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
E Merdjanovska, A Rashkovska - Expert Systems with Applications, 2022 - Elsevier
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 …
ECG-based machine-learning algorithms for heartbeat classification
S Aziz, S Ahmed, MS Alouini - Scientific reports, 2021 - nature.com
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 …
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 …
[HTML][HTML] Self-supervised representation learning from 12-lead ECG data
T Mehari, N Strodthoff - Computers in biology and medicine, 2022 - Elsevier
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 …
Clocs: Contrastive learning of cardiac signals across space, time, and patients
D Kiyasseh, T Zhu, DA Clifton - International Conference on …, 2021 - proceedings.mlr.press
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 …
Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records
Background and objective Cardiac arrhythmia, which is an abnormal heart rhythm, is a
common clinical problem in cardiology. Detection of arrhythmia on an extended duration …
common clinical problem in cardiology. Detection of arrhythmia on an extended duration …
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 …