Classification of Cardiac Abnormalities From ECG Signals Using SE-ResNet

Zhu, Zhaowei; Wang, Han; Zhao, Tingting; Guo, Yangming; Xu, Zhihong; Liu, Zhuo; Liu, Siqi; Lan, Xiang; Sun, Xingzhi; Feng, Mengling

doi:10.22489/cinc.2020.281

Cited by 25 publications

(20 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various teams that participated in the Physionet/Challenge considered the deep learning approach [ 27 , 28 , 29 , 30 ], showing a particular interest in this methodology. For example, the team with the highest score [ 27 ] considered both raw ECG data and ECG features extracted from ECG signals, including age and gender.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics

2021

View full text Add to dashboard Cite

The main objective of this study is to propose relatively simple techniques for the automatic diagnosis of electrocardiogram (ECG) signals based on a classical rule-based method and a convolutional deep learning architecture. The validation task was performed in the framework of the PhysioNet/Computing in Cardiology Challenge 2020, where seven databases consisting of 66,361 recordings with 12-lead ECGs were considered for training, validation and test sets. A total of 24 different diagnostic classes are considered in the entire training set. The rule-based method uses morphological and time-frequency ECG descriptors that are defined for each diagnostic label. These rules are extracted from the knowledge base of a cardiologist or from a textbook, with no direct learning procedure in the first phase, whereas a refinement was tested in the second phase. The deep learning method considers both raw ECG and median beat signals. These data are processed via continuous wavelet transform analysis, obtaining a time-frequency domain representation, with the generation of specific images (ECG scalograms). These images are then used for the training of a convolutional neural network based on GoogLeNet topology for ECG diagnostic classification. Cross-validation evaluation was performed for testing purposes. A total of 217 teams submitted 1395 algorithms during the Challenge. The diagnostic accuracy of our algorithm produced a challenge validation score of 0.325 (CPU time = 35 min) for the rule-based method, and a 0.426 (CPU time = 1664 min) for the deep learning method, which resulted in our team attaining 12th place in the competition.

show abstract

Section: Resultsmentioning

confidence: 99%

“…In [ 29 ], wavelet analysis and a convolutional network were used for each single lead, and a single output label was obtained, reducing the diagnostic categories to the individual and the most frequent combinations. In [ 30 ], the authors combined a rule-based model and a squeeze-and-excitation network.…”

Section: Resultsmentioning

confidence: 99%

Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics

2021

View full text Add to dashboard Cite

show abstract

“…Data distributions can be unified and the influence of noise and outliers can be alleviated through normalization. Other signal processing techniques such as zeropadding (Natarajan et al, 2020), median filters (Hsu et al, 2020), and wavelet transformation denoising (Zhu et al, 2020) can also be used.…”

Section: Signal Processingmentioning

confidence: 99%

“…This is also an effective method to avoid overfitting. Common data augmentation methods in Challenge 2020 included the introduction of external data (Bos et al, 2020;Zhu et al, 2020), addition of noise (Chen et al, 2020;Weber et al, 2020), and random cropping (Duan et al, 2020a;Weber et al, 2020). All these methods enlarged the size of the training data.…”

Section: Data Augmentationmentioning

confidence: 99%

“…We notice that the three highest-ranking teams used the model ensemble (Natarajan et al, 2020;Zhao et al, 2020;Zhu et al, 2020), but only 14 out of 41 teams employed this strategy. The results are not expected, and we believe that this is because the model ensemble can help improve the single model, whereas it is less meaningful to compare among different teams.…”

Section: Model Ensemblementioning

confidence: 99%

See 1 more Smart Citation

Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020

et al. 2022

View full text Add to dashboard Cite

Cardiovascular diseases (CVDs) are one of the most fatal disease groups worldwide. Electrocardiogram (ECG) is a widely used tool for automatically detecting cardiac abnormalities, thereby helping to control and manage CVDs. To encourage more multidisciplinary researches, PhysioNet/Computing in Cardiology Challenge 2020 (Challenge 2020) provided a public platform involving multi-center databases and automatic evaluations for ECG classification tasks. As a result, 41 teams successfully submitted their solutions and were qualified for rankings. Although Challenge 2020 was a success, there has been no in-depth methodological meta-analysis of these solutions, making it difficult for researchers to benefit from the solutions and results. In this study, we aim to systematically review the 41 solutions in terms of data processing, feature engineering, model architecture, and training strategy. For each perspective, we visualize and statistically analyze the effectiveness of the common techniques, and discuss the methodological advantages and disadvantages. Finally, we summarize five practical lessons based on the aforementioned analysis: (1) Data augmentation should be employed and adapted to specific scenarios; (2) Combining different features can improve performance; (3) A hybrid design of different types of deep neural networks (DNNs) is better than using a single type; (4) The use of end-to-end architectures should depend on the task being solved; (5) Multiple models are better than one. We expect that our meta-analysis will help accelerate the research related to ECG classification based on machine-learning models.

show abstract

Multi-label Anomaly Classification Based on Electrocardiogram

Li¹,

Sun²

2021

Health Information Science

View full text Add to dashboard Cite

Classification of Cardiac Abnormalities From ECG Signals Using SE-ResNet

Cited by 25 publications

References 9 publications

Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics

Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics

Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020

Multi-label Anomaly Classification Based on Electrocardiogram

Contact Info

Product

Resources

About