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Nuryani, Nuryani, Utomo, Trio Pambudi, Wiyono, Nanang, Sutomo, Artono Dwijo, Ling, Steve. (1402). Cuffless Hypertension Detection using Swarm Support Vector Machine Utilizing Photoplethysmogram and Electrocardiogram. سامانه مدیریت نشریات علمی, 13(5), 477-488. doi: 10.31661/jbpe.v0i0.2206-1504
Nuryani Nuryani; Trio Pambudi Utomo; Nanang Wiyono; Artono Dwijo Sutomo; Steve Ling. "Cuffless Hypertension Detection using Swarm Support Vector Machine Utilizing Photoplethysmogram and Electrocardiogram". سامانه مدیریت نشریات علمی, 13, 5, 1402, 477-488. doi: 10.31661/jbpe.v0i0.2206-1504
Nuryani, Nuryani, Utomo, Trio Pambudi, Wiyono, Nanang, Sutomo, Artono Dwijo, Ling, Steve. (1402). 'Cuffless Hypertension Detection using Swarm Support Vector Machine Utilizing Photoplethysmogram and Electrocardiogram', سامانه مدیریت نشریات علمی, 13(5), pp. 477-488. doi: 10.31661/jbpe.v0i0.2206-1504
Nuryani, Nuryani, Utomo, Trio Pambudi, Wiyono, Nanang, Sutomo, Artono Dwijo, Ling, Steve. Cuffless Hypertension Detection using Swarm Support Vector Machine Utilizing Photoplethysmogram and Electrocardiogram. سامانه مدیریت نشریات علمی, 1402; 13(5): 477-488. doi: 10.31661/jbpe.v0i0.2206-1504

Cuffless Hypertension Detection using Swarm Support Vector Machine Utilizing Photoplethysmogram and Electrocardiogram

Journal of Biomedical Physics and Engineering
دوره 13، شماره 5، دی 2023، صفحه 477-488    اصل مقاله (962.4 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2206-1504
نویسندگان
Nuryani Nuryani* 1؛ Trio Pambudi Utomo2؛ Nanang Wiyono3؛ Artono Dwijo Sutomo2؛ Steve Ling4
1Department of Physics, University of Sebelas Maret Jl. Ir. Sutami 36A Kentingan Jebres Surakarta 57126, Indonesia
2Department of Physics, Graduate Program, University of Sebelas Maret Jl. Ir. Sutami 36A Kentingan Jebres Surakarta 57126, Indonesia
3Faculty of Medicine, University of Sebelas Maret Jl. Ir. Sutami 36A Kentingan Jebres Surakarta 57126, Indonesia
4Centre for Health Technologies, University of Technology Sydney, Broadway NSW 2007, Australia
چکیده
Background: Hypertension is associated with severe complications, and its detection is important to provide early information about a hypertension event, which is essential to prevent further complications.
Objective: This study aimed to investigate a strategy for hypertension detection without a cuff using parameters of bioelectric signals, i.e., Electrocardiogram (ECG), Photoplethysmogram (PPG,) and an algorithm of Swarm-based Support Vector Machine (SSVM).
Material and Methods: This experimental study was conducted to develop a hypertension detection system. ECG and PPG bioelectrical records were collected from the Medical Information Mart for Intensive Care (MIMIC) from normal and hypertension participants and processed to find the parameters, used for the inputs of SSVM and comprised Pulse Arrival Time (PAT) and the characteristics of PPG signal derivatives. The SSVM was n Support Vector Machine (SVM) algorithm optimized using particle swarm optimization with Quantum Delta-potential-well (QDPSO). The SSVMs with different inputs were investigated to find the optimal detection performance.
Results: The proposed strategy was performed at 96% in terms of F1-score, accuracy, sensitivity, and specificity with better performance than the other methods tested and methods and also could develop a cuff-free hypertension monitoring system. 
Conclusion: Hypertension using SSVM, ECG, and PPG parameters is acceptably performed. The hypertension detection had lower performance utilizing only PPG than both ECG and PPG.

تازه های تحقیق

Nuryani Nuryani (Google Scholar)

