Paper Info
Title
An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment.
Abstract
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.
Year
DOI
Venue
2018
10.3390/s18020405
SENSORS
Keywords
Field
DocType
PPG,ECG,pattern recognition,physiological signal
Pattern recognition,Photoplethysmogram,Waveform,Robustness (computer science),Electronic engineering,Artificial intelligence,Engineering,Cellular neural network,Distortion,Pattern recognition system
Journal
Volume
Issue
ISSN
18
2.0
1424-8220
Citations 
PageRank 
References 
4
0.47
4
Authors
4
Name
Order
Citations
PageRank
Francesco Rundo1247.39
Sabrina Conoci265.58
alessandro ortis3248.54
Sebastiano Battiato465978.73