An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection

Ng, E. Y. K.; Liu, Feifei; Liu, Chengyu; Zhao, Lina; Zhang, Xiangyu; Wu, Xiaoling; Xu, Xiaoyan; Liu, Yulin; Ma, Caiyun; Wei, Shoushui; He, Zhiqiang; Li, Jianqing

doi:10.1166/jmihi.2018.2442

Cited by 427 publications

(212 citation statements)

References 22 publications

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“…Training data were from the 2018 China physiological signal challenge (CSPC-2018) [11] and test data were recorded from a newly developed 12-lead Lenovo Smart ECG vest [12]. All recordings were intercepted the only first 10-s segments.…”

Section: Datamentioning

confidence: 99%

PVC Recognition for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Network

Zhao¹,

Wang²,

Cai³

et al. 2019

Computing in Cardiology Conference (CinC)

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Progress in wearable techniques makes the long-term daily electrocardiogram (ECG) monitoring possible. Premature ventricular contraction (PVC) is one of the most common cardiac arrhythmias. This study proposed a method by combining the modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN). Training data are from the 2018 China physiological signal challenge . The first 10-s ECG waveforms in each recording were transformed into 2-D time-frequency images (frequency range of 0-50 Hz and size of 300 × 100) using MFSWT. A 25-layer CNN structure was constructed, which includes five convolution layers with kernel size of 3×3, five dropout layers, five ReLU layers, five maximum pooling layers with kernel size of 2 × 2, a flatten layer, two fully connected layers, as well as the input and output layers. Test data were recorded from Results showed that, the proposed method achieved a high accuracy of 97.89% for PVC/non-PVC episodes classification, indicating that the combination of MFSWT and CNN provides new insight to accurately identify PVC from the wearable ECG recordings.

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Section: Datamentioning

confidence: 99%

PVC Recognition for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Network

Zhao¹,

Wang²,

Cai³

et al. 2019

Computing in Cardiology Conference (CinC)

Self Cite

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“…In some papers, the signal-to-noise ratio (SNR) is fixed at a certain number of decibels, which is convenient for comparison, but it is difficult to represent the generalization performance of the proposed method. The ground-truth ECG used in this article was derived from ICBEB 2018 [19], and 1,379 high-quality single-lead signals were manually selected from the competition dataset by trained volunteers. These data came from different, random leads in the standard 12-lead ECG.…”

Section: Data Descriptionmentioning

confidence: 99%

A two-stage ECG signal denoising method based on deep convolutional network

Qiu¹,

Cai²,

Yu³

et al. 2020

Preprint

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Abstract-Electrocardiogram (ECG) is an effective and non-invasive indicator for the detection and prevention of arrhythmia. ECG signals are susceptible to noise contamination, which can lead to errors in ECG interpretation. Therefore, ECG pretreatment is important for accurate analysis. In this paper, a method of noise reduction based on deep learning is proposed. The method is divided into two stages, and two corresponding models are formed. In the first stage, a one-dimensional U-net model is designed for ECG signal denoising to eliminate noise as much as possible. The one-dimensional DR-net model in the second stage is used to reconstruct the ECG signal and to correct the waveform distortion caused by noise removal in the first stage. In this paper, the U-net and the DR-net are constructed by the convolution method to achieve end-to-end mapping from noisy ECG signals to clean ECG signals. The ECG data used in this paper are from CPSC2018, and the noise signal is from MIT-BIH Noise Stress Test Database (NSTDB). In the experiment, the improvement in the signal-to-noise ratio imp SNR , the root mean square error decrease de RMSE , and the correlation coefficient P , are used to evaluate the performance of the network. This two-stage method is compared with FCN and U-net alone. The experimental results show that the two-stage noise reduction method can eliminate complex noise in the ECG signal while retaining the characteristic shape of the ECG signal. According to the results, we believe that the proposed method has a good application prospect in clinical practice.

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“…The dataset is from the China Physiological Signal Challenge 2018 [11]. The ECG recordings were collected from 11 hospitals.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Our method can deal with variable-length ECG without any segmentation or padding process. The method is evaluated on the dataset of the China Physiological Signal Challenge 2018 [11], which contains multiple abnormalities such as Atrial fibrillation and Premature ventricular contraction. Our method achieves an average F1-score of 0.889 over 5-fold cross validation, exceeding state-of-the-art methods.…”

Section: Introductionmentioning

confidence: 99%

Detection of First-Degree Atrioventricular Block on Variable-Length Electrocardiogram via a Multimodal Deep Learning Method

Jia¹,

Zhao²,

Hu³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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Automatic detection of first-degree atrioventricular block (I-AVB) from electrocardiogram (ECG) is of great importance in prevention of more severe cardiac diseases. I-AVB is characterized by a prolonged PR interval. However, due to various artifacts and diversity of ECG morphology, existing ECG delineation algorithms is unable to provide robust measurement of the PR interval. Deep neural network is good at extracting high-level feature from ECG waveform, but merely using waveform as input of neural network may aggravate overfitting when lack of I-AVB records. In this paper, we propose a multimodal-input deep learning method to effectively detect I-AVB from 12lead ECG records. We utilize ECG waveform and delineation result as the multimodal input of neural network. Our neural network, mainly composed of convolutional neural network and Long Short-term Memory, is well designed to adapt to variable-length ECG. Our method is evaluated on dataset of CPSC2018, and outperforms the baseline methods in F1 score.

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An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection

Cited by 427 publications

References 22 publications

PVC Recognition for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Network

PVC Recognition for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Network

A two-stage ECG signal denoising method based on deep convolutional network

Detection of First-Degree Atrioventricular Block on Variable-Length Electrocardiogram via a Multimodal Deep Learning Method

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