Indian Journal of Science and Technology
Year: 2017, Volume: 10, Issue: 12, Pages: 1-8
S. Mohanalakshmi1,2*, A. Sivasubramanian3 and A. Swarnalatha4
1Anna University, Chennai – 600025, Tamil Nadu, India; [email protected] 2Department of Electronics and Communication Engineering, M. N. M Jain Engineering College, Gurumarudhar Keseri Building, Jyothi Nagar, Thoraipakkam, Chennai – 600097, Tamil Nadu, India 3School of Electronics Engineering, VIT University, Vandalur, Kelambakkam Road, Chennai – 600127, Tamil Nadu, India; [email protected] 4Department of Electronics and Communication Engineering, St. Joseph’s College of Engineering, Old Mamallapuram Road, Semmencherry, Chennai – 600119, Tamil Nadu, India; [email protected]
*Author for correspondence
Anna University, Chennai – 600025, Tamil Nadu, India; Department of Electronics and Communication Engineering, M. N. M Jain Engineering College, Gurumarudhar Keseri Building, Jyothi Nagar, Thoraipakkam, Chennai – 600097, Tamil Nadu, India [email protected]
Objectives: Cardiovascular diseases arise mainly due to arterial stiffening leading to atherosclerosis and arteriosclerosis. Contour analysis of second Derivative Photoplethysmogram (SDPPG) reveals cardiovascular properties. In this paper, a novel SDPPG analysis algorithm is used for the assessment of arterial stiffness. Methods/Statistical Analysis: The proposed algorithm based on re-sampling technique is used for an accurate detection of significant points of interest (a, b and e wave) in SDPPG to evaluate desired parameters for the assessment of arterial stiffness. The parameters identified are PPL (PPG peak latency), PNL (PPG notch latency), PNRA (PPG notch relative amplitude), PTNL (peak to notch latency) and NI (Notch Index) and the correlation between these parameters are studied on the records obtained from the large-scale openly available database PhysioNet. Findings: The performance evaluation of the proposed SDPPG analysis algorithm is better than existing methods in terms of sensitivity and positive predictivity for a, b and e wave detection. Correlation analysis were examined for the PPG signals with low and varying amplitudes, regular and irregular heart rhythms and non-stationary signals that varies from healthy adults to unhealthy and aged patients. The positive linear correlation coefficient ‘r’ ranges from 0.7 to 0.9 for NI and PNL or PNL and PTNL, representing a significant relationship between them whereas PNL and PPL, NI and PTNL or NI and PPL, present a moderate correlation. However, a negative correlation with r=-0.66 is obtained for PNRA and PTNL. The parameters associated with dicrotic notch NI, PNL and PTNL considerably reflect the stiffness of the arteries, with smaller values of these parameters indicating stiffer arteries. Hence, the notch is the valuable characteristics of the PPG Waveform and plays a significant role in the diagnosis of arterial condition. Also, faithful association between arterial stiffness and Pulse Wave Velocity (PWV) can be acquired from PNRA and PNL, as they represent relative height of notch and position of notch with respect to time respectively, instead of Stiffness index (SI) and Reflection Index (RI) that relates stiffness of arteries. Application/Improvement: Although, the number of PPG records used for performance evaluation was self-effacing, a larger database is required to validate the findings of this study.
Keywords: Arterial Stiffness, Dicrotic Notch Detection, Digital Volume Pulse (DVP), Derivative of the Photoplethysmography Signal (SDPPG), Onsets, Second Systolic Peaks
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