Abstract
PurposeIn this study, we aimed to develop an automatic atrial fibrillation detection technique for the early prediction of atrial fibrillation, that can be used with wearable devices.MethodsAn effective deep learning-based technology is proposed to automatically detect atrial fibrillation. First, novel preprocessing algorithms, wavelet transform and sliding window filtering, are introduced to reduce the noise and outliers, respectively, from ECG signals. Then, a robust R-wave detection algorithm is developed. In addition, we proposed a feedforward neural network to detect atrial fibrillation based on ECG records.ResultsExperiments verified using a tenfold cross-validation strategy showed that the proposed method achieves competitive detection performance, and can be applied to wearable detection devices. The proposed R-wave detection algorithm achieved a detection sensitivity of 99.22%, a positive recognition rate of 98.55%, and a deviance of 2.25% on the MIT-BIH arrhythmia database. The proposed atrial fibrillation detection model achieved an accuracy of 84.00%, a detection sensitivity of 84.26%, a specificity of 93.23%, and an area under the receiver operating curve of 89.40% on a mixed dataset composed of the Challenge2017 database and the MIT-BIH arrhythmia database.ConclusionThe analysis demonstrated that the proposed atrial fibrillation detection method could automatically detect atrial fibrillation with high accuracy and efficiency, could be applied to wearable devices, and has great value in the early detection of atrial fibrillation. We believe that our work will make a valuable contribution to the area of atrial fibrillation.
Highlights
According to Cardiovascular Health and Disease Report 2019 Summary [1], the number of cardiovascular patients from 1990 to 2017 continues to increase, and the mortality rate is on the rise
Many studies in areas of cardiovascular disease prevention have been reported during the past decade [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19], there are still some difficulties in the research of atrial fibrillation detection: (1) It is difficult to detect atrial fibrillation in the early stage due to it has the characteristics of intermittent onset, short onset time, and rapid disappearance of symptoms; (2) In the clinic, doctors usually diagnose atrial fibrillation by observing the ECG manually, which is inefficient and subjective
We evaluate the proposed feedforward neural network (FNN) model on the mixed dataset composed of the Challenge 2017 database [24] and MIT-BIH arrhythmia database [23] by using a 10-fold cross-validation strategy
Summary
According to Cardiovascular Health and Disease Report 2019 Summary [1], the number of cardiovascular patients from 1990 to 2017 continues to increase, and the mortality rate is on the rise. Traditional atrial fibrillation detection methods are reliant on feature extraction of F wave, P wave, and R peak in electrocardiogram (ECG). Those methods only perform well when tested on small datasets, such as the MIT-BIH database [23]. An effective FNN model is developed to detect atrial fibrillation based on ECG records. This model takes the characteristic matrix extracted from the RR interval as input and the model parameters of the model are optimized through the grid search method.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
