Detecting atrial fibrillation by deep convolutional neural networks

Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

doi:10.1016/j.compbiomed.2017.12.007

Cited by 295 publications

(185 citation statements)

References 29 publications

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“…The study showed that AI can determine hyperkalemia in a non-invasive way without a blood test and can contribute to the monitoring of abnormalities of electrolytes in patients with chronic kidney disease. In addition, studies on the application of AI to detect and classify arrhythmias such as detecting atrial fibrillation [20] or classifying myocardial infarction [21] using 12-electrode ECG databases are regularly carried out and the results demonstrate an efficacy as high as or higher than that of a human cardiologist.…”

Section: Recent Cases Of Application Of Ai To Biosignals In Medicinementioning

confidence: 99%

Discovering hidden information in biosignals from patients using artificial intelligence

Yoon

Jang

Choi

et al. 2020

Korean J Anesthesiol

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Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.

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Section: Recent Cases Of Application Of Ai To Biosignals In Medicinementioning

confidence: 99%

Discovering hidden information in biosignals from patients using artificial intelligence

Yoon

Jang

Choi

et al. 2020

Korean J Anesthesiol

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“…Many of the algorithms (Moody and Mark, 1983; Cerutti et al, 1997; Tateno and Glass, 2001; Logan and Healey, 2005; Couceiro et al, 2008; Babaeizadeh et al, 2009; Dash et al, 2009; Huang et al, 2011; Lake and Moorman, 2011; Asgari et al, 2015; Ladavich and Ghoraani, 2015; García et al, 2016; Xia et al, 2018) were chosen for comparison as the best performing results for various methods.…”

Section: Resultsmentioning

confidence: 99%

Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks

et al. 2018

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Atrial fibrillation (AF) is the most common cardiac arrhythmias causing morbidity and mortality. AF may appear as episodes of very short (i.e., proximal AF) or sustained duration (i.e., persistent AF), either form of which causes irregular ventricular excitations that affect the global function of the heart. It is an unmet challenge for early and automatic detection of AF, limiting efficient treatment strategies for AF. In this study, we developed a new method based on continuous wavelet transform and 2D convolutional neural networks (CNNs) to detect AF episodes. The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities. Then a 2D CNN was trained to improve AF detection performance. The MIT-BIH Atrial Fibrillation Database was used for evaluating the algorithm. The efficacy of the proposed method was compared with those of some existing methods, most of which implemented the same dataset. The newly developed algorithm using CNNs achieved 99.41, 98.91, 99.39, and 99.23% for the sensitivity, specificity, positive predictive value, and overall accuracy (ACC) respectively. As the proposed algorithm targets the time-frequency feature of ECG signals rather than isolated atrial or ventricular activity, it has the ability to detect AF episodes for using just five beats, suggesting practical applications in the future.

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“…Second, each ECG signal of AFBD is divided into 5-s segments and each segment is labeled based on a threshold parameter, p. When the percentage of annotated AF beats of the 5-s segment is greater than or equal to p, we considered it as AF, otherwise non-AF arrhythmia. Similar to previous reported studies in [13], [8], we selected p = 50%. It is worth noticing that no noise removal approaches have been applied to the ECG signals.…”

Section: Dataset and Data Preparationmentioning

confidence: 99%

“…Similar to other deep learning-based AF detectors [13], [18], the deep neural network part of our model takes a 2-D representation with the wavelet power spectrum of the ECG segment. They employ 2-D convolution operators on the entire input, while our method applies 1-D convolution operators to each frequency vector (i.e., at each time step) of the given the spectrograms obtained from each segment, and feeds the output of the 1-D convolutional layers to long shortterm memory units to capture dependencies between each frequency vector.…”

Section: Deep Recurrent Convolutioal Neural Network (Rcnn)mentioning

confidence: 99%

ECGNET: Learning Where to Attend for Detection of Atrial Fibrillation with Deep Visual Attention

Mousavi

Afghah

Razi

et al. 2019

2019 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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The complexity of the patterns associated with Atrial Fibrillation (AF) and the high level of noise affecting these patterns have significantly limited the current signal processing and shallow machine learning approaches to get accurate AF detection results. Deep neural networks have shown to be very powerful to learn the non-linear patterns in the data. While a deep learning approach attempts to learn complex pattern related to the presence of AF in the ECG, they can benefit from knowing which parts of the signal is more important to focus during learning. In this paper, we introduce a two-channel deep neural network to more accurately detect AF presented in the ECG signal. The first channel takes in a preprocessed ECG signal and automatically learns where to attend for detection of AF. The second channel simultaneously takes in the preprocessed ECG signal to consider all features of entire signals. The model shows via visualization that what parts of the given ECG signal are important to attend while trying to detect atrial fibrillation. In addition, this combination significantly improves the performance of the atrial fibrillation detection (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy of 99.40% on the MIT-BIH atrial fibrillation database with 5-s ECG segments.) 1 .

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Detecting atrial fibrillation by deep convolutional neural networks

Cited by 295 publications

References 29 publications

Discovering hidden information in biosignals from patients using artificial intelligence

Discovering hidden information in biosignals from patients using artificial intelligence

Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks

ECGNET: Learning Where to Attend for Detection of Atrial Fibrillation with Deep Visual Attention

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