Nonlinear Pulse Wave Dynamics in Prediction of Coronary Heart Disease and Myocardial Infarction

AbstractAtherosclerosis may induce coronary heart diseases (CHD) that progress to myocardial infarction (MCI), if untreated. The pulse plethysmogram (PPG) plot demonstrated the change in peripheral blood volume influence induced by the sympathetic branch of the autonomic nervous system that causes vasoconstriction. Thus, the present work has been aimed at proposing a noninvasive, low-cost, and wearable technology for the forecasting of CHD and MCI subjects using PPG-derived features with a support vector machine (SVM). A total of 70 volunteers (50-55 years) including MCI (n = 10), CHD (n = 30), and Control (n = 30) has been participated. Digital PPG was recorded for 10 min from the index finger of the left hand in the supine position. Ten samples from each subject were selected from the PPG signal for deriving nonlinear pulse rate variability (PRV) features using Kubios 2.0. The sensitivity, specificity, and accuracy were calculated from the obtained confusion matrix in the classification of control, CHD, and MCI classes. The findings suggested that the reduced value of PRV features in the Poincare plot, detrended fluctuation analysis recurrence plot, and correlation dimension while higher entropy measures in MCI than the CHD subjects. The best accuracy of 98.2% and 98.3% were observed with quadratic functions in the prediction of CHD and MCI subjects from the control group, respectively. The obtained results suggested the applicability of nonlinear PRV features in designing the low-cost real-time wearable technique in the prediction of CHD and MCI subjects.KEYWORDS: AtherosclerosisCoronary heart diseaseMyocardial infarctionPulse plethysmogramPulse rate variabilitySupport vector machine ACKNOWLEDGMENTSWe would like to express our gratitude to Dr. Prabin Kumar Shrivastava of Ranchi's Rajendra Institute of Medical Sciences for his assistance in gathering ECG data. Mr. Rohit Kumar of Birla Institute of Technology, Ranchi, helped implement the machine learning technique. The authors express their gratitude to the CSIR-UGC for providing an SRF to Mr. Rahul Kumar of the Birla Institute of Technology in Ranchi.Disclosure statementNo potential conflict of interest was reported by the author(s).ROLE OF FUNDINGAll the authors declare that they have received no funding.DATA AVAILABILITYThe PPG data recordings and analysis files are not public. However, the files will be provided on request to the corresponding author.Additional informationNotes on contributorsRahul KumarRahul Kumar received his BSc degree in medical laboratory technology from University Polytechnic, Birla Institute of Technology Ranchi, India in 2013 and MSc degree in microbiology from Jiwaji University, India in 2015. He is currently pursuing PhD at the Department of Bioengineering and Biotechnology, Birla Institute of Technology, Ranchi, India. His area of interest are cardiovascular diseases and medical microbiology. Email: be10001.18@bitmesra.ac.inYogender AggarwalYogender Aggarwal received his BSc degree in instrumentation (H) from University of Delhi, India in 2002 and MSc degree in biomedical instrumentation from Birla Institute of Technology, Ranchi, India in 2005. He received his PhD degree in technology from Birla Institute of Technology, Ranchi, India in 2011. His area of interest are biosignal analysis and medical informatics.Vinod Kumar NigamVinod Kumar Nigam received his BSc degree in biology from Allahabad University, India in 1988 and MSc degree in biochemistry from Allahabad University, India in 1992. He received his PhD in biochemical engineering from Banaras Hindu University, Varanasi, India in 1999. His area of interest is bioprocess technology. Email: vknigam@bitmesra.ac.inRakesh Kumar SinhaRakesh Kumar Sinha received his BSc degree in life sciences from Magadh University, India in 1993 and MSc degree in biomedical instrumentation from Birla Institute of Technology Ranchi, India in 1998. He received his PhD degree in biomedical engineering from Institute of Technology, Banaras Hindu University, India in 2004. His areas of interest are electrophysiology and biomedical instrumentation. Email: rakeshsinha@bitmesra.ac.in