An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment
1
STMicroelectronics—ADG Central R&D, 95121 Catania, Italy
2
Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(2), 405; https://doi.org/10.3390/s18020405
Received: 21 December 2017
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Revised: 23 January 2018
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Accepted: 26 January 2018
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Published: 30 January 2018
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
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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Keywords:
PPG; ECG; pattern recognition; physiological signal
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MDPI and ACS Style
Rundo, F.; Conoci, S.; Ortis, A.; Battiato, S. An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment. Sensors 2018, 18, 405. https://doi.org/10.3390/s18020405
AMA Style
Rundo F, Conoci S, Ortis A, Battiato S. An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment. Sensors. 2018; 18(2):405. https://doi.org/10.3390/s18020405
Chicago/Turabian StyleRundo, Francesco, Sabrina Conoci, Alessandro Ortis, and Sebastiano Battiato. 2018. "An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment" Sensors 18, no. 2: 405. https://doi.org/10.3390/s18020405
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