An Age Index for Vascular System Based on Photoplethysmogram ...

An Age Index for Vascular System Based on Photoplethysmogram Pulse Contour Analysis K. Chellappan1, M.A. Mohd Ali2, E. Zahedi3 1

Faculty of Information Technology, Universiti Tun Abdul Razak, Kelana Jaya, Malaysia 2 Department of Electrical, Electronics & Systems Engineering, Faculty of Engineering, Universiti Kebangsaan Malaysia, Bangi, Malaysia 3 School of Electrical Engineering, Sharif University of Technology, Iran

Abstract — An approach based pulse on contour analysis is proposed for the development of an aging index for the vascular system. The approach is non-invasive, using photoplethysmography (PPG), which is in wide clinical use. A probe is applied to the subject’s finger-tip to provide a measure of the pulse waveform. The measured waveforms are filtered, digitized and post-processed. Individual pulses are extracted and the best pulse was selected by using the best pulse selection algorithm. Pulse contour analysis is then performed with specially formulated PPG fitness equation. Results of measurements taken from 72 subjects are reported. The age range of the subjects was from 19 to 68 years, all of them are healthy (free from cardiovascular related risks and diseases). There are linear relationship between the PPG fitness and the age recorded from the 72 individuals. The gradient of the line of best fit tends to decrease with increasing age. An age index, which indicates vascular age, is thus proposed based on the slope of this line of best fit for a given individual. Keywords — cardiovascular, photoplethysmogram, ageing, peripheral pulse wave

I. INTRODUCTION The symptoms of certain cardiovascular diseases that lead to heart failure and stroke, such as hypertension and diabetes, more likely become altered in advanced age because of interactions that occur between age-associated cardiovascular changes in health and specific pathologic conditions underlying these diseases. The age-disease interaction results in greater impact of these diseases in older versus younger person [1]. In this regard, the cardiovascular changes that occur during healthy ageing should not be considered as normal process; rather, these age-associated changes must be constructed as one of the risk factors for the cardiovascular diseases mentioned earlier. It is also to be considered as part of the intervention designed to prevent the epidemic of cardiovascular diseases in elderly [2]. Such a strategy supports preventive treatment of what is generally considered to be normal cardiovascular ageing. Effective and efficient prevention of the risk associated with cardiovascular ageing in fortunately healthy individuals and with

age-disease interactions in older patient requires a fundamental understanding of these age-associated changes. Peripheral pulse has been commonly used in the assessment of healthy and disease [3, 4]. It can provide information about the cardiovascular system, such as heart rate or pulse pressure, and hence properties of blood vessels, including arterial elasticity, narrowing or occlusion [5, 6]. Peripheral pulse is often used by clinicians in the assessment of vascular diseases, where a weak, delayed or damped pulse is often a sign of occlusive arterial disease [7]. In clinical practice the peripheral pulse is still measured manually by palpation but there are also techniques available for noninvasive pulse assessment [8]. Photoplethysmography (PPG) is one of the common techniques, using simple low-cost optical technology [9]. PPG measures relative blood volume changes in the blood vessels close to the skin. PPG pulse signals can easily be obtained from the tissue pads of the fingers where there is a high degree of superficial vasculature. These pulses can be used to detect vascular disease, but there is limited data describing how age affects the peripheral pulse, which leads to uncertainty in establishing normal ranges for comparison with risk groups. The relationship between age and pulse characteristics, particularly on the contour of the pulse at the periphery is unclear. There is also uncertainty about whether these changes occur from an early age or later in life [1, 10]. The interpretation of such data is further complicated by the influence of heart rate and blood pressure. We are building on the work of Nitzan et al [10] and Allen and Murray [7] and our earlier reported age related data [11] by looking closer at the relationship between age and pulse contour. The purpose of our research, then, was to predict the vascular status of the subject and ultimately the severity of the underlying cardiovascular aging by means of a non-invasive investigation of the peripheral vascular system: namely, the capillaries.

N.A. Abu Osman, F. Ibrahim, W.A.B. Wan Abas, H.S. Abd Rahman, H.N. Ting (Eds.): Biomed 2008, Proceedings 21, pp. 125–128, 2008 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2008

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K. Chellappan, M.A. Mohd Ali, E. Zahedi

hours before the studies. Data processing was performed off-line.

II. MATERIAL AND METHODS A. Subjects The study has been granted approval by the Research and Ethics Committee of the National University of Malaysia Hospital. Seventy two subjects (M/F = 33/39) were investigated in the study and written informed consents were obtained. We have divided our patients into male (mean age 46.6r13.1) and female (mean age 48.1r11.1), and for both gender we made a distinction between three age brackets. The clinical data summary for study sample is as in Table 1. There were no overall significant differences in sex distribution (male/female subjects), and heart rate between the three different age groups. As expected, the mean systolic blood pressure increased with subject age. All subjects underwent laboratory test for blood glucose and cholesterol at the hematology and pathology laboratory of Hospital of National University, Malaysia, combined with blood pressure measurement to ensure they are free from cardiovascular risk factors and non-smokers.

C. Signal Processing The recorded signals were analyzed off-line using MATLAB as in Fig. 1. The signal outliers, drifts, offset and movement artifacts were removed by detrending where the mean value is subtracted from the data. The signal was futher processed to remove the effect of the respiratory rythm and higher frequency disturbance by applying band pass filtering at the frequency range of 0.6 -15 Hz. The preprocessed signal then underwent a scaling-to-one process where the signal was normalized to unity (range at 0 to 1). The next stage signal processing was to select one hundred pulses from the 90 second recorded data length. A customize PPG valley detection algorithm was used to detect the entire valley in the data length and select the best one hundred pulses. One pulse has been defined as two consecutive valleys.

Table 1 Summary of clinical data for study sample (with mean ± standard deviation) Clinical parameter/Age Group Subjects (male/female) Age Distribution (yrs) Heart rate (min-1) SBP (mmHg)

53 – 68 MeanrS. D. 26(11 /15 ) 59.9r4.7

ANOVA (P value)

29.2r5.5

36 – 52 MeanrS. D. 33(14 /19 ) 44.7r4.6

73.1r6.0

69.4r8.1

69.8r10.8

0.435

121.9r13. 1

134.4r15. 2