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Atrial fibrillation (AF) increases the risk of stroke. Electrocardiogram(ECG) is used for AF detection, while photoplethysmography (PPG) is simple touse and appropriate for long-term monitoring. We have developed a novelapproach to detect AF from smartwatch-based wrist PPG signals. To the best ofour knowledge, this is the first study to employ 1D CycleGAN for thereconstruction of 1D wrist PPG signals, since the quality of wrist PPG signalsis very poor due to motion artifacts and acquisition site limitations. Ourproposed approach is validated on a dataset of 21,278 10s long wrist PPGsegments. The dataset was divided into two halves and two experiments wereconducted. One half was used for training and the other half for testing andvice versa. Our classification model (Self-AFNet) with 1D-CycleGAN forrestoration achieves an accuracy of 96.41% and 97.09% for both splits,respectively. There is a noteworthy improvement in accuracy (2.94% and 5.08%for test splits, respectively) for restored signals as compared to unrestoredPPG. AF detection task was also performed using ECG signals with the matchedPPG signals to assess the validity of employing reconstructed PPG-based AFclassification. Self-AFNet managed to achieve an accuracy of 98.07% and 98.97%,respectively using two ECG splits which is comparable to the performance of AFdetection utilizing reconstructed PPG segments. This study reflects thatreconstructed wrist PPG signals acquired from wearable devices can be used forreliable detection of atrial fibrillation, which can significantly help in thereduction of the risk of stroke.

A Novel 1D Generative Adversarial Network-based Framework for Atrial Fibrillation Detection using Restored Wrist Photoplethysmography Signals