Journal
IEEE ACCESS
Volume 8, Issue -, Pages 172692-172706Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3025374
Keywords
Electrocardiography; Data models; Time-frequency analysis; Monitoring; Databases; Training; Machine learning; Atrial fibrillation; photoplethysmography (PPG); time-frequency analysis; convolutional neural networks (CNN); long short-term memory (LSTM)
Categories
Funding
- National Natural Science Foundation of China [61627807]
- Guangxi Key Laboratory of Automatic Detecting Technology, and Instruments [YQ19112]
- Promotion of Basic Ability of Young and Middle-aged Teachers in Universities of Guangxi [2019KY0217]
- Guangxi Innovation Driven Development Project [2019AA12005]
- Guangxi Key Research and Development Program [Guike AB20072003]
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Atrial fibrillation (AF) is the most common persistent arrhythmia and is likely to cause strokes and damage to heart function in patients. Electrocardiogram (ECG) is the gold standard for detecting AF. However, ECGs have short boards with short monitoring cycles and problems with gathering. It is also difficult to detect a burst AF through ECG. In contrast, photoplethysmography (PPG) is easy to perform and suitable for long-term monitoring. In this study, we propose a method that combines time-frequency analysis with deep learning and identifies AF based on PPG. The advantage of the method is that there is no need for the noise filtering and feature extraction of PPG, and it has a high generalization capability. The data for the experiment came from three publicly accessible databases. The first part of the experimental method uses data augmentation to convert the 10 s PPG segment into a time-frequency chromatograph by means of time-frequency analysis. The second part inputs the chromatograph into a hybrid framework that combines a convolutional neural network (CNN) and long short-term memory (LSTM) for AF/nonAF classification. The experimental results show that the method has a high classification accuracy, sensitivity, specificity, and F1 score, which are equal to 98.21%, 98.00%, 98.07% and 98.13%, respectively. The area under the receiver operating characteristic curve (AUC) is 0.9959. The model we propose not only aids doctors in diagnosing AF but also provides a method for identifying AF through portable wearable devices.
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