SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices
| Autor: | Giulio Masinelli, Fabio Dell'Agnola, Adriana Arza Valdes, David Atienza |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
motion artifacts removal
Computer science 0206 medical engineering Wearable computer Context (language use) 02 engineering and technology lcsh:Chemical technology Biochemistry Signal Article Analytical Chemistry Wearable Electronic Devices Heart Rate 0202 electrical engineering electronic engineering information engineering Waveform Humans lcsh:TP1-1185 Electrical and Electronic Engineering Photoplethysmography Instrumentation Wearable technology business.industry Noise (signal processing) Signal reconstruction 020208 electrical & electronic engineering Signal Processing Computer-Assisted SPARE 020601 biomedical engineering Atomic and Molecular Physics and Optics Identification (information) wearables multimodal monitoring PPG business Artifacts Algorithm Algorithms |
| Zdroj: | Sensors Volume 21 Issue 8 Sensors, Vol 21, Iss 2725, p 2725 (2021) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|