Version 1
: Received: 21 April 2022 / Approved: 26 April 2022 / Online: 26 April 2022 (11:39:45 CEST)
Version 2
: Received: 29 April 2022 / Approved: 4 May 2022 / Online: 4 May 2022 (12:50:40 CEST)
How to cite:
JiangSi, Q.; Gao, H.; Wu, J. A One-Dimensional Serial ECG Diagnosis Approach Based on Ensemble Deep Learning. Preprints2022, 2022040241. https://doi.org/10.20944/preprints202204.0241.v1
JiangSi, Q.; Gao, H.; Wu, J. A One-Dimensional Serial ECG Diagnosis Approach Based on Ensemble Deep Learning. Preprints 2022, 2022040241. https://doi.org/10.20944/preprints202204.0241.v1
JiangSi, Q.; Gao, H.; Wu, J. A One-Dimensional Serial ECG Diagnosis Approach Based on Ensemble Deep Learning. Preprints2022, 2022040241. https://doi.org/10.20944/preprints202204.0241.v1
APA Style
JiangSi, Q., Gao, H., & Wu, J. (2022). A One-Dimensional Serial ECG Diagnosis Approach Based on Ensemble Deep Learning. Preprints. https://doi.org/10.20944/preprints202204.0241.v1
Chicago/Turabian Style
JiangSi, Q., HaoJun Gao and Jian Wu. 2022 "A One-Dimensional Serial ECG Diagnosis Approach Based on Ensemble Deep Learning" Preprints. https://doi.org/10.20944/preprints202204.0241.v1
Abstract
An electrocardiograph (ECG) reflects the health of the human heart and is used to help diagnose arrhythmia and myocardial infarction(MI) in clinical practice. Early diagnosis of arrhythmia helps implement preventive measures and plays a crucial role in saving a patient's life. With the increasing demand of clinicians for ECG analysis technology, intelligent detection and diagnosis of ECG signals has become a more efficient means to assist physicians in diagnosing cardiovascular diseases. This paper introduces an ECG diagnosis approach based on an ensemble deep learning combination of CNN(convolutional neural network) and SLAP(stacked-long short term memory architecture for prediction) architecture. ECG data is denoised and further divided into single heartbeats to achieve data standardization and sample diversity. Adam optimizer and BCEwithlogitsloss multi-classification loss function were used to enhance the model effect, and the system achieved the classification effect of 99.3% average accuracy, 99.0% F1-value, and 99.2% sensitivity in MIT-BIH standard database classification. It also shows good generalization ability on the Tianchi data set.
Keywords
deep learning; ensemble learning; intelligent detection and diagnosis; multi-classification; preventive measures
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
