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

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.

Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

“…Even more so than ECG, obtaining high‐quality PPG signals in the presence of patient motion is a notable challenge for wearable devices. In their description of the difficulty of processing PPG motion artifacts, Masinelli et al [60] . distinguish between those works that have used supplemental measurements and those that purely use signal processing or statistical metrics such as kurtosis to quantify the repeatability of the signal.…”

“…Even more so than ECG, obtaining high-quality PPG signals in the presence of patient motion is a notable challenge for wearable devices. In their description of the difficulty of processing PPG motion artifacts, Masinelli et al [60] distinguish between those works that have used supplemental measurements and those that purely use signal processing or statistical metrics such as kurtosis to quantify the repeatability of the signal. The authors presented an example of the former, a novel algorithm they termed SPARE which built on the older TROIKA spectral peak recovery technique introduced in Zhang et al [61] to denoise PPG signals with the aid of a simultaneous ECG measurement.…”

Preprocessing and Mathematical Analysis of PPG Signals

“…Математическите анализи на извлечените времеви серии дават възможност да се изследват емоционални състояния, стрес [2,3], натоварване, промени, предизвикани от физическа активност [4], да се прави биометрична идентификация [5,6], изследване на когнитивни отговори. Създават се различни видове приносими устройства [7], регистриращи и анализиращи PPG сигнали.…”

An Adaptive Heart Rate Monitoring Algorithm for Wearable Healthcare Devices

“…Two nonlinear time-frequency analysis techniques, the de-shape short time Fourier transform and the synchrosqueezing transform, were applied to extract the instantaneous physiological information from the PPG signal in a reliable way. Masinelli [26] proposed a spectral peak recovery algorithm for the pulsewave reconstruction of PPG signals, exploiting the local semiperiodicity of the pulsewave signal, together with the information about the cardiac rhythm provided by an available simultaneous ECG.…”