Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network

Cai, Weiping; Chen, Yundai; Guo, Jun; Han, Bingchao; Shi, Yajun; Ji, Lei; Wang, Jinliang; Zhang, Guanglei; Luo, Jianwen

doi:10.1016/j.compbiomed.2019.103378

Cited by 77 publications

(33 citation statements)

References 21 publications

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“…2. ReLATeD woRK Cai et al, (2020), analyzed ECG recordings of 16,557 patients for atrial fibrillation detection. They used deep learning and densely connected neural network for better prediction.…”

Section: Adaboostm1 Ensemble Methodsmentioning

confidence: 99%

Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques

Yadav

Pal

2021

International Journal of Big Data and Analytics in Healthcare

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This paper has organized a heart disease-related dataset from UCI repository. The organized dataset describes variables correlations with class-level target variables. This experiment has analyzed the variables by different machine learning algorithms. The authors have considered prediction-based previous work and finds some machine learning algorithms did not properly work or do not cover 100% classification accuracy with overfitting, underfitting, noisy data, residual errors on base level decision tree. This research has used Pearson correlation and chi-square features selection-based algorithms for heart disease attributes correlation strength. The main objective of this research to achieved highest classification accuracy with fewer errors. So, the authors have used parallel and sequential ensemble methods to reduce above drawback in prediction. The parallel and serial ensemble methods were organized by J48 algorithm, reduced error pruning, and decision stump algorithm decision tree-based algorithms. This paper has used random forest ensemble method for parallel randomly selection in prediction and various sequential ensemble methods such as AdaBoost, Gradient Boosting, and XGBoost Meta classifiers. In this paper, the experiment divides into two parts: The first part deals with J48, reduced error pruning and decision stump and generated a random forest ensemble method. This parallel ensemble method calculated high classification accuracy 100% with low error. The second part of the experiment deals with J48, reduced error pruning, and decision stump with three sequential ensemble methods, namely AdaBoostM1, XG Boost, and Gradient Boosting. The XG Boost ensemble method calculated better results or high classification accuracy and low error compare to AdaBoostM1 and Gradient Boosting ensemble methods. The XG Boost ensemble method calculated 98.05% classification accuracy, but random forest ensemble method calculated high classification accuracy 100% with low error.

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Section: Adaboostm1 Ensemble Methodsmentioning

confidence: 99%

Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques

Yadav

Pal

2021

International Journal of Big Data and Analytics in Healthcare

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“…With 16,557 annotated 12-lead ECGs, an NN model was trained to diagnose AF, achieving an overall accuracy >0.99. 8 To further explore the ability of AI to detect AF during sinus rhythm, a convolutional neural network (CNN) was trained with 649,931 annotated 12-lead ECGs from 180,922 patients with sinus rhythm, with the ML model able to diagnose AF with an prediction, which involves estimating an unknown variable (e.g., predicting whether a patient will die in 5 years). In contrast, unsupervised learning is focused on discovering underlining patterns and relationships among the unlabeled dataset.…”

Section: Recognition Of Heart Rhythmmentioning

confidence: 99%

Clinical Application of Machine Learning-Based Artificial Intelligence in the Diagnosis, Prediction, and Classification of Cardiovascular Diseases

Shu

Ren

Song

2021

Circ J

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With the rapid development of artificial intelligence (AI) and machine learning (ML), as well as the arrival of the big data era, technological innovations have occurred in the field of cardiovascular medicine. First, the diagnosis of cardiovascular diseases (CVDs) is highly dependent on assistive examinations, the interpretation of which is time consuming and often limited by the knowledge level and clinical experience of doctors; however, AI could be used to automatically interpret the images obtained in auxiliary examinations. Second, some of the predictions of the incidence and prognosis of CVDs are limited in clinical practice by the use of traditional prediction models, but there may be occasions when AI-based prediction models perform well by using ML algorithms. Third, AI has been used to assist precise classification of CVDs by integrating a variety of medical data from patients, which helps better characterize the subgroups of heterogeneous diseases. To help clinicians better understand the applications of AI in CVDs, this review summarizes studies relating to AI-based diagnosis, prediction, and classification of CVDs. Finally, we discuss the challenges of applying AI to cardiovascular medicine.

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“…Recently, deep learning (DL) methods have shown great potential in the healthcare and medical areas [17] [18]. Specifically, some pioneering work has shown success in using DL methods for AF detection [19][20] [21]. DL models can be trained to perform beat and rhythm detection/classification using ECG data collections but, unfortunately, the use of DL for AF detection remains essentially unexplored [22].…”

Section: Introductionmentioning

confidence: 99%

AFibNet: An Implementation of Atrial Fibrillation Detection With Convolutional Neural Network

Tutuko

Nurmaini

Tondas³

et al. 2021

Preprint

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Background: Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R-R intervals to determine the Heart Rate Variability (HRV). An accurate HRV is the gold standard for predicting the AF condition. Hence, we propose a DL approach to analyze massive amounts of ECG raw data in a broad range of devices to overcome a current challenge.Results: This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8,232 records based on three datasets and tested with 3,610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. When tested with unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity in 0.02 seconds for one instance when processed in theDL-Cloud System.Conclusions: These findings demonstrate that the proposed model approach can used in a broad range of devices and validated to unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment.

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Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network

Cited by 77 publications

References 21 publications

Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques

Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques

Clinical Application of Machine Learning-Based Artificial Intelligence in the Diagnosis, Prediction, and Classification of Cardiovascular Diseases

AFibNet: An Implementation of Atrial Fibrillation Detection With Convolutional Neural Network

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