Prediction to Atrial Fibrillation Using Deep Convolutional Neural Networks

Cho, Jungrae; Kim, Yoonnyun

doi:10.1007/978-3-030-00320-3_20

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…The second category focuses on R to R intervals (RRI) analysis, often consisting of handcrafted feature extraction and power spectral analysis [38][39][40][41][42][43][44]. Or a combination of both, which typically involves ECG morphological features and RRI analysis combined with convolutional neural networks (CNN) and automatic feature extraction algorithms [23,[45][46][47][48][49][50][51][52][53][54][55][56]. Such handcrafted features entail serious limitations: they are computationally expensive, can lead to human bias, and require long-term samples, making them unsuitable for real-time monitoring applications [57].…”

Section: Introductionmentioning

confidence: 99%

“…Despite these data challenges, recent advances based on machine-learning and deep-learning models have been proposed for short-term prediction of AF using models trained on features extracted from ECG leads [25][26][27][28], R-to-R intervals (RRI) [29][30][31][32][33], or a combination of both [34][35][36][37][38][39][40][41]. All these methods have strong limitations.…”

Section: Introductionmentioning

confidence: 99%

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Early Warning of Atrial Fibrillation Using Deep Learning

Gavidia

Zhu

Montanari

et al. 2022

Preprint

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Atrial Fibrillation (AF) is the most common cardiac rhythm disorder. Advance knowledge of an imminent switch from sinus rhythm (SR) to AF could prompt patients to take preventive actions to avoid AF, like taking oral antiarrhythmic drugs. The question is whether there is information, even if subtle, in the minutes prior to AF to indicate an imminent switch from SR. This paper shows that, for the vast majority of patients, the answer is affirmative. On test data, our algorithm can predict the onset of AF on average 31 minutes before it appears, with an accuracy of 83\% and an F1-score of 85\%. The predictions were based on deep learning and data from 350 patients, plus an external validation of 48 patients. Overall, the proposed method has low computational complexity and can be embedded in common wearable devices for continuous heart monitoring and early warning of AF onset.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Early Warning of Atrial Fibrillation Using Deep Learning

Gavidia

Zhu

Montanari

et al. 2022

Preprint

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“…Gotlibovych et al [ 8 ] constructed a model combining a convolutional neural network and LSTM to achieve nearly real-time identification of atrial fibrillation. Cho et al [ 29 ] obtained a convolutional neural network model to predict atrial fibrillation within 4 to 6 minutes using ECG signals.…”

Section: Introductionmentioning

confidence: 99%

TOP-Net Prediction Model Using Bidirectional Long Short-term Memory and Medical-Grade Wearable Multisensor System for Tachycardia Onset: Algorithm Development Study

Liu

Zhang

et al. 2021

JMIR Med Inform

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Background Without timely diagnosis and treatment, tachycardia, also called tachyarrhythmia, can cause serious complications such as heart failure, cardiac arrest, and even death. The predictive performance of conventional clinical diagnostic procedures needs improvement in order to assist physicians in detecting risk early on. Objective We aimed to develop a deep tachycardia onset prediction (TOP-Net) model based on deep learning (ie, bidirectional long short-term memory) for early tachycardia diagnosis with easily accessible data. Methods TOP-Net leverages 2 easily accessible data sources: vital signs, including heart rate, respiratory rate, and blood oxygen saturation (SpO2) acquired continuously by wearable embedded systems, and electronic health records, containing age, gender, admission type, first care unit, and cardiovascular disease history. The model was trained with a large data set from an intensive care unit and then transferred to a real-world scenario in the general ward. In this study, 3 experiments incorporated merging patients’ personal information, temporal memory, and different feature combinations. Six metrics (area under the receiver operating characteristic curve [AUROC], sensitivity, specificity, accuracy, F1 score, and precision) were used to evaluate predictive performance. Results TOP-Net outperformed the baseline models on the large critical care data set (AUROC 0.796, 95% CI 0.768-0.824; sensitivity 0.753, 95% CI 0.663-0.793; specificity 0.720, 95% CI 0.645-0.758; accuracy 0.721; F1 score 0.718; precision 0.686) when predicting tachycardia onset 6 hours in advance. When predicting tachycardia onset 2 hours in advance with data acquired from our hospital using the transferred TOP-Net, the 6 metrics were 0.965, 0.955, 0.881, 0.937, 0.793, and 0.680, respectively. The best performance was achieved using comprehensive vital signs (heart rate, respiratory rate, and SpO2) statistical information. Conclusions TOP-Net is an early tachycardia prediction model that uses 8 types of data from wearable sensors and electronic health records. When validated in clinical scenarios, the model achieved a prediction performance that outperformed baseline models 0 to 6 hours before tachycardia onset in the intensive care unit and 2 hours before tachycardia onset in the general ward. Because of the model’s implementation and use of easily accessible data from wearable sensors, the model can assist physicians with early discovery of patients at risk in general wards and houses.

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“…Qayyum et al [ 10 ] proposed converting ECG signals into 2D images using short-time Fourier transform and put them into a pre-trained CNN model. Cho et al [ 11 ] proposed an approach for the prediction of AF by using DCNN. Wang et al [ 12 ] adopted the CNN and the improved Elman neural network for detection of AF.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Atrial Fibrillation Based on CNN-LSTM and Shortcut Connection

et al. 2020

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Atrial fibrillation (AF) is one of the most common persistent arrhythmias, which has a close connection to a large number of cardiovascular diseases. However, if spotted early, the diagnosis of AF can improve the effectiveness of clinical treatment and effectively prevent serious complications. In this paper, a combination of an 8-layer convolutional neural network (CNN) with a shortcut connection and 1-layer long short-term memory (LSTM), named 8CSL, was proposed for the Electrocardiogram (ECG) classification task. Compared with recurrent neural networks (RNN) and multi-scale convolution neural networks (MCNN), not only can 8CSL extract features skillfully, but also deal with long-term dependency between data. In particular, 8CSL includes eight shortcut connections that can improve the speed of the data transmission and processing as a result of the shortcut connections. The model was evaluated on the base of the test set of the Computing in Cardiology Challenge 2017 dataset with the F1 score. The ECG recordings were cropped or padded to the same length. After 10-fold cross-validation, the average test F1 score was 84.89%, 89.55%, and 85.64% when the segment length was 5, 10, 20 s, respectively. The experiment results demonstrate excellent performance with potential practical applications.

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Prediction to Atrial Fibrillation Using Deep Convolutional Neural Networks

Cited by 6 publications

References 13 publications

Early Warning of Atrial Fibrillation Using Deep Learning

Early Warning of Atrial Fibrillation Using Deep Learning

TOP-Net Prediction Model Using Bidirectional Long Short-term Memory and Medical-Grade Wearable Multisensor System for Tachycardia Onset: Algorithm Development Study

Automatic Detection of Atrial Fibrillation Based on CNN-LSTM and Shortcut Connection

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