Skip to main content

Advertisement

Log in

Pulse (Nadi) Analysis for Disease Diagnosis: A Detailed Review

  • Review Article
  • Published:
Proceedings of the National Academy of Sciences, India Section A: Physical Sciences Aims and scope Submit manuscript

Abstract

“Nadi Pariksha,” i.e., measurement of the pulse, has been documented to be used by Ayurveda for assessing diseases and various other psychological conditions. Ayurveda and another ancient medicine system like traditional Chinese medicine (TCM) and traditional Korean medicine have thousand years of experience in wrist pulse analysis to diagnose the subject's health condition and the root cause of a disease. According to ancient literature, a human’s health status is dependent on three essential constitutions Vata, Pitta, and Kapha (Cun, Guan, and Chi in TCM). The term “tri-dosha” is used to characterize them. An imbalance of these doshas causes disease. Therefore, in modern health practices, it is necessary to understand these pulses imbalance with modern signal processing techniques. In this review, an attempt has been made to discuss the classical hypotheses of pulse measurement based on different Ayurvedic parameters and modern wrist pulse parameters like pulse wave analysis using pulse wave velocity and pulse rate variability. The various modern parameters and other feature extraction methods used to detect multiple diseases are also discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Upadhyaya S (1986) Nadi Vijnana. Vedic Life Sci Pvt Ltd, Mumbai

    Google Scholar 

  2. Svoboda R (1992) Ayurveda: life, health and longevity. Penguin Books, India

    Google Scholar 

  3. Suoboda RE (2002) Prakriti: your ayurvedic constitution. Lotus Press, New Delhi

    Google Scholar 

  4. Sharma AK, Kumar R, Mishra A, Gupta R (2010) Problems associated with clinical trials of Ayurvedic medicines. Rev Bras Farmacogn 20:276–281

    Article  Google Scholar 

  5. SSB M (2011) Yoga Ratnakara. vol. I. Varanasi Chowkhamba Sanskrit Ser Off

  6. Dattatray LV (2007) Secrets of the pulse. Motilal Banarsidass Publishers, New Delhi

    Google Scholar 

  7. Walia R, Singh M (2010) Pulse based diagnosis system using the concept of ayurveda. Electr Electron Dep Lingaya’s Univ Faridabad, Haryana

  8. Steffen T, Seney M (2008) Test-retest reliability and minimal detectable change on balance and ambulation tests, the 36-item short-form health survey, and the unified Parkinson disease rating scale in people with parkinsonism. Phys Ther 88:733–746

    Article  Google Scholar 

  9. Matza LS, Thompson CL, Krasnow J et al (2005) Test-retest reliability of four questionnaires for patients with overactive bladder: the overactive bladder questionnaire (OAB-q), patient perception of bladder condition (PPBC), urgency questionnaire (UQ), and the primary OAB symptom questionnaire (POSQ). Neurourol Urodynamics Off J Int Cont Soc 24:215–225

    Article  Google Scholar 

  10. Strand LI, Ljunggren AE, Bogen B et al (2008) The Short-Form McGill Pain Questionnaire as an outcome measure: Test-retest reliability and responsiveness to change. Eur J Pain 12:917–925

    Article  Google Scholar 

  11. Basavarajeeyam Rangacharya V (2007) Central council of research in Ayurveda and Siddha. New Delhi

  12. Murthy PHC (2007) Sarngadhara samhita of sarngadharacarya. Madhyamakhanda Churna Kalpana 2007:26–36

    Google Scholar 

  13. Srikantha Murthy KR (2008) Bhavaprakasa of Bhavamisra, vol I. Chowkhamba Krishnadas Academy, Varanasi, India

    Google Scholar 

  14. Joshi RR (2005) Diagnostics using computational nadi patterns. Math Comput Model 41:33–47

    Article  MATH  Google Scholar 

  15. Kumar PVG, Deshpande S, Nagendra HR (2019) Traditional practices and recent advances in Nadi Pariksha: a comprehensive review. J Ayurveda Integr Med 10:308–315

    Article  Google Scholar 

  16. Joshi RR (2004) A biostatistical approach to Ayurveda: quantifying the Tridosha. J Altern Complement Med 10:879–889

    Article  Google Scholar 

  17. Kalange AE, Mahale BP, Aghav ST, Gangal SA (2012) Nadi Parikshan Yantra and analysis of radial pulse. In: 2012 1st International Symposium on Physics and Technology of Sensors (ISPTS-1). IEEE, pp 165–168

