Carotid artery thermography and thermal image processing with data management (MAE03) NEO JUN WEI ANGLO-CHINESE SCHOOL (INDEPENDENT) RESEARCH MENTOR: PROF. NG YIN KWEE, EDDIE SCHOOL OF MECHANICAL AND AEROSPACE ENGINEERING NANYANG TECHNOLOGICAL UNIVERSITY
Project Objectives Using pulse rates and Determining pulse other quantitative and rates using method qualitative parameters of non-invasive to detect stenosis within thermography carotid arteries Unable to complete due to time constraints and methodological limitations
Introduction: Significance of Project Cardiovascular diseases (CVDs) are the largest cause of death worldwide 17.7 million deaths caused by CVDs in 2015 Current methods used for CVD often invasive and/or time-consuming. ECGs take from 10-30 minutes and MRIs can take from 25 minutes to 1 hour Aid in devising a method to preliminarily screen a subject before further examinations Could give rise to potential methods of contactless pulse rate detection
Introduction: Background Information Thermography The use of infrared thermal imaging to study temperature fluctuations and blood flow within the body Passive thermography is utilised in this study – does not require an energy (heat) source Subject is placed under regular conditions, not subject to heating and cooling cycles
Introduction: Theoretical basis of project Thermal images of left and right superficial temporal arteries and external carotid arteries are taken Temperature fluctuations within these arteries should correspond to cardiac cycle since they are caused by start-stop flow of blood with each pump of the heart
Methodology 1) Thermal Imaging Collection 39 male human subjects were recruited VarioCAM Infrared (IR) camera was placed 0.5m from each subject Thermal images of 240 × 320 pixels were captured at a rate of 25 frames per second for 20 seconds The body parts observed were left and right sides of the neck and foreheads.
Methodology 2) Image Pre-processing Thermal images were Superficial cropped such to include only temporal artery the features of interest (blood vessels) Filters were applied to enhance contrast and to minimise noise from ambient heat.
Methodology 3) Image Post-processing Step 1: Rectangular region R containing the major blood ROI vessel of the body part of interest was selected. Step 2: The multiple thermograms are treated as a three-dimensional matrix ( x , y , t ). t=20
Methodology y 3) Image Post-processing Step 3: This is then reduced into a two dimensional matrix by averaging one of the spatial dimensions, which is chosen as x in this case. This matrix is mathematically x expressed as R 1 x ( , ) y t ( , , ) x y t R x 0 x time Step 4: Fast Fourier Transform (FFT) is then applied to plot the signal in R 1 y the frequency domain. The values obtained from this transformation are P P known as the power spectra P , and they are averaged along the y y R dimension in order to obtain a composite power spectrum, ത 𝑄 . This is 0 y y expressed by the equation on the left.
Methodology 3) Image Post-processing Step 5: The power spectrum at any frequency f in time ത M 𝑄 0 is is ( ) P f convolved with the average power spectrum (over the existing i period of time M ) in order to obtain the historic power spectrum at i 1 H f ( ) any value of f , as expressed mathematically on the right. M F P j ( ) This amplifies the signal as a result of heat from blood-flow and i minimised any “noise” due to heat from surrounding tissue i 1 j 1 Step 6: H(f) is then averaged to obtain the historic power spectrum ഥ 𝐼 before it is then convolved with ത 𝑄 0 to obtain the peak frequency which is designated to be the pulse frequency f pulse . This is multiplied by 60 to obtain the pulse rate in beats per minute, bpm.
Methodology 3) Image Post-processing Step 7: The calculated pulse rates were compared to heart rates obtained from NHCS. These were taken to be the ground truth readings
Table 1: Pulse rates of left and Results right necks and heads, mean and actual pulse rates and percentage accuracy Average percentage accuracy of the pulse rates however were 83.37%. The Pearson product moment correlation test was applied yielding an R 2 value of 0.01, indicating a weak correlation.
Results Mean HR(Neck) against Actual HR Mean HR(Head) against Actual HR 120 120 100 100 Mean HR(Head) Mean HR (Neck) 80 80 60 60 40 40 y = 0.2178x + 52.712 y = -0.0467x + 72.177 R² = 0.0415 R² = 0.0016 20 20 0 0 0 10 20 30 40 50 60 70 80 90 100 0 20 40 60 80 100 Actual HR Actual HR Poor correlation for heart rates derived from both necks and heads Heart rates for necks show better correlation Mean accuracy for Necks = 79.78%, whereas for Heads = 84.23%
Results RMSE = 17.06 bpm RMSE = 13.86 bpm Proportional error for both detected, where bias increases proportionally to mean Good agreement despite poor correlation as fell within 95% confidence intervals Bias for necks deviates from 0 more than for heads Could be due to greater contour contrast offered by head, as less obstruction by skin and fat on head compared to neck
Discussion Repetition of the pulse-rate values 58.776 and 88.164. Reasons for these two values constituting the majority of values displayed are still unknown Could be due to a bug within the Python code or the FFT package installed These two values appear at random for various body parts The mean yielded from the average pulse rate of each patient appears to be accurate to a certain degree However, the extremely low R 2 between the derived and real pulse rates indicates that this is likely to be by coincidence, and that the reliability of this method is questionable When compared to Bin and Li utilising discrete wavelet transform, They obtained accuracies varying from 85.2% to 98.5%, with a mean accuracy rate of 94.5% While the functions used were different, the methods both approached pulse extraction through power spectral density and obtained significantly more accurate results
Conclusion Results obtained were accurate to a certain extent However, reliability of this method remains unknown given the weak trend between extracted and true data The study does showcase a possible method for contactless pulse rate extraction that could serve in a future medical extensions if the method is further improved upon This would be applicable to many scenarios where short-term non- invasive pulse-rate determination would be desirable, for sports training studies, sleep studies and psychological evaluations
Further work Addition of motion-tracking algorithm to fix ROI Use of thermal imaging to determine temperature of blood vessels and other parameters that could aid in cardiovascular disease diagnosis Use DFT instead of FFT due to high-frequency and infinite-period nature of waves FFT is suited to
References Cardiovascular diseases (CVDs). (2018, September 26). Retrieved from https://www.who.int/cardiovascular_diseases/en/ Heart disease. (2018, March 22). Retrieved from https://www.mayoclinic.org/diseases- conditions/heart-disease/diagnosis-treatment/drc-20353124 Blood Vessels In The Head Blood Vessel Back Of Head This Diagram Shows The Blood Vessels In (n.d.). Retrieved September 3, 2018, from https://anatomyclass01.us/blood- vessels-in-the-head/blood-vessels-in-the-head-blood-vessel-back-of-head-this- diagram-shows-the-blood-vessels-in/. Accessed 3 Sept. 2018. Khan, S. U., MD, DePersis, M., DO, & Kaluski, E., MD, FACC, FESC, FSCAI. (n.d.). Figure 1. Course of ulnar artery in forearm and palmar blood flow with puncture sites. [Digital image]. Retrieved September 3, 2018, from https://citoday.com/2017/10/ulnar- access-for-catheterization-and-intervention/. Accessed 3 Sept. 2018. Sun, N., Garbey, M., Merla, A., & Pavlidis, I. (2005, June). Imaging the cardiovascular pulse. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 2, pp. 416-421). IEEE. Accessed 3 Sept. 2018.
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