Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings.

Journal: Physiological measurement

Published Date: Jun 4, 2019

Abstract

OBJECTIVE: We aim to combine deep neural networks and engineered features (hand-crafted features based on medical domain knowledge) for cardiac arrhythmia detection from short single-lead ECG recordings.

Authors

Shenda Hong

National Institute of Health Data Science at Peking University, Peking University, 100871 Beijing, China.
Yuxi Zhou
Meng Wu
Junyuan Shang
Qingyun Wang

Department of Dynamics and Control, Beihang University, Beijing, 100191, China. nmqingyun@163.com.
Hongyan Li

Department of Psychogeriatrics, Kangci Hospital of Jiaxing, Tongxiang, Zhejiang, China.
Junqing Xie

Keywords

Algorithms Arrhythmias, Cardiac Deep Learning Electrocardiography Humans Neural Networks, Computer

External Resources

View on PubMed Access via DOI PubMed (30943458)

Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings.

Abstract

Authors

Keywords

External Resources

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