What does this research mean for the field?

A hybrid deep learning framework combining convolutional neural networks and long short-term memory with time-frequency analysis can accurately identify atrial fibrillation from photoplethysmography signals without requiring noise filtering or manual feature extraction. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

January 1, 2020Open Access

Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning

Structured PICO

Does a hybrid CNN-LSTM deep learning model using time-frequency analysis of PPG signals accurately identify atrial fibrillation?

Population

PPG data segments from three publicly accessible databases

Intervention

Hybrid deep learning framework combining convolutional neural network (CNN) and long short-term memory (LSTM) with time-frequency analysis of 10 s PPG segments

Outcome

AF/nonAF classification accuracy, sensitivity, specificity, F1 score, and AUCsurrogate

A novel deep learning approach using time-frequency analysis of PPG signals demonstrates high accuracy (>98%) for detecting atrial fibrillation, potentially enabling better AF screening via wearable devices.

Abstract

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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