Heartbeat-based end-to-end classification of arrhythmias / 南方医科大学学报
Journal of Southern Medical University
;
(12): 1071-1077, 2019.
Article
in Chinese
| WPRIM
| ID: wpr-773489
ABSTRACT
OBJECTIVE@#We propose a heartbeat-based end-to-end classification of arrhythmias to improve the classification performance for supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB).@*METHODS@#The ECG signals were preprocessed by heartbeat segmentation and heartbeat alignment. An arrhythmia classifier was constructed based on convolutional neural network, and the proposed loss function was used to train the classifier.@*RESULTS@#The proposed algorithm was verified on MIT-BIH arrhythmia database. The AUC of the proposed loss function for SVEB and VEB reached 0.77 and 0.98, respectively. With the first 5 min segment as the local data, the diagnostic sensitivities for SVEB and VEB were 78.28% and 98.88%, respectively; when 0, 50, 100, and 150 samples were used as the local data, the diagnostic sensitivities for SVEB and VEB reached 82.25% and 93.23%, respectively.@*CONCLUSIONS@#The proposed method effectively reduces the negative impact of class-imbalance and improves the diagnostic sensitivities for SVEB and VEB, and thus provides a new solution for automatic arrhythmia classification.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Arrhythmias, Cardiac
/
Algorithms
/
Classification
/
Neural Networks, Computer
/
Ventricular Premature Complexes
/
Diagnosis
/
Electrocardiography
/
Heart Rate
Type of study:
Diagnostic study
Limits:
Humans
Language:
Chinese
Journal:
Journal of Southern Medical University
Year:
2019
Type:
Article
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