Computerized approach for cardiovascular risk level detection using photoplethysmography signals

Abstract Cardiovascular disease (CVD) is the leading cause of death globally. In order to decrease the medical cost for treating the heart related pathologies, this paper proposes a computer-aided diagnostic system to classify various risk level of cardiovascular disease utilizing inexpensive and non-obtrusive diagnostic tool called photoplethysmography (PPG). In this study, features such as singular value decomposition (SVD), statistical features and wavelets (Haar, Daubechies, and Symlet) are extracted from the photoplethysmography signals. These feature vectors are then applied to the softmax discriminant classifier (SDC) and Gaussian mixture model classifier (GMM) for classification of various risk phases of CVDs. The classification performance of the proposed model incorporating SDC with SVD and statistical feature vectors increases with sensitivity of 97.24%, specificity of 99.09% and an accuracy of 97.88%.The method presented in this paper assist cardiologists to validate their diagnosis.

[1]  Jiangshe Zhang,et al.  Softmax Discriminant Classifier , 2011, 2011 Third International Conference on Multimedia Information Networking and Security.

[2]  Kaiyang Li,et al.  Non-contact remote estimation of cardiovascular parameters , 2018, Biomed. Signal Process. Control..

[3]  Mohd Alauddin Mohd Ali,et al.  Recognizing cardiovascular risk from photoplethysmogram signals using ELM , 2016, 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP).

[4]  Shabana Urooj,et al.  Wavelets: biomedical applications , 2015 .

[5]  N H Lovell,et al.  Signal quality measures for pulse oximetry through waveform morphology analysis , 2011, Physiological measurement.

[6]  Javier Saiz,et al.  Photoplethysmographic Augmentation Index as a Non Invasive Indicator for Vascular Assessments , 2009 .

[7]  Lalit K. Mestha,et al.  Method for classifying cardiac arrhythmias using photoplethysmography , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  Asadollah Shahbahrami,et al.  CLASSIFICATION OF ECG ARRHYTHMIAS USING DISCRETE WAVELET TRANSFORM AND NEURAL NETWORKS , 2012 .

[9]  John Allen.  Photoplethysmography and its application in clinical physiological measurement , 2007, Physiological measurement.

[10]  Janko Drnovsek,et al.  Non-contact heart rate and heart rate variability measurements: A review , 2014, Biomed. Signal Process. Control..

[11]  Stefan Kramer,et al.  Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer’s Disease , 2015, PloS one.

[12]  Partha Pratim Kanjilal,et al.  Analysis and characterization of photo-plethysmographic signal , 2001, IEEE Transactions on Biomedical Engineering.

[13]  R. Harikumar,et al.  Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor , 2015, Int. J. Imaging Syst. Technol..

[14]  Jalal Karam.  Theory and Practice of Wavelets in Signal Processing , 2014 .

[15]  E. Irwansyah,et al.  Detection of Cardiovascular Disease Risk's Level for Adults Using Naive Bayes Classifier , 2016, Healthcare informatics research.

[16]  Eduardo Gil,et al.  Heart Rate Turbulence Analysis Based on Photoplethysmography , 2013, IEEE Transactions on Biomedical Engineering.

[17]  M. Elgendi.  On the Analysis of Fingertip Photoplethysmogram Signals , 2012, Current cardiology reviews.

[18]  K. Takazawa,et al.  Assessment of vasoactive agents and vascular aging by the second derivative of photoplethysmogram waveform. , 1998, Hypertension.

[19]  Mohamed Elhoseny,et al.  Hybrid optimization with cryptography encryption for medical image security in Internet of Things , 2018, Neural Computing and Applications.

[20]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[21]  Zahra Sadat Hosseini,et al.  Discrimination between different degrees of coronary artery disease using time-domain features of the finger photoplethysmogram in response to reactive hyperemia , 2015, Biomed. Signal Process. Control..

[22]  A. Al-Fahoum,et al.  A multiple signal classification approach for photoplethysmography signals in healthy and athletic subjects , 2015 .

[23]  P. Laguna,et al.  Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions , 2010, Physiological measurement.

[24]  Yuan-Ting Zhang,et al.  Investigation on Cardiovascular Risk Prediction Using Physiological Parameters , 2013, Comput. Math. Methods Medicine.

[25]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[26]  Antoni Nowakowski,et al.  Assessment of Cardiovascular Risk in Assisted Living , 2012 .

[27]  Walter Karlen,et al.  Multiparameter Respiratory Rate Estimation From the Photoplethysmogram , 2013, IEEE Transactions on Biomedical Engineering.

[28]  Niranjana Krupa,et al.  CLASSIFICATION OF PHOTOPLETHYSMOGRAPHIC SIGNALS USING SUPPORT VECTOR MACHINES FOR VASCULAR RISK ASSESSMENT , 2013, BioMed 2013.