An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment

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.

[1]  G P Shorten,et al.  A time domain based classifier for ECG pattern recognition , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Emmanuel Skordalakis,et al.  Syntactic Pattern Recognition of the ECG , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  L. Cosentino,et al.  Silicon Photomultiplier Technology at STMicroelectronics , 2009, IEEE Transactions on Nuclear Science.

[4]  Michiel Steyaert,et al.  VLSI Implementation of Cellular Neural Networks , 1997 .

[5]  Sun K. Yoo,et al.  Motion artifact reduction in photoplethysmography using independent component analysis , 2006, IEEE Transactions on Biomedical Engineering.

[6]  Jong-Jin Kim,et al.  Real time car driver's condition monitoring system , 2010, 2010 IEEE Sensors.

[7]  F.C. Morabito,et al.  ECG-derived respiratory signal using Empirical Mode Decomposition , 2011, 2011 IEEE International Symposium on Medical Measurements and Applications.

[8]  Suwoong Lee,et al.  Driver drowsiness detection via PPG biosignals by using multimodal head support , 2017, 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT).

[9]  Heidar Ali Talebi,et al.  Introducing a training methodology for cellular neural networks solving partial differential equations , 2009, 2009 International Joint Conference on Neural Networks.

[10]  Luigi Fortuna,et al.  A CNN-based chip for robot locomotion control , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

[11]  Domenico Mello,et al.  Electro-Optical Characterization of SiPMs With Green Bandpass Dichroic Filters , 2017, IEEE Sensors Journal.

[12]  A. Fasih,et al.  Use of CNN processors for ultra-fast solution ODE's and PDE's: A renaissance of the analog computer , 2009, 2009 2nd International Workshop on Nonlinear Dynamics and Synchronization.

[13]  M. L. Dennis Wong,et al.  PPG signal reconstruction using a combination of discrete wavelet transform and empirical mode decomposition , 2016, 2016 6th International Conference on Intelligent and Advanced Systems (ICIAS).

[14]  S. BATTIATO,et al.  A NEW EDGE-ADAPTIVE ZOOMING ALGORITHM FOR DIGITAL IMAGES , 2002 .

[15]  Sabrina Conoci,et al.  A miniaturized silicon based device for nucleic acids electrochemical detection , 2015 .

[16]  F. Bereksi Reguig,et al.  Photoplethysmogram signal analysis for detecting vital physiological parameters: An evaluating study , 2016, 2016 International Symposium on Signal, Image, Video and Communications (ISIVC).

[17]  Dr. Gabriele Manganaro,et al.  Cellular Neural Networks , 1999, Springer Series in Advanced Microelectronics.

[18]  S. Battiato,et al.  ALZ: Adaptive Learning for Zooming Digital Images , 2007, 2007 Digest of Technical Papers International Conference on Consumer Electronics.

[19]  G. S. Herman-Giddens,et al.  Selection of the number and positions of measuring locations for electrocardiography. , 1971, IEEE transactions on bio-medical engineering.

[20]  Tinoosh Mohsenin,et al.  Utilizing deep neural nets for an embedded ECG-based biometric authentication system , 2015, 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[21]  H. Asada,et al.  Identification of Vascular Dynamics and Estimation of the Cardiac Output Waveform from Wearable PPG Sensors , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[22]  Giorgio Fallica,et al.  Silicon photomultiplier technology for low-light intensity detection , 2013, 2013 IEEE SENSORS.

[23]  D. Narayana Dutt,et al.  Digital processing of ECG and PPG signals for study of arterial parameters for cardiovascular risk assessment , 2015, 2015 International Conference on Communications and Signal Processing (ICCSP).

[24]  Marko Sarlija,et al.  A convolutional neural network based approach to QRS detection , 2017, Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis.

[25]  K. Ashoka Reddy,et al.  Use of complex EMD generated noise reference for adaptive reduction of motion artifacts from PPG signals , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).

[26]  Hsien-Tsai Wu,et al.  Assessment of Vascular Health With Photoplethysmographic Waveforms From the Fingertip , 2017, IEEE Journal of Biomedical and Health Informatics.

[27]  P. Macfarlane.  The Pierre Rijlant lecture 2007: the future of electrocardiography. , 2007, Anadolu kardiyoloji dergisi : AKD = the Anatolian journal of cardiology.

[28]  M. Arzi,et al.  New algorithms for continuous analysis of long term ECG recordings using symplectic geometry and fuzzy pattern recognition , 2005, Computers in Cardiology, 2005.

[29]  Ming-Feng Yeh,et al.  ECG signal pattern recognition using grey relational analysis , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[30]  Sebastiano Battiato,et al.  Noise Reduction for CFA Image Sensors Exploiting HVS Behaviour , 2009, Sensors.

[31]  Y.T. Zhang,et al.  Continuous and noninvasive estimation of arterial blood pressure using a photoplethysmographic approach , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[32]  Francesco Rundo,et al.  Progresses towards a processing pipeline in photoplethysmogram (PPG) based on SiPMs , 2017, 2017 European Conference on Circuit Theory and Design (ECCTD).

[33]  Yuan-Ting Zhang,et al.  Adaptive reduction of motion artifact from photoplethysmographic recordings using a variable step-size LMS filter , 2002, Proceedings of IEEE Sensors.

[34]  S. Battiato,et al.  A Cellular Neural Network for Zooming Digital Colour Images , 2008, 2008 Digest of Technical Papers - International Conference on Consumer Electronics.

[35]  Thambipillai Srikanthan,et al.  VLSI efficient discrete-time cellular neural network processor , 2002 .

[36]  Chen Xiaohui,et al.  Motion artifact detection and reduction in PPG signals based on statistics analysis , 2017, 2017 29th Chinese Control And Decision Conference (CCDC).

[37]  L. Mistretta,et al.  Physiological parameters measurements in a cardiac cycle via a combo PPG-ECG system , 2015, 2015 AEIT International Annual Conference (AEIT).

[38]  Xiaolin Zhen,et al.  Recognition of ECG Patterns Using Artificial Neural Network , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[39]  Sabrina Conoci,et al.  SiPM as miniaturised optical biosensor for DNA-microarray applications , 2015 .

[40]  Tapio Seppänen,et al.  Comparing features from ECG pattern and HRV analysis for emotion recognition system , 2016, 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).

[41]  Luigi Fortuna,et al.  SC-CNN based systems to realize a class of autonomous and coupled chaotic circuits , 1997, Proceedings of 1997 IEEE International Symposium on Circuits and Systems. Circuits and Systems in the Information Age ISCAS '97.