SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices.

Giulio Masinelli,Fabio Dell’Agnola,Adriana Arza Valdés,David Atienza

doi:10.3390/s21082725

Giulio Masinelli, Fabio Dell’Agnola + Show 2 more

Open Access

https://doi.org/10.3390/s21082725

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Abstract

The photoplethysmographic (PPG) signal is an unobtrusive blood pulsewave measure that has recently gained popularity in the context of the Internet of Things. Even though it is commonly used for heart rate detection, it has been lately employed on multimodal health and wellness monitoring applications. Unfortunately, this signal is prone to motion artifacts, making it almost useless in all situations where a person is not entirely at rest. To overcome this issue, we propose SPARE, a spectral peak recovery algorithm for PPG signals pulsewave reconstruction. Our solution exploits the local semiperiodicity of the pulsewave signal, together with the information about the cardiac rhythm provided by an available simultaneous ECG, to reconstruct its full waveform, even when affected by strong artifacts. The developed algorithm builds on state-of-the-art signal decomposition methods, and integrates novel techniques for signal reconstruction. Experimental results are reported both in the case of PPG signals acquired during physical activity and at rest, but corrupted in a systematic way by synthetic noise. The full PPG waveform reconstruction enables the identification of several health-related features from the signal, showing an improvement of up to 65% in the detection of different biomarkers from PPG signals affected by noise.

Highlights

The photoplethysmographic (PPG) signal is a noninvasive measure of the blood pulsewaves [1] that reflects the cardiovascular system’s state
In order to evaluate the spectral peak recovery (SPARE) capabilities in terms of quality of the reconstruction of the PPG signals and detection accuracy of multiple fiducial points, we applied the algorithm to an artificially corrupted reference PPG signal acquired from a subject at rest and compared its output to the original signal
Conventional noise generators—using random noise drawn from different distributions such as Gaussian or Poissonian—do not allow to properly evaluate the algorithm’s performance, as they can only provide unrealistic noises compared to the one commonly found in PPG signals

Summary

Introduction

The photoplethysmographic (PPG) signal is a noninvasive measure of the blood pulsewaves [1] that reflects the cardiovascular system’s state For this reason, it allows researchers and clinicians to evaluate various cardiovascular-related diseases, such as atherosclerosis and arterial stiffness [2], and can be even used for biometric identification [3,4]. Several biomarkers can be extracted from each pulsewave and correlated with cognitive workload, stress, and emotional state of subjects [5,6,7,8,9] These biomarkers allow assessing the physiological changes induced by both physical activity and cognitive tasks (e.g., cardiac response, blood volume, and peripheral blood vessel resistance). The PPG signal periodicity corresponds to the cardiac rhythm—the so-called pulse interval—and the analysis of each pulsewave can extract features correlated to blood volume, wall vessel elasticity, blood flow velocity, and ankle–brachial index [11,12]

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