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.
Similar content being viewed by others
References
Upadhyaya S (1986) Nadi Vijnana. Vedic