Next Article in Journal
HKSiamFC: Visual-Tracking Framework Using Prior Information Provided by Staple and Kalman Filter
Next Article in Special Issue
Foveation Pipeline for 360° Video-Based Telemedicine
Previous Article in Journal
Chipless RFID Sensors for the Internet of Things: Challenges and Opportunities
Previous Article in Special Issue
Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice
Article

Detection of Atrial Fibrillation Using 1D Convolutional Neural Network

1
College of Artificial Intelligence, Yango University, Fuzhou 350015, China
2
Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 2136; https://doi.org/10.3390/s20072136
Received: 28 February 2020 / Revised: 31 March 2020 / Accepted: 8 April 2020 / Published: 10 April 2020
(This article belongs to the Special Issue Multimodal Data Fusion and Machine-Learning for Healthcare)
The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection methods usually convert 1D time-series electrocardiogram (ECG) signal into 2D spectrogram to train a complex AF detection system, which results in heavy training computation and high implementation cost. This paper proposes an AF detection method based on an end-to-end 1D convolutional neural network (CNN) architecture to raise the detection accuracy and reduce network complexity. By investigating the impact of major components of a convolutional block on detection accuracy and using grid search to obtain optimal hyperparameters of the CNN, we develop a simple, yet effective 1D CNN. Since the dataset provided by PhysioNet Challenge 2017 contains ECG recordings with different lengths, we also propose a length normalization algorithm to generate equal-length records to meet the requirement of CNN. Experimental results and analysis indicate that our method of 1D CNN achieves an average F1 score of 78.2%, which has better detection accuracy with lower network complexity, as compared with the existing deep learning-based methods. View Full-Text
Keywords: electrocardiogram (ECG); atrial fibrillation (AF); convolutional neural network (CNN); deep learning electrocardiogram (ECG); atrial fibrillation (AF); convolutional neural network (CNN); deep learning
Show Figures

Figure 1

MDPI and ACS Style

Hsieh, C.-H.; Li, Y.-S.; Hwang, B.-J.; Hsiao, C.-H. Detection of Atrial Fibrillation Using 1D Convolutional Neural Network. Sensors 2020, 20, 2136. https://doi.org/10.3390/s20072136

AMA Style

Hsieh C-H, Li Y-S, Hwang B-J, Hsiao C-H. Detection of Atrial Fibrillation Using 1D Convolutional Neural Network. Sensors. 2020; 20(7):2136. https://doi.org/10.3390/s20072136

Chicago/Turabian Style

Hsieh, Chaur-Heh, Yan-Shuo Li, Bor-Jiunn Hwang, and Ching-Hua Hsiao. 2020. "Detection of Atrial Fibrillation Using 1D Convolutional Neural Network" Sensors 20, no. 7: 2136. https://doi.org/10.3390/s20072136

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop