Constrained transformer network for ECG signal processing and arrhythmia classification

Constrained transformer network for ECG signal processing and arrhythmia classification. Che, C., Zhang, P., Zhu, M., Qu, Y., & Jin, B. BMC Medical Informatics and Decision Making, 21(1):184, June, 2021.

Paper doi abstract bibtex

Heart disease diagnosis is a challenging task and it is important to explore useful information from the massive amount of electrocardiogram (ECG) records of patients. The high-precision diagnostic identification of ECG can save clinicians and cardiologists considerable time while helping reduce the possibility of misdiagnosis at the same time.Currently, some deep learning-based methods can effectively perform feature selection and classification prediction, reducing the consumption of manpower.

@article{che_constrained_2021,
	title = {Constrained transformer network for {ECG} signal processing and arrhythmia classification},
	volume = {21},
	issn = {1472-6947},
	url = {https://doi.org/10.1186/s12911-021-01546-2},
	doi = {10.1186/s12911-021-01546-2},
	abstract = {Heart disease diagnosis is a challenging task and it is important to explore useful information from the massive amount of electrocardiogram (ECG) records of patients. The high-precision diagnostic identification of ECG can save clinicians and cardiologists considerable time while helping reduce the possibility of misdiagnosis at the same time.Currently, some deep learning-based methods can effectively perform feature selection and classification prediction, reducing the consumption of manpower.},
	number = {1},
	urldate = {2023-03-12},
	journal = {BMC Medical Informatics and Decision Making},
	author = {Che, Chao and Zhang, Peiliang and Zhu, Min and Qu, Yue and Jin, Bo},
	month = jun,
	year = {2021},
	keywords = {CNNs, ECG signal, Link constraints, Transformer},
	pages = {184},
}

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