Key result
Deep learning PPG-to-ECG model accurately reconstructs single beats with ~216 ms latency.
Why the study?
Reconstruction of physiological signals can reduce sensor attachments and recover lost data, but periodic recalibration for continuous usage has been rarely addressed.
A novel deep learning model successfully reconstructs ECG signals from PPG with high accuracy and low latency, featuring on-device recalibration for continuous wearable health monitoring.
Reconstruction of physiological signals can be useful for biomedical applications from two perspectives, i.e., fewer sensor attachments for wearable health applications and lost data recovery in case of signal corruption in one channel. One important aspect to consider is periodic recalibration for continuous usage, rarely addressed in reported works. This article describes photoplethysmogram (PPG) to electrocardiogram (ECG) reconstruction model (P2ERM) using a data-driven model developed using deep learning (DL) tools. It employs a feature translation from PPG to ECG using a novel memory-based autoencoder, proposed for the first time, that tunes a multilayer perceptron neural net (MLPNN) for PPG-to-ECG feature translation. The final stage uses a long short-term memory (LSTM) for the feature to raw sample prediction, with heart rate (HR) adjustment. The model was evaluated with 142 data records (42-h duration) for the MIMIC-II/III waveform dataset. The average MSE and MAE for ECG sample prediction were found as <tex-math notation="LaTeX">1.28× 10⁻³ </tex-math> and <tex-math notation="LaTeX">17.81× 10⁻³ </tex-math> mV, respectively. The average deviation of ten clinical features in the predicted ECG was around 4.97%. A notable feature of the P2ERM is on-device recalibration (in situ), which can provide improved results in case the PPG morphology varies with time. An end-to-end hardware implementation using an ARM v6 controller resulted in energy savings even with a hardware ECG sensor. The latency for a single ECG beat prediction was found as 216.05 ms. P2ERM also provides superior performance over reported works on PPG-to-ECG signal reconstruction and can be useful for rehabilitation monitoring applications.
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Murmu et al. (2024) studied this question. PPG-to-ECG reconstruction model (P2ERM) was evaluated on ECG sample prediction error (MSE and MAE). A deep learning-based PPG-to-ECG reconstruction model achieved an average MSE of 1.28x10^-3 and MAE of 17.81x10^-3 mV for ECG sample prediction, with a single beat prediction latency of 216.05 ms.
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