Towards Interpretable Arrhythmia Classification With Human-Machine ...
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Sep 18, 2020 · In this paper, we address this deficiency by proposing the first novel interpretable arrhythmia classification approach based on a human-machine collaborative ...
After the AE network is completely trained, Encoder can generate a human-machine collaborative knowledge representation for any input ECG signal. In the second ...
Our approach first employs an AutoEncoder to encode electrocardiogram signals into two parts: hand-encoding knowledge and machine-encoding knowledge. A ...
Duration: 0:58
Posted: Aug 30, 2021
Posted: Aug 30, 2021
Aug 27, 2024 · We propose XDTEncoder, an explainable arrhythmia classification framework that leverages multi-level features to classify arrhythmia heartbeats.
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Our research introduces an innovative approach employing image-based ECG representations and cascading deep neural networks (CDNNs) to enhance arrhythmia ...
Aug 31, 2024 · We propose XDTEncoder, an explainable arrhythmia classification framework that leverages multi-level features to classify arrhythmia heartbeats.
We propose a hybrid heartbeat classification method that combines Transformer and multi branch convolutional neural networks (CNNs).
This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications.
Aug 15, 2022 · This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications.