Key result
A dynamic model using two Gaussian functions synthesized excellent and acceptable PPG pulses with 0.99 and 0.98 correlations to real templates, respectively, and generated heart rate variability strongly correlated with real PPGs.
Why the study?
Evaluating PPG event detection algorithms requires numerous signals with varied noise levels and sampling frequencies, but publicly available PPG databases offer limited options.
Effect estimate: 0.99, 0.98, and 0.85 correlations
A novel dynamic model using two Gaussian functions can successfully synthesize realistic PPG signals, providing a valuable tool for evaluating PPG event detection algorithms.
May aid PPG algorithm development via synthetic signals; leaves open clinical validation before use.
Evaluating the performance of photoplethysmogram (PPG) event detection algorithms requires a large number of PPG signals with different noise levels and sampling frequencies. As publicly available PPG databases provide few options, artificially constructed PPG signals can also be used to facilitate this evaluation. Here, we propose a dynamic model to synthesize PPG over specified time durations and sampling frequencies. In this model, a single pulse was simulated by two Gaussian functions. Additionally, the beat-to-beat intervals were simulated using a normal distribution with a specific mean value and a specific standard deviation value. To add periodicity and to generate a complete signal, the circular motion principle was used. We synthesized three classes of pulses by emulating three different templates: excellent (systolic and diastolic waves are salient), acceptable (systolic and diastolic waves are not salient), and unfit (systolic and diastolic waves are noisy). The optimized model fitting of the Gaussian functions to the templates yielded 0.99, 0.98, and 0.85 correlations between the template and synthetic pulses for the excellent, acceptable, and unfit classes, respectively, with mean square errors of 0.001, 0.003, and 0.017, respectively. By comparing the heart rate variability of real PPG and randomly synthesized PPG for 5 min in 116 records from the MIMIC III database, strong correlations were found in SDNN, RMSSD, LF, HF, SD1, and SD2 (0.99, 0.89, 0.84, 0.89, 0.90 and 0.95, respectively).
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Tang et al. (2020) studied this question. Synthetic PPG generation model using two Gaussian functions vs. Real PPG signals was evaluated on Correlation between template and synthetic pulses for excellent, acceptable, and unfit classes (0.99, 0.98, and 0.85 correlations). A dynamic model using two Gaussian functions synthesized excellent and acceptable PPG pulses with 0.99 and 0.98 correlations to real templates, respectively, and generated heart rate variability strongly correlated with real PPGs.
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