کلیدواژه‌ها
Photoplethysmography؛ Support Vector Machine؛ Medical Informatics
سایر فایل های مرتبط با مقاله
مراجع
  1. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560-72. doi: 10.1001/jama.289.19.2560. PubMed PMID: 12748199.
  2. Kearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J. Global burden of hypertension: analysis of worldwide data. Lancet. 2005;365(9455):217-23. doi: 10.1016/S0140-6736(05)17741-1. PubMed PMID: 15652604.
  3. Iadecola C, Yaffe K, Biller J, Bratzke LC, Faraci FM, Gorelick PB, et al. Impact of Hypertension on Cognitive Function: A Scientific Statement From the American Heart Association. 2016;68(6):e67-94. doi: 10.1161/HYP.0000000000000053. PubMed PMID: 27977393. PubMed PMCID: PMC5361411.
  4. Hyman L, Schachat AP, He Q, Leske MC. Hypertension, cardiovascular disease, and age-related macular degeneration. Age-Related Macular Degeneration Risk Factors Study Group. Arch Ophthalmol. 2000;118(3):351-8. doi: 10.1001/archopht.118.3.351. PubMed PMID: 10721957.
  5. Zandi-Nejad K, Luyckx VA, Brenner BM. Adult hypertension and kidney disease: the role of fetal programming. Hypertension. 2006;47(3):502-8. doi: 10.1161/01.HYP.0000198544.09909.1a. PubMed PMID: 16415374.
  6. El-Hajj C, Kyriacou PA. A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure. Biomedical Signal Processing and Control. 2020;58:101870. doi: 10.1016/j.bspc.2020.101870.
  7. Rajput JS, Sharma M, Tan RS, Acharya UR. Automated detection of severity of hypertension ECG signals using an optimal bi-orthogonal wavelet filter bank. Comput Biol Med. 2020;123:103924. doi: 10.1016/j.compbiomed.2020.103924. PubMed PMID: 32768053.
  8. Ding X, Zhang YT. Pulse transit time technique for cuffless unobtrusive blood pressure measurement: from theory to algorithm. Biomed Eng Lett. 2019;9(1):37-52. doi: 10.1007/s13534-019-00096-x. PubMed PMID: 30956879. PubMed PMCID: PMC6431352.
  9. Kamal AA, Harness JB, Irving G, Mearns AJ. Skin photoplethysmography--a review. Comput Methods Programs Biomed. 1989;28(4):257-69. doi: 10.1016/0169-2607(89)90159-4. PubMed PMID: 2649304.
  10. Nuryani N, Ling SS, Nguyen HT. Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection. Ann Biomed Eng. 2012;40(4):934-45. doi: 10.1007/s10439-011-0446-7. PubMed PMID: 22012087.
  11. Li J, Lu L, Zhang YH, Xu Y, Liu M, Feng K, et al. Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine. Cancer Gene Ther. 2020;27(1-2):56-69. doi: 10.1038/s41417-019-0105-y. PubMed PMID: 31138902.
  12. Sun J, Feng B, Xu W, editors. Particle swarm optimization with particles having quantum behavior. Proceedings of the 2004 congress on evolutionary computation (IEEE Cat No 04TH8753); Portland, OR, USA: IEEE; 2004.
  13. Saeed M, Villarroel M, Reisner AT, Clifford G, Lehman LW, Moody G, et al. Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database. Crit Care Med. 2011;39(5):952-60. doi: 10.1097/CCM.0b013e31820a92c6. PubMed PMID: 21283005. PubMed PMCID: PMC3124312.
  14. Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3:160035. doi: 10.1038/sdata.2016.35. PubMed PMID: 27219127. PubMed PMCID: PMC4878278.
  15. Liang Y, Chen Z, Ward R, Elgendi M. Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database. Diagnostics (Basel). 2018;8(3):65. doi: 10.3390/diagnostics8030065. PubMed PMID: 30201887. PubMed PMCID: PMC6163274.
  16. Elgendi M. On the analysis of fingertip photoplethysmogram signals. Curr Cardiol Rev. 2012;8(1):14-25. doi: 10.2174/157340312801215782. PubMed PMID: 22845812. PubMed PMCID: PMC3394104.
  17. Zhou Z, Adeli H. Time-frequency signal analysis of earthquake records using Mexican hat wavelets. Computer-Aided Civil and Infrastructure Engineering. 2003;18(5):379-89. doi: 10.1111/1467-8667.t01-1-00315.
  18. Avola D, Cinque L, Foresti GL, Lamacchia F, Marini MR, Perini L, et al. A Shape Comparison Reinforcement Method Based on Feature Extractors and F1-Score. IEEE International Conference on Systems, Man and Cybernetics (SMC); Bari, Italy: IEEE; 2019.
  19. Liang Y, Chen Z, Ward R, Elgendi M. Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification. Biosensors (Basel). 2018;8(4):101. doi: 10.3390/bios8040101. PubMed PMID: 30373211. PubMed PMCID: PMC6316358.
  20. Yang L, Sun G, Wang A, Jiang H, Zhang S, Yang Y, et al. Predictive models of hypertensive disorders in pregnancy based on support vector machine algorithm. Technol Health Care. 2020;28(S1):181-6. doi: 10.3233/THC-209018. PubMed PMID: 32364150. PubMed PMCID: PMC7369093.
  21. Shi L, Duan Q, Ma X, Weng M, editors. The research of support vector machine in agricultural data classification. International Conference On Computer And Computing Technologies In Agriculture; Berlin, Heidelberg: Springer; 2011.
  22. Aoyagi K, Wang H, Sudo H, Chiba A. Simple method to construct process maps for additive manufacturing using a support vector machine. Additive Manufacturing. 2019;27:353-62. doi: 10.1016/j.addma.2019.03.013.
  23. Ren R, Wu DD, Liu T. Forecasting stock market movement direction using sentiment analysis and support vector machine. IEEE Systems Journal. 2018;13(1):760-70. doi: 10.1109/JSYST.2018.2794462.
  24. Vogado LH, Veras RM, Araujo FH, Silva RR, Aires KR. Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification. Engineering Applications of Artificial Intelligence. 2018;72:415-22. doi: 10.1016/j.engappai.2018.04.024.
  25. Lameski P, Zdravevski E, Mingov R, Kulakov A. SVM parameter tuning with grid search and its impact on reduction of model over-fitting. In Rough sets, fuzzy sets, data mining, and granular computing. Springer; 2015. p. 464-74.
  26. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al., editors. Going deeper with convolutions. IEEE conference on computer vision and pattern recognition; Boston, MA, USA: IEEE; 2015.
  27. Tamura T, Maeda Y, Sekine M, Yoshida M. Wearable photoplethysmographic sensors—past and present. 2014;3(2):282-302. doi: 10.3390/electronics3020282.
  28. Bortolotto LA, Blacher J, Kondo T, Takazawa K, Safar ME. Assessment of vascular aging and atherosclerosis in hypertensive subjects: second derivative of photoplethysmogram versus pulse wave velocity. Am J Hypertens. 2000;13(2):165-71. doi: 10.1016/s0895-7061(99)00192-2. PubMed PMID: 10701816.
  29. Charlton PH, Paliakaitė B, Pilt K, Bachler M, Zanelli S, Kulin D, et al. Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet. Am J Physiol Heart Circ Physiol. 2022;322(4):H493-522. doi: 10.1152/ajpheart.00392.2021. PubMed PMID: 34951543. PubMed PMCID: PMC8917928.
  30. Di Maria C, Sharkey E, Klinge A, Zheng D, Murray A, O’Sullivan J, et al. Feasibility of monitoring vascular ageing by multi-site photoplethysmography. Computing in Cardiology; Krakow, Poland: IEEE; 2012.
  31. Ahn JM. New Aging Index Using Signal Features of Both Photoplethysmograms and Acceleration Plethysmograms. Healthc Inform Res. 2017;23(1):53-9. doi: 10.4258/hir.2017.23.1.53. PubMed PMID: 28261531. PubMed PMCID: PMC5334132.
  32. Allen J, Murray A. Effects of filtering on multisite photoplethysmography pulse waveform characteristics. Computers in Cardiology; Chicago, IL, USA: IEEE; 2004.
  33. Joerger M, Huitema AD, Krähenbühl S, Schellens JH, Cerny T, Reni M, et al. Methotrexate area under the curve is an important outcome predictor in patients with primary CNS lymphoma: A pharmacokinetic-pharmacodynamic analysis from the IELSG no. 20 trial. Br J Cancer. 2010;102(4):673-7. doi: 10.1038/sj.bjc.6605559. PubMed PMID: 20125159. PubMed PMCID: PMC2837574.
  34. Le J, Bradley JS, Murray W, Romanowski GL, Tran TT, Nguyen N, et al. Improved vancomycin dosing in children using area under the curve exposure. Pediatr Infect Dis J. 2013;32(4):e155-63. doi: 10.1097/INF.0b013e318286378e. PubMed PMID: 23340565. PubMed PMCID: PMC3632448.
  35. Baek HJ, Kim JS, Kim YS, Lee HB, Park KS, et al. Second derivative of photoplethysmography for estimating vascular aging. 6th International Special Topic Conference on Information Technology Applications in Biomedicine; Tokyo, Japan: IEEE; 2007.
  36. Miyai N, Miyashita K, Arita M, Morioka I, Kamiya K, Takeda S. Noninvasive assessment of arterial distensibility in adolescents using the second derivative of photoplethysmogram waveform. Eur J Appl Physiol. 2001;86(2):119-24. doi: 10.1007/s004210100520. PubMed PMID: 11822470.
  37. Elgendi M, Jonkman M, De Boer F. Heart Rate Variability Measurement using the Second Derivative Photoplethysmogram. Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing; Valencia, Spain: INSTICC Press; 2010.
  38. Otsuka T, Kawada T, Katsumata M, Ibuki C. Utility of second derivative of the finger photoplethysmogram for the estimation of the risk of coronary heart disease in the general population. Circ J. 2006;70(3):304-10. doi: 10.1253/circj.70.304. PubMed PMID: 16501297.
  39. Raj S, Ray KC, Shankar O. Development of robust, fast and efficient QRS complex detector: a methodological review. Australas Phys Eng Sci Med. 2018;41(3):581-600. doi: 10.1007/s13246-018-0670-7. PubMed PMID: 30117043.
  40. Tan L, Jiang J. Digital signal processing: fundamentals and applications. Academic Press; 2018.
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