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A comprehensive survey on applications of transformers for deep learning tasks
Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …
mechanism to capture contextual relationships within sequential data. Unlike traditional …
[HTML][HTML] Comprehensive survey of computational ECG analysis: Databases, methods and applications
Electrocardiogram (ECG) recordings are indicative for the state of the human heart.
Automatic analysis of these recordings can be performed using various computational …
Automatic analysis of these recordings can be performed using various computational …
Deep learning for ECG analysis: Benchmarks and insights from PTB-XL
Electrocardiography (ECG) is a very common, non-invasive diagnostic procedure and its
interpretation is increasingly supported by algorithms. The progress in the field of automatic …
interpretation is increasingly supported by algorithms. The progress in the field of automatic …
ECG-based machine-learning algorithms for heartbeat classification
Electrocardiogram (ECG) signals represent the electrical activity of the human hearts and
consist of several waveforms (P, QRS, and T). The duration and shape of each waveform …
consist of several waveforms (P, QRS, and T). The duration and shape of each waveform …
Will two do? Varying dimensions in electrocardiography: the PhysioNet/Computing in Cardiology Challenge 2021
The PhysioNet/Computing in Cardiology Challenge 2021 focused on the identification of
cardiac abnormalities from electrocardiograms (ECGs) and assessed the diagnostic …
cardiac abnormalities from electrocardiograms (ECGs) and assessed the diagnostic …
[HTML][HTML] Self-supervised representation learning from 12-lead ECG data
Abstract Clinical 12-lead electrocardiography (ECG) is one of the most widely encountered
kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity …
kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity …
Explainable AI decision model for ECG data of cardiac disorders
Electrocardiogram (ECG) data is used to monitor the electrical activity of the heart. It is
known that ECG data could help in detecting cardiac (heart) abnormalities. AI-enabled …
known that ECG data could help in detecting cardiac (heart) abnormalities. AI-enabled …
Self-supervised contrastive learning for medical time series: A systematic review
Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
PTB-XL+, a comprehensive electrocardiographic feature dataset
Abstract Machine learning (ML) methods for the analysis of electrocardiography (ECG) data
are gaining importance, substantially supported by the release of large public datasets …
are gaining importance, substantially supported by the release of large public datasets …
Clocs: Contrastive learning of cardiac signals across space, time, and patients
The healthcare industry generates troves of unlabelled physiological data. This data can be
exploited via contrastive learning, a self-supervised pre-training method that encourages …
exploited via contrastive learning, a self-supervised pre-training method that encourages …