Towards High Generalization Performance on Electrocardiogram Classification

Han, Hyeongrok; Park, Seongjae; Min, Shungeng; Choi, Hyun-Soo; Kim, Eunji; Kim, Hyunki; Park, Sangha; Kim, Jinkook; Park, Junsang; An, Junho; Lee, Kwanglo; Jeong, Wonsun; Chon, Sangil; Ha, Kwon-Woo; Han, Myungkyu; Yoon, Sungroh

doi:10.23919/cinc53138.2021.9662737

Cited by 11 publications

(8 citation statements)

References 12 publications

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“…Compared with our previous solution (Xu et al 2021), in this work we introduce several new techniques to handle the summarized challenges in ECG classification. Firstly, inspired by Han et al (2021), we evaluated the generalizability of the developed models with a dataset-wise cross-evaluation approach, which is not evaluation on the same distribution. The averaged cross-evaluation score is a good indicator of model performance on an unknown distribution.…”

Section: Discussionmentioning

confidence: 99%

Section: Dataset-wise Cross-evaluationmentioning

confidence: 99%

“…To evaluate the generalization performance on the distribution of interest, previous works tried to separate the distribution of interest from the given data as an offline validation set (Han et al 2021, Li et al 2021. Inspired by Han et al (2021), we adopted a dataset-wise cross-evaluation approach. For the six datasets considered in our training data, we used five to develop the model and the other as the offline validation set to validate the model performance.…”

Section: Dataset-wise Cross-evaluationmentioning

confidence: 99%

“…with ISIBrno-AIMT(Nejedly et al 2021), NIMA(Han et al 2021) and DSAIL_SNU(Wickramasinghe and Athif 2021), which are also the leading solutions in the Physionet/Computing in Cardiology Challenge 2021. Our method achieved the challenge metric scores of 0.57, 0.59, 0.59, 0.58, 0.57 on 12-, 6-, 4-, 3-and 2-lead combinations, respectively, and achieved the best performance on 6-, 4-and 3-lead versions and the averaged score.…”

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confidence: 99%

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Abnormality classification from electrocardiograms with various lead combinations

Xu¹,

Guo²,

Zhao³

et al. 2022

Physiol. Meas.

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As cardiovascular diseases have been one of the leading causes of death, early and accurate diagnosis of cardiac abnormalities with less cost becomes particularly important. Given the electrocardiogram (ECG) datasets from multiple sources, there exist many challenges to develop the generalized models that can identify multiple types of cardiac abnormalities from both 12-lead ECG signals and reduced-lead ECG signals. In this study, our objective is to build robust models which can accurately classify 30 types of abnormalities from various lead combinations of ECG signals. Given the challenges of this problem, we proposed a framework for building robust models for ECG signal classification. Firstly, a pre-processing workflow was adopted for each ECG dataset to mitigate the problem of data divergence. Secondly, to capture the lead-wise relations, we used a squeeze-and-excitation deep residual network (SE_ResNet) as our base model. Thirdly, we proposed the cross relabeling strategy and applied the sign-augmented loss function to tackle the corrupted labels in the data. Furthermore, we utilized a pos-if-any-pos ensemble strategy and a dataset-wise cross evaluation strategy to handle the uncertainty of the data distribution in the application. In the Physionet/Computing in Cardiology Challenge 2021, our approach achieved the challenge metric scores of 0.57, 0.59, 0.59, 0.58, 0.57 on 12, 6, 4, 3, 2 lead versions and an averaged challenge metric score of 0.58 over all the lead versions.Using the proposed framework, we developed the models from several large datasets with sufficiently labeled abnormalities. Our models could identify 30 ECG abnormalities accurately based on various lead combinations of ECG signals. The performance on hidden test data demonstrated the effectiveness of the proposed approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Dataset-wise Cross-evaluationmentioning

confidence: 99%

Section: Dataset-wise Cross-evaluationmentioning

confidence: 99%

mentioning

confidence: 99%

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Abnormality classification from electrocardiograms with various lead combinations

Xu¹,

Guo²,

Zhao³

et al. 2022

Physiol. Meas.

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“…These are the methods that are developed in a similar way to that of our proposed algorithm. The proposed algorithm is also compared with the Challenge's top entrants, who have developed their algorithm using end-to-end deep learning techniques (Han et al 2021, Nejedly et al 2021, Wickramasinghe and Athif 2021.…”

Section: Discussionmentioning

confidence: 99%

Application of Fourier-Bessel expansion and LSTM on multi-lead ECG for cardiac abnormalities identification

Sawant¹,

Patidar²

2022

Physiol. Meas.

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Objective: The availability of online electrocardiogram (ECG) repositories can aid researchers in developing automated cardiac abnormality diagnostic systems. Using such ECG repositories, this study aims to develop an algorithm that can assist physicians in diagnosing cardiac abnormalities. Approach: The PhysioNet/CinC 2021 Challenge has opened the venues for creating benchmark algorithms using standard and relatively diverse 12-lead ECG datasets. This work attempts to create a new machine learning approach for identifying common cardiac abnormalities using an ensemble-based classification with two models resulting from two different feature sets. The first feature set extracts RR variability based information by deploying Fourier-Bessel (FB) expansion. The second feature set is composed of time- and frequency-domains-based hand-crafted features. Two long short-term memory (LSTM)-based classifiers are trained using these two feature sets as input to categorize ECG signals. Predictions from these two models are fused to arrive at a final medical decision that improves the multi-label classification of the given ECG signals into twenty-six categories. \\ Main results: We participated in the George B. Moody Physionet Challenge 2021 as team 'Medics', and the proposed methodology was evaluated for all five lead combinations. The challenge scoring metrics obtained on the test data for twelve-, six-, four-, three-, and two-leads combinations are 0.360, 0.368, 0.376, 0.323, and 0.381, respectively. The proposed methodology was ranked 11th among all the follow-up entries of the Challenge. Significance: The obtained results of the proposed method justify the use of an ensemble classifier developed using the extracted feature sets for devising a diagnostic system for detecting and identifying common cardiac problems.

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Multi-channel masked autoencoder and comprehensive evaluations for reconstructing 12-lead ECG from arbitrary single-lead ECG

Chen,

Wu,

Liu

et al. 2024

npj Cardiovasc Health

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Electrocardiogram (ECG) has emerged as a widely accepted diagnostic instrument for cardiovascular diseases (CVD). The standard clinical 12-lead ECG configuration causes considerable inconvenience and discomfort, while wearable devices offers a more practical alternative. To reduce information gap between 12-lead ECG and single-lead ECG, this study proposes a multi-channel masked autoencoder (MCMA) for reconstructing 12-Lead ECG from arbitrary single-lead ECG, and a comprehensive evaluation benchmark, ECGGenEval, encompass the signal-level, feature-level, and diagnostic-level evaluations. MCMA can achieve the state-of-the-art performance. In the signal-level evaluation, the mean square errors of 0.0175 and 0.0654, Pearson correlation coefficients of 0.7772 and 0.7287. In the feature-level evaluation, the average standard deviation of the mean heart rate across the generated 12-lead ECG is 1.0481, the coefficient of variation is 1.58%, and the range is 3.2874. In the diagnostic-level evaluation, the average F1-score with two generated 12-lead ECG from different single-lead ECG are 0.8233 and 0.8410.

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Towards High Generalization Performance on Electrocardiogram Classification

Cited by 11 publications

References 12 publications

Abnormality classification from electrocardiograms with various lead combinations

Abnormality classification from electrocardiograms with various lead combinations

Application of Fourier-Bessel expansion and LSTM on multi-lead ECG for cardiac abnormalities identification

Multi-channel masked autoencoder and comprehensive evaluations for reconstructing 12-lead ECG from arbitrary single-lead ECG

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