Organised by: Co-Sponsored: Malaysian Healthy Ageing Society
1st WORL RLD CONGRE GRESS SS ON HEALTHY HY AGEIN ING Vascular Age Sustainability in Ageing Health Management Author/Presenter: Kalaivani Chellappan (PhD) Co-Author: Prof. Dr. Mohd Alauddin Mohd Ali Assoc. Prof. Dr. Nor Anita Megat Mohd Nordin UNIVERSITI VERSITI KEBANGS GSAAN AAN MALAY AYSIA SIA Biomedical Engineering Research Cluster (Medical Devices Group)
Introduction…… Ageing Department of Electrical, Electronics and systems Engineering Ageing • The progressive deterioration of the near total of the functions of the organization in the course of time 1. • Extrinsic Ageing - These are from external factors. Contributing factors can be smoking, lifestyle habits such as sun exposure (sun baking), pollution, nutrition/diet, alcohol, drugs, lack of sleep, exhaustion, and of course, poor maintenance of overall health. • Intrinsic Ageing - These are genetically determined. 1 Austad, Steven NR.; Why we age; New York; John Wiley & Sounds, Inc. ; 1997
Introduction…… Healthy Ageing Department of Electrical, Electronics and systems Engineering Healthy Ageing Healthy aging is the development and maintenance of optimal mental, social and physical well-being and function in older adults. This is most likely to be achieved when communities are safe, promote health and well-being, and use health services and community programs to prevent or minimize disease. 1 Austad, Steven NR.; Why we age; New York; John Wiley & Sounds, Inc. ; 1997
Introduction…. Sustainability Department of Electrical, Electronics and systems Engineering • Health Sustainability • Capacity to implement (ABILITY) by patient • Appropriateness in implementation (OBERVATION) by medical practioners • Impact of implementation (MEASUREMENT) by ? Sustainable? Unsustainable?
Introduction…. Health Management Department of Electrical, Electronics and systems Engineering Health Management • What is needs? Plan, design and manage • Who’s is going to manage? Vendor, … • Type? Individual or Group • Cost? $$$$$$$$
Introduction…. Vascular Age Department of Electrical, Electronics and systems Engineering Vascular Age: You're only as old as your arteries The number of candles on your birthday cake may add up to your chronological age, but it doesn't necessarily equal your biological age: environmental factors, such as stress and diet, and genetics can speed up or slow down how the body ages.
Proposed Solution Noninvasive & Low Cost Vascular Health Screener NIVAR
Introduction Cardiovascular Risk Factors Risk Factors: 1. Ageing 2. Diabetes 3. Hypertension 4. Hyperlipidemia 5. Smoking 6. Obesity 50 percent of death and disability from CVD can be reduced by a combination of simple effective national efforts and individual actions to reduce major CVD risk factors. 1 1. Integrated Management of Cardiovascular Risk. Report of a WHO Meeting, Geneva, July 2002.
Introduction Current Clinical Approach in Vascular Risk Assessment Noninvasive Method The present techniques are • Ankle Brachial Index found to be: • Echocardiographic • Limited information assessment • Disease base diagnostic • Ultrasonography • Others: • Too Complex – M easurement of: • Costly • body fat • body mass index • waist circumference • blood pressure Need a more affordable and • Lipid profile precise vascular assessment • glucose levels technique for all cardiovascular • Arterial stiffness related risk. Invasive Method • Coronarography
Introduction Photoplethysmography Photoplethysmography (PPG) (Bhattacharya et al. 2001; Webster 1997) : • Optoelectronic method (using LED and PD). • Measures blood volume changed associated with cardiac contraction. • Obtained from finger, ear lobes and toes. • Low optical absorption • High degree of vasculature. • Widely used in: • Blood oxygenation saturation. • Heart rate.
Objective Establish a vascular risk prediction index through a noninvasive assessment technique using finger PPG waveform.
Methodology Signal processing techniques
Signal Pre-processing • Single channel recording. – Left or right finger (based on whether subject is left or right handed) • Sampling rate at 275 Hz. • Signal detrending to remove outliers, drifts, offset and movement artifacts. • Bandpass filtering at range of 0.6 – 15Hz. • Scaling to one: signal was normalized to unity (range at 0 to 1).
Methodology Clinical data acquisition
Statistical Analysis
Gender Health Status Total Type of Analysis Without Population Male Female With Risk Risk Data Set 1 142 161 135 168 303 (All subjects) Data Set 2F None 161 56 105 161 (Female only) Data Set 2M 142 None 79 63 142 (Male only) Data Set 3A 127 147 123 151 274 Age(24 – 66) w/o ref Data Set 3B 128 148 125 151 276 Age(24 – 66) with ref Data Set 4L 71 78 53 96 149 Age(20 – 44) with ref Data Set 4U 58 70 72 56 128 Age(45 – 66) with ref Data Set 5L 53 47 50 50 100 Exact Age Match (20 – 44) with ref Data Set 5U 39 45 42 42 84 Exact Age Match(45 – 66) with ref
Statistical Analysis Description Total Male Female 303 142 161 Number of Subject 17 17 19 Minimum Age 80 80 76 Maximum Age 44.00 44.35 43.69 Mean 12.66 13.62 11.77 Std Deviation 160.25 185.68 138.64 Variance 168\135 79\63 56\105 Without\With Risk
Methodology Empirical Data Analysis and Modelling
Data Analysis … 1 Department of Electrical, Electronics and systems Engineering Total number of subject: 184 Age range: 20 – 66 yrs Group 1: 100 age matched subjects (50 without risk & 50 with risk) Age range: 20 – 44 yrs Group 2: 84 age matched subjects (42 without risk & 42 with risk) Age range: 45 – 66 yrs Remarks: Without Risk: Subjects without any clinically diagnosed cardiovascular disease or changeable risk factors and non- smokers. With Risk: Subjects with anyone of the risk only. Risk: Hypertension, Diabetes, Hyperlipidemia & Smoking.
Reference Signal Establishment Department of Electrical, Electronics and systems Engineering • 30 subjects aged 19 years old without risk for each gender was recruited. • PPG data recorded according to the experiment protocol. • Variability assessment was carried out. • The variability between subjects for each gender was less than 5% in all recording consistently.
Health Index Definition Department of Electrical, Electronics and systems Engineering 2 (( x x ) ( y y )) fitness _ PCT 100 ( 1 ) 2 ( y y ) • PPG Health Index measuring the percentage difference between the reference signal and test signal. The reference signal is a 19 year old male and female. The health index assessment is gender base. • Following are the steps: – Removing mean from both reference and test signal. – Difference between reference and test sample standard deviation divide by test sample standard deviation. • The standard deviation value will be small if the values are clustered tightly about their mean and vice versa. • As such the above fitness value can be defined as a Vascular Health Index
Signal Processing Department of Electrical, Electronics and systems Engineering • PPG Peak Detection algorithm will detect every single valley in the entire signal length. • Best Pulse Selection algorithm will calculate the fitness of every signal pulse in the entire signal length. The median of this pulses are calculated and the pulse which is closes to the average median will be selected and matched against the reference pulse. 2 (( x x ) ( y y )) fitness _ PCT 100 ( 1 ) 2 ( ) y y
25 Application Identification of a person at risk of cardiovascular risk factors. Major application: vascular risk-prediction Risk Index Model Output Group 1 (20 – 44) yrs As such Residual (R): No Risk : R 6 Low Risk : 6 R 12 Moderate Risk : 12 R 20 High Risk : R > 20 Group 2 (45 – 66) yrs As such Residual (R): No Risk : R 7 Low Risk : 7 R 16 Moderate Risk : 16 R 22 High Risk : R > 22 Area Under ROC Curve 1.00 0.963
Variability & Repeatability Department of Electrical, Electronics and systems Engineering Repeatability on 10 subjects: Plot of Coefficient of Variability vs recording If recoding done on the same day, interval time is 30 minutes
Variability & Repeatability Department of Electrical, Electronics and systems Engineering Repeatability on 10 subjects: Plot of Coefficient of Repeatability vs recording If recoding done on the same day, interval time is 30 minutes
Conclusion Department of Electrical, Electronics and systems Engineering • Variability • Repeatability The variability between The coefficient of pulses are less than 10% repeatability is more than in all recording 90% for all recording consistently. consistently. The fitness for anyone subject recorded on a particular day do not vary much, but subjects recorded in far apart time frames shows large variation in fitness.
Prototype
Prototype GUI
Recommend
More recommend