  18. Constant I, Laude D, Murat I, Elghozi J-L (1999) Pulse rate variability is not a surrogate for heart rate variability. Clin Sci 97:391–397

    Article  Google Scholar 

  19. Hayano J, Barros AK, Kamiya A et al (2005) Assessment of pulse rate variability by the method of pulse frequency demodulation. Biomed Eng Online 4:1–12

    Article  Google Scholar 

  20. Wong J-S, Lu W-A, Wu K-T et al (2012) A comparative study of pulse rate variability and heart rate variability in healthy subjects. J Clin Monit Comput 26:107–114

    Article  Google Scholar 

  21. Murthy KRS (2000) Ashtanga Hridaya (Sanskrit with English Translation). Krishnadas Acad Varanasi

  22. Shokawa T, Imazu M, Yamamoto H et al (2005) Pulse wave velocity predicts cardiovascular mortality. Circ J 69:259–264

    Article  Google Scholar 

  23. McIntyre CW, Crowley LE (2017) Avoidance and treatment of cardiovascular disease in dialysis. Handbook of dialysis therapy. Elsevier, New York, pp 640–651

    Chapter  Google Scholar 

  24. Meaume S, Benetos A, Henry OF et al (2001) Aortic pulse wave velocity predicts cardiovascular mortality in subjects> 70 years of age. Arterioscler Thromb Vasc Biol 21:2046–2050

    Article  Google Scholar 

  25. Blacher J, Guerin AP, Pannier B et al (1999) Impact of aortic Stiffness on survival in end-stage renal disease. Circulation 99:2434–2439

    Article  Google Scholar 

  26. Sutton-Tyrrell K, Najjar SS, Boudreau RM et al (2005) Elevated aortic pulse wave velocity, a marker of arterial Stiffness, predicts cardiovascular events in well-functioning older adults. Circulation 111:3384–3390

    Article  Google Scholar 

  27. Nejadgholi I, Moradi MH, Abdolali F (2011) Using phase space reconstruction for patient independent heartbeat classification in comparison with some benchmark methods. Comput Biol Med 41:411–419

    Article  Google Scholar 

  28. Mattace-Raso FUS, van der Cammen TJM, Hofman A et al (2006) Arterial Stiffness and risk of coronary heart disease and stroke. Circulation 113:657–663

    Article  Google Scholar 

  29. Stefanadis C, Dernellis J, Tsiamis E et al (2000) Aortic stiffness as a risk factor for recurrent acute coronary events in patients with ischaemic heart disease. Eur Heart J 21:390–396

    Article  Google Scholar 

  30. Blacher J, Pannier B, Guerin AP et al (1998) Carotid arterial stiffness as a predictor of cardiovascular and all-cause mortality in end-stage renal disease. Hypertension 32:570–574

    Article  Google Scholar 

  31. Korpas D, Halek J, Doležal L (2009) Parameters describing the pulse wave. Physiol Res 58:473–479

    Article  Google Scholar 

  32. Millasseau SC, Ritter JM, Takazawa K, Chowienczyk PJ (2006) Contour analysis of the photoplethysmographic pulse measured at the finger. J Hypertens 24:1449–1456

    Article  Google Scholar 

  33. Millasseau SC, Guigui FG, Kelly RP et al (2000) Noninvasive assessment of the digital volume pulse: comparison with the peripheral pressure pulse. Hypertension 36:952–956

    Article  Google Scholar 

  34. Wu H-T, Lee C-H, Liu A-B et al (2010) Arterial stiffness using radial arterial waveforms measured at the wrist as an indicator of diabetic control in the elderly. IEEE Trans Biomed Eng 58:243–252

    Google Scholar 

  35. Suguna GC, Veerabhadrappa ST (2019) A review of wrist pulse analysis. Biomed Res 30:538–545

    Google Scholar 

  36. Zhang D-Y, Zuo W-M, Zhang D et al (2010) Wrist blood flow signal-based computerized pulse diagnosis using spatial and spectrum features. J Biomed Sci Eng 3:361

    Article  Google Scholar 

  37. Xu L, Meng MQ-H, Liu R, Wang K (2008) Robust peak detection of pulse waveform using height ratio. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp 3856–3859

  38. Luo C-H, Chung Y-F, Yeh C-C et al (2012) Stringlike pulse quantification study by pulse wave in 3D pulse mapping. J Altern Complement Med 18:924–931

    Article  Google Scholar 

  39. Sekine T, Sugano R, Tashiro T et al (2018) Fully printed wearable vital sensor for human pulse rate monitoring using ferroelectric polymer. Sci Rep 8:1–10

    Google Scholar 

  40. Vasimalla K, Challa N, Naik SM (2016) Efficient dynamic time warping for time series classification. Indian J Sci Technol 9:1–7

    Article  Google Scholar 

  41. Cha S-H (2007) Comprehensive survey on distance/similarity measures between probability density functions. City 1:1

    Google Scholar 

  42. Bisht A, Garg N, Ryait HS, Kumar A (2016) Comparative analysis of DTW based outlier segregation algorithms for wrist pulse analysis. Indian J Sci Technol 9:1–5

    Article  Google Scholar 

  43. Tawalare K, Hedaoo G, Kothekar M, Tawalare K (2017) Use of assessment of Satva Sarata (Essence of Psyche) in Prognosis of female patient of breast cancer receiving chemotherapy. Int J Complement Alt Med 9:295

    Google Scholar 

  44. Chung C-Y, Chung Y-F, Chu Y-W, Luo C-H (2013) Spatial feature extraction from wrist pulse signals. In: 2013 1st International Conference on Orange Technologies (ICOT). IEEE, pp 1–4

  45. Rangaprakash D, Dutt DN (2015) Study of wrist pulse signals using time-domain spatial features. Comput Electr Eng 45:100–107

    Article  Google Scholar 

  46. Wang K, Xu L, Li Z, et al (2003) Approximate entropy-based pulse variability analysis. In: 16th IEEE Symposium Computer-Based Medical Systems, 2003. Proceedings. IEEE, pp 236–241

  47. Jianjun Y, Yiqin W, Fufeng L, et al (2008) Analysis and classification of wrist pulse using sample entropy. In: 2008 IEEE International Symposium on IT in Medicine and Education. IEEE, pp 609–612

  48. Parikh K, Thakker B (2015) Wrist pulse classification system for healthy and unhealthy subjects. Int J Comput Appl 124:1–5

    Google Scholar 

  49. Thakker B, Vyas AL, Farooq O, et al (2011) Wrist pulse signal classification for health diagnosis. In: 2011 4th International conference on biomedical engineering and informatics (BMEI). IEEE, pp 1799–1805

  50. Hu C-S, Chung Y-F, Yeh C-C, Luo C-H (2012) Temporal and spatial properties of arterial pulsation measurement using pressure sensor array. Evidence-Based Complement Altern Med. https://doi.org/10.1155/2012/745127

    Article  Google Scholar 

  51. Thakker B, Vyas AL (2011) Suppressed dicrotic notch pulse classifier design. Int J Mach Learn Comput 1:148

    Article  Google Scholar 

  52. Liu L, Li N, Zuo W et al (2012) Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis. International conference on intelligent science and intelligent data engineering. Springer, Berlin, pp 475–482

    Google Scholar 

  53. Chang H, Chen J, Liu Y (2018) Micro-piezoelectric pulse diagnoser and frequency domain analysis of human pulse signals. J Tradit Chinese Med Sci 5:35–42

    Google Scholar 

  54. Khaire NN, Joshi Y V (2015) Diagnosis of disease using wrist pulse signal for classification of pre-meal and post-meal samples. In: 2015 International Conference on Industrial Instrumentation and Control (ICIC). IEEE, pp 866–869

  55. Zhang Z, Zhang Y, Yao L et al (2018) A sensor-based wrist pulse signal processing and lung cancer recognition. J Biomed Inform 79:107–116

    Article  Google Scholar 

  56. Kelkar P, Karamchandani S, Jindal SK (2010) Identifying tridosha for disease characterization in morphology of an IPG pulse waveform. In: Conf. on advance Applications in physiological variability

  57. Manohar PR, Sorokin O, Chacko J, Nampoothiri V (2018) An exploratory clinical study to determine the utility of heart rate variability analysis in the assessment of dosha imbalance. J Ayurveda Integr Med 9:126–130

    Article  Google Scholar 

  58. Kalange AE, Gangal SA (2007) Piezoelectric sensor for human pulse detection. Def Sci J 57:109

    Article  Google Scholar 

  59. Leonard P, Beattie TF, Addison PS, Watson JN (2004) Wavelet analysis of pulse oximeter waveform permits identification of unwell children. Emerg Med J 21:59–60

    Article  Google Scholar 

  60. Zhang D, Zhang L, Zhang D, Zheng Y (2008) Wavelet-based analysis of Doppler ultrasonic wrist-pulse signals. In: 2008 International Conference on BioMedical Engineering and Informatics. IEEE, pp 539–543

  61. Zhang Y, Wang Y, Wang W, Yu J (2002) Wavelet feature extraction and classification of Doppler ultrasound blood flow signals. Sheng wu yi xue Gong Cheng xue za zhi J Biomed Eng Shengwu Yixue Gongchengxue Zazhi 19:244–246

    Google Scholar 

  62. Jiang Z, Guo C, Zang J et al (2020) Features fusion of multichannel wrist pulse signal based on KL-MGDCCA and decision level combination. Biomed Signal Process Control 57:101751

    Article  Google Scholar 

  63. Lukman S, He Y, Hui S-C (2007) Computational methods for traditional Chinese medicine: a survey. Comput Methods Programs Biomed 88:283–294

    Article  Google Scholar 

  64. Zhang D, Zuo W, Li N (2016) Medical biometrics: computerized TCM data analysis. World Scientific, Singapore

    Book  Google Scholar 

  65. Chen Y, Zhang L, Zhang D, Zhang D (2009) Wrist pulse signal diagnosis using modified Gaussian models and Fuzzy C-Means classification. Med Eng Phys 31:1283–1289

    Article  Google Scholar 

  66. Sareen M, Prakash P, Anand S (2008) Wavelet decomposition and feature extraction from pulse signals of the radial artery. In: 2008 International Conference on Advanced Computer Theory and Engineering. IEEE, pp 551–555

  67. Pooja More HN, Joshi Aniruddha J (2014) Developing a diagnostic tool for type 2 diabetes based on tridosha analysis through Nadi pariksha. Int Ayurvedic Med J 2:9

    Google Scholar 

  68. Lee BJ, Jeon YJ, Ku B et al (2015) Association of hypertension with physical factors of wrist pulse waves using a computational approach: a pilot study. BMC Complement Altern Med 15:222

    Article  Google Scholar 

  69. Yan R, Zhou M, Sun W, Meng J (2017) Analyzing wrist pulse signals measured with polyvinylidene fluoride film for hypertension identification. Sensors Mater 29:1339–1351

    Google Scholar 

  70. Qiao L, Qi Z, Tu L et al (2018) The association of radial artery pulse wave variables with the pulse wave velocity and echocardiographic parameters in hypertension. Evid-Based Complement Altern Med 2018:1–11

    Google Scholar 

  71. Arunkumar N, Sirajudeen KMM (2011) Approximate Entropy-based ayurvedic pulse diagnosis for diabetics-a case study. In: 3rd International Conference on Trendz in Information Sciences & Computing (TISC2011). IEEE, pp 133–135

  72. Nanyue W, Youhua Y, Dawei H et al (2015) Pulse diagnosis signals analysis of fatty liver disease and cirrhosis patients by using machine learning. Sci World J 2015:1–9

    Article  Google Scholar 

  73. Chuang C-Y, Chang T-T, Li D-K et al (2020) Colectomy influences the radial pulse parameters of traditional Chinese medicine pulse diagnosis in patients with colorectal cancer. Eur J Integr Med 35:101067

    Article  Google Scholar 

  74. Chang C-W, Liao K, Chang Y-T et al (2019) The effect of radial pulse spectrum on the risk of major adverse cardiovascular events in patients with type 2 diabetes. J Diabetes Complicat 33:160–164

    Article  Google Scholar 

  75. Lee BJ, Jeon YJ, Kim JY (2017) Association of obesity with anatomical and physical indices related to the radial artery in Korean adults. Eur J Integr Med 14:22–27

    Article  Google Scholar 

Download references

Acknowledgements

None declared.

Funding

Not available.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Karan Veer.

Ethics declarations

Ethical approval

Not required.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, S., Kumar, S. & Veer, K. Pulse (Nadi) Analysis for Disease Diagnosis: A Detailed Review. Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci. 93, 135–145 (2023). https://doi.org/10.1007/s40010-022-00800-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40010-022-00800-0

Keywords

Navigation