Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings

Zhang, Peng; Ma, Chenbin; Sun, Yangyang; Fan, Guangda; Song, Fan; Feng, Youdan; Zhang, Guanglei

doi:10.1016/j.compbiomed.2021.104880

Cited by 26 publications

(10 citation statements)

References 36 publications

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“…Finally, Ma et al [ 208 ] introduced an improved dilated causal CNN to classify ECG signals from the MIT-BIH Atrial Fibrillation Database, achieving a high model performance (sensitivity 98.79%, specificity 99.04%, and accuracy 98.65%), whereas Zhang et al [ 238 ] tested (sensitivity 99.65%, specificity 99.98%, and accuracy 99.84%) a CNN for AF detection on ECG signals from 2 major public databases.…”

Section: Resultsmentioning

confidence: 99%

“…Another study by Dai et al [195] proposed a CNN for CVD diagnosis using different intervals of ECG signals from the PTB Diagnostic ECG Database and achieved accuracies of 99.59%, 99.80%, and 99.84% for 1-, 2-, and 3-second ECG segments, respectively. Finally, Ma et al [208] introduced an improved dilated causal CNN to classify ECG signals from the MIT-BIH Atrial Fibrillation Database, achieving a high model performance (sensitivity 98.79%, specificity 99.04%, and accuracy 98.65%), whereas Zhang et al [238] tested (sensitivity 99.65%, specificity 99.98%, and accuracy 99.84%) a CNN for AF detection on ECG signals from 2 major public databases.…”

Section: Cvd Diagnosismentioning

confidence: 99%

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State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Petmezas¹,

Stefanopoulos²,

Kilintzis³

et al. 2022

JMIR Med Inform

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Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.

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Section: Resultsmentioning

confidence: 99%

Section: Cvd Diagnosismentioning

confidence: 99%

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Petmezas¹,

Stefanopoulos²,

Kilintzis³

et al. 2022

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“… 2-Lead ECG DL, ML Patient-independent Cardiovascular diseases Wu et al [ 86 ] 2021 Wuhan (China) Journal article MIT–BIH arrhythmia [ 48 ] (48 ECG records) A 12-layer deep one-dimensional convolutional neural network is proposed for classification of the five micro-classes of heartbeat types 2-Lead ECG CNN, DL, Ensemble classifiers, ML Patient-independent Arrhythmia Xiong et al [ 87 ] 2021 Baoding (China) Journal article Physikalisch-Technische-Bundesanstalt (PTB) [ 29 ] database (290 subjects) The development of a multi-lead MI localization approach based on the densely connected convolutional network. 12-Lead ECG CNN Patient-independent Myocardial infarction Zhang et al [ 88 ] 2021 Beijing (China) Journal article CPSC 2018 dataset [ 83 ] (3,178 females and 3,699 males) A deep learning classification method, namely, a global hybrid multi-scale convolutional neural network is proposed, to implement binary classification for AF detection. 1-Lead ECG CNN, DL Patient-independent Atrial fibrillation Zhang et al [ 89 ] 2021 Jinjiang (China) Journal article MIT–BIH arrhythmia [ 48 ] database (48 ECG records) This paper proposed a high-accuracy ECG arrhythmia classification method based on convolutional neural networks.…”

Section: Methodsmentioning

confidence: 99%

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Denysyuk¹,

Pinto²,

Silva³

et al. 2023

Heliyon

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“…Relationship administration in the supply chain has also been impacted by the current epidemic. According to Zhang et al (2021b), social contact between supply chain participants tends to be shallow and infrequent. As a consequence of these restrictions on how people may engage with one another, information is often vague or unclear.…”

Section: Tourism Small-and Medium-sized Enterprisesmentioning

confidence: 99%

Financial impact of cost of capital on tourism-based SMEs in COVID-19: implications for tourism disruption mitigation

Song

Yan

Yu³

et al. 2022

Environ Sci Pollut Res

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Opportunities for funding Tourism SMEs are emerging globally due to the expansion of tourism sector. However, it is still being determined how these financial arrangements will be controlled at more significant sizes equitably. In the contemporary period, E7 economy is deficient in producing the financial resources to ensure the availability of funds for the acquisition of funds for tourism-based SMEs. However, this research tested the empirical position of cost of debt in E-7 economies during COVID-19 crises. Study findings have shown significant outcomes between the constructs. The variation of conditions, structural uncertainty, transection systems, and variation in support by the financial institution for tourism-based SMEs are the main reasons that lessen borrowing and lending system of funds, from banks to SMEs. However, theorists must revisit the transaction system of debt financing for SMEs. Policymakers are suggested to develop viable and SME system-friendly policies to finance through debt capital from the banks in the time of structural imposed crises, like COVID-19.

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Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings

Cited by 26 publications

References 36 publications

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Financial impact of cost of capital on tourism-based SMEs in COVID-19: implications for tourism disruption mitigation

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