Multi-ECGNet for ECG Arrythmia Multi-Label Classification

Cai, Junxian; Sun, Weiwei; Guan, Jianfeng; You, Ilsun

doi:10.1109/access.2020.3001284

Cited by 44 publications

(12 citation statements)

References 14 publications

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“…The HF-challenge multilabel dataset is an ECG smart competition organized by the Tianchi platform [ 23 ]. The preliminary data contain 24106 records in the training set and 8036 in the test set A (testA), each record has 8 leads (mainly I, II and six limb lead V1∼V6), the length is 10 seconds, a total of 55 categories, the sampling rate is 500 Hz, and the unit voltage is 4.88 microvolts.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Wang et al [ 22 ] proposed an arrhythmia detection method based on the multiresolution representation of ECG signals by taking four different deep neural networks as four channel models for ECG vector representations learning and finally performed 34 kinds of ECG classification on a multilabel HF-challenge dataset with the F 1 score of 92.38%, all higher than the results of individual channels. Cai et al [ 23 ] developed multi-ECGNet to identify patients with multiple cardiac diseases at the same time, with an F 1 score of 86.3% in identifying 55 arrhythmia classifications. Sun et al [ 24 ] proposed a novel ensemble multilabel classification model to perform 7 kinds of multilabel ECG classification on the CCDD, and the final F 1 score was 75.2%.…”

Section: Related Workmentioning

confidence: 99%

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Multiscale Residual Network Based on Channel Spatial Attention Mechanism for Multilabel ECG Classification

Wang

et al. 2021

Journal of Healthcare Engineering

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Automatic classification of ECG is very important for early prevention and auxiliary diagnosis of cardiovascular disease patients. In recent years, many studies based on ECG have achieved good results, most of which are based on single-label problems; one record corresponds to one label. However, in actual clinical applications, an ECG record may contain multiple diseases at the same time. Therefore, it is very important to study the multilabel ECG classification. In this paper, a multiscale residual deep neural network CSA-MResNet model based on the channel spatial attention mechanism is proposed. Firstly, the residual network is integrated into a multiscale manner to obtain the characteristics of ECG data at different scales and then increase the channel spatial attention mechanism to better focus on more important channels and more important ECG data fragments. Finally, the model is used to classify multilabel in large databases. The experimental results on the multilabel CCDD show that the CSA-MResNet model has an average F1 score of 88.2% when the multilabel classification of 9 ECGs is performed. Compared with the benchmark model, the F1 score of CSA-MResNet in the multilabel ECG classification increased by up to 1.7%. And, in the model verification on another database HF-challenge, the final average F1 score is 85.8%. Compared with the state-of-the-art methods, CSA-MResNet can help cardiologists perform early-stage rapid screening of ECG and has a certain generalization performance, providing a feasible analysis method for multilabel ECG classification.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multiscale Residual Network Based on Channel Spatial Attention Mechanism for Multilabel ECG Classification

Wang

et al. 2021

Journal of Healthcare Engineering

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“…For some rare types of cardiac abnormalities, it is difficult to collect such a large ECG dataset with annotation. In following-up works, it warrants to study further how algorithm adaption method ( 46 ) and other neural network architectures ( 47 – 49 ) help to deal with multi-labeled data directly and reduce time-demand for training. Moreover, unsupervised and semi-supervised learning can also be tested for addressing the lack of enough annotations.…”

Section: Limitation Of Studymentioning

confidence: 99%

Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks

Zhang

2021

Front. Cardiovasc. Med.

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Introduction: Electrocardiograms (ECG) provide information about the electrical activity of the heart, which is useful for diagnosing abnormal cardiac functions such as arrhythmias. Recently, several algorithms based on advanced structures of neural networks have been proposed for auto-detecting cardiac arrhythmias, but their performance still needs to be further improved. This study aimed to develop an auto-detection algorithm, which extracts valid features from 12-lead ECG for classifying multiple types of cardiac states.Method: The proposed algorithm consists of the following components: (i) a preprocessing component that utilizes the frame blocking method to split an ECG recording into frames with a uniform length for all considered ECG recordings; and (ii) a binary classifier based on ResNet, which is combined with the attention-based bidirectional long-short term memory model.Result: The developed algorithm was trained and tested on ECG data of nine types of cardiac states, fulfilling a task of multi-label classification. It achieved an averaged F1-score and area under the curve at 0.908 and 0.974, respectively.Conclusion: The frame blocking and bidirectional long-short term memory model represented an improved algorithm compared with others in the literature for auto-detecting and classifying multi-types of cardiac abnormalities.

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“…Data Processing. ECG signals processing includes noise elimination, baseline drift, and data enhancement [10,11]. The empirical decomposition algorithm (EDM) is used to decompose the ECG signal into 10 intrinsic mode functions (IMF) [12].…”

Section: Sensor Geometrymentioning

confidence: 99%

In‐Body Electromagnetic Sensor Combined with AI‐Enhanced Electrocardiography for Pacemaker Working Status Telemonitoring

Lü

Ding

et al. 2021

Journal of Sensors

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This paper describes the design and implementation of an in-body electromagnetic sensor for patients with implanted pacemakers. The sensor can either be mounted on myocardial tissue and monitor the electrocardiography (ECG) with contact electrodes or implanted under the skin and monitor the ECG with coaxial leads. A 16-bit high-resolution analog front-end (AFE) and an energy-efficient 32-bit CPU are used for instantaneous ECG recording. Wireless data transmission between the sensor and clinician’s computer is achieved by an embedded low-power Bluetooth transmitter. In order to automatically recognize the working status of the pacemaker and alarm the episodes of arrhythmias caused by pacemaker malfunctions, pacing mode classification and fault diagnosis on the recorded ECG were achieved based on an AI algorithm, i.e., a resource allocation network (RAN). A prototype of the sensor was implemented on a human torso, and the in vitro test results prove that the sensor can work properly for the 1-4-meter transmission range.

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Multi-ECGNet for ECG Arrythmia Multi-Label Classification

Cited by 44 publications

References 14 publications

Multiscale Residual Network Based on Channel Spatial Attention Mechanism for Multilabel ECG Classification

Multiscale Residual Network Based on Channel Spatial Attention Mechanism for Multilabel ECG Classification

Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks

In‐Body Electromagnetic Sensor Combined with AI‐Enhanced Electrocardiography for Pacemaker Working Status Telemonitoring

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