECGs are crucial for diagnosing serious conditions, interpretation accuracy varies among emergency physicians. Artificial intelligence (AI), suc...

Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. ...

FlexPoints: Efficient electrocardiogram signal compression for machine learning.

Journal of electrocardiology
The electrocardiogram (ECG) stands out as one of the most frequently used medical tests, playing a crucial role in the accurate diagnosis and treatment of patients. While ECG devices generate a huge amount of data, only a fraction of it holds valuabl...

A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution-Pooling Method.

Sensors (Basel, Switzerland)
Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power cardiac arrhythmia classifiers owing to their high weight compression rate. However, multi-class classification of ECG signals based on bCNNs is chal...

Precision of artificial intelligence in paediatric cardiology multimodal image interpretation.

Cardiology in the young
Multimodal imaging is crucial for diagnosis and treatment in paediatric cardiology. However, the proficiency of artificial intelligence chatbots, like ChatGPT-4, in interpreting these images has not been assessed. This cross-sectional study evaluates...

Automated arrhythmia classification based on a pyramid dense connectivity layer and BiLSTM.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundDeep neural networks (DNNs) have recently been significantly applied to automatic arrhythmia classification. However, their classification accuracy still has room for improvement.ObjectivesThe aim of this study is to address the existing li...

Deep learning hybrid model ECG classification using AlexNet and parallel dual branch fusion network model.

Scientific reports
Cardiovascular diseases are a cause of death making it crucial to accurately diagnose them. Electrocardiography plays a role in detecting heart issues such as heart attacks, bundle branch blocks and irregular heart rhythms. Manual analysis of ECGs is...

ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors.

IEEE journal of biomedical and health informatics
Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological signals can be used for security applications to achieve continuous authentication and user convenie...

Federated Learning With Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification.

IEEE journal of biomedical and health informatics
In response to increasing data privacy regulations, this work examines the use of federated learning for deep residual networks to diagnose cardiac abnormalities from electrocardiogram (ECG) data. This approach allows medical institutions to collabor...