An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment
Open accessAbstract
Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG “combo” pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting “clean” PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.
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For reliable detection in skin layers, LEDs and photodiodes must ideally operate in wavelength range of absorption peaks of blood hemoglobin and deoxyhemoglobin, between 540 and 570 nm.
“For reliable detection in these skin layers, the used LEDs (respectively, light sources) and photodiodes PDs must ideally operate in the wavelength range of the absorption peaks of blood hemoglobin and deoxyhemoglobin, i.e., between 540 and 570 nm [77].”
From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [77]
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“PPG probe PPG and ECG pattern OSRAM LT M673 LEDs, STMicroelectronics SiPM detector Wavelength 529 nm [77]”
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- Authors
- Rundo, F., Conoci, S., Ortis, A., & Battiato, S.
- Affiliations
- STMicroelectronics (Italy) (IT)
- STMicroelectronics—ADG Central R&D, 95121 Catania, Italy
- University of Catania (IT)
- Corresponding author
- Rundo, Francesco
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-01-30
- Volume
- 18
- Issue
- 2
- Pages
- 405
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC5855408
- OpenAlex
- W2793412197
- Cited by
- 143
- References
- 37
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 14 · 2023: 7 · 2022: 21 · 2021: 28 · 2020: 22 · 2019: 31 · 2018: 5
- FWCI
- 8.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- photoplethysmography, electrocardiogram, PPG signal denoising, ECG signals, reaction-diffusion systems, cellular neural networks, bio-inspired signal processing, cardiovascular disease diagnosis, physiological monitoring, artifact removal
- MeSH terms
- Electrocardiography, Heart Rate, Humans, Pattern Recognition, Automated, Signal Processing, Computer-Assisted, Artifacts, Photoplethysmography
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