About: An Advanced Two-Step DNN-Based Framework for Arrhythmia Detection

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	An Advanced Two-Step DNN-Based Framework for Arrhythmia Detection
Creator	Wang, Hua Le Sun, Zhang, Yanchun He, Jinyuan Rong, Jia
Source	PMC
abstract	Heart arrhythmia is a severe heart problem. Automated heartbeat classification provides a cost-effective screening for heart arrhythmia and allows at-risk patients to receive timely treatments, which is a highly demanded but challenging task. Recent works have brought visible improvements to this area, but to identify the problematic supraventricular ectopic (S-type) heartbeats is still a bottleneck in most existing studies. This paper presents a two-step DNN-based framework to identify arrhythmia-related heartbeats. In the first step, a deep dual-channel convolutional neural network (DDCNN) is proposed to classify all heartbeat classes, except for the normal and S-type heartbeats. In the second stage, a central-towards LSTM supportive model (CLSM) is specially designed to distinguish S-type heartbeats from the normal ones. By processing heart rhythms in central-towards directions, CLSM learns and abstracts hidden temporal information between a heartbeat and its neighbors to reveal the deep differences between the two heartbeat types. As an improvement, we also propose a rule-based data augmentation method to solve the training data imbalance problem. The proposed framework is evaluated over three real-world ECG databases. The results show that our method outperforms the baselines in most evaluation metrics.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47436-2_32
has license	no-cc
sha1sum (hex)	0015cd5eff53d9504a5e2aa47756e31579d3dbe0
schema:url	https://doi.org/10.1007/978-3-030-47436-2_32
resource representing a document's title	An Advanced Two-Step DNN-Based Framework for Arrhythmia Detection
has PubMed Central identifier	PMC7206245
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:0015cd5eff53d9504a5e2aa47756e31579d3dbe0#body_text
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