Key result
A new PPG quality metric based on modulation spectrum properties significantly outperformed benchmark SQIs, improving balanced accuracy by 19.0% to 21.6% across different wavelengths.
Why the study?
Accurate physiological parameter estimation from wearable photoplethysmography requires good-quality signals, creating a need for effective signal quality metrics.
Effect estimate: 19.0% to 21.6% BACC improvement
A novel PPG quality metric based on modulation spectrum shape features significantly improves the accuracy of PPG signal quality detection across multiple wavelengths.
May improve PPG monitoring reliability in wearables; leaves open clinical validation.
Photoplethysmography (PPG) is used to measure blood volume changes in the microvascular bed of tissue. Information about these changes along time can be used for estimation of various physiological parameters, such as heart rate variability, arterial stiffness, and blood pressure, to name a few. As a result, PPG has become a popular biological modality and is widely used in wearable health devices. However, accurate measurement of various physiological parameters requires good-quality PPG signals. Therefore, various signal quality indexes (SQIs) for PPG signals have been proposed. These metrics have usually been based on statistical, frequency, and/or template analyses. The modulation spectrogram representation, however, captures the second-order periodicities of a signal and has been shown to provide useful quality cues for electrocardiograms and speech signals. In this work, we propose a new PPG quality metric based on properties of the modulation spectrum. The proposed metric is tested using data collected from subjects while they performed various activity tasks contaminating the PPG signals. Experiments on this multi-wavelength PPG dataset show the combination of proposed and benchmark measures significantly outperforming several benchmark SQIs with improvements of 21.3% BACC (balanced accuracy) for green, 21.6% BACC for red, and 19.0% BACC for infrared wavelengths, respectively, for PPG quality detection tasks. The proposed metrics also generalize for cross-wavelength PPG quality detection tasks.
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Tiwari et al. (2023) studied this question. Modulation spectrum-based PPG quality metric vs. Benchmark signal quality indexes (SQIs) was evaluated on PPG quality detection (balanced accuracy) (19.0% to 21.6% BACC improvement). A new PPG quality metric based on modulation spectrum properties significantly outperformed benchmark SQIs, improving balanced accuracy by 19.0% to 21.6% across different wavelengths.
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