Key result
Recursive GAN model reconstructs corrupted PPG signals to measure heart rate within 1.7 bpm.
Why the study?
Motion artifacts affect photoplethysmography signals measured from the wrist, compromising the accuracy of heart rate monitoring in wearable devices.
A recursive GAN model can effectively reconstruct motion artifact-corrupted PPG signals, improving heart rate measurement accuracy in wearable devices.
This article explores how motion artifacts (MAs) affect photoplethysmography (PPG) signals measured from the radial artery of the wrist through our wearable system called WrisTee. We propose a recursive generative adversarial network (GAN) model that reconstructs corrupted PPG signals across a wide range of signal-to-noise ratios (SNRs) from −33 to 25 dB. To train and evaluate the model’s performance, we constructed a dataset of PPG signals obtained from 15 subjects and developed an algorithm for generating synthetic noisy data. The proposed GAN model enables the measurement of heart rate (HR) from synthetic data with a mean absolute error (MAE) of 1.7 bpm. Finally, our model successfully processed 77% of PPG segments from real noisy data. This method provides a foundation for developing generative models aimed at reconstructing noisy PPG data affected by MAs, therefore enhancing the accuracy of personal health monitoring devices.
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Long et al. (2023) studied Corrupted photoplethysmography (PPG) signals (n=15). Recursive generative adversarial network (GAN) model was evaluated on Heart rate (HR) measurement error from synthetic data (MAE 1.7 bpm). A recursive generative adversarial network model reconstructed corrupted photoplethysmography signals, enabling heart rate measurement with a mean absolute error of 1.7 bpm.
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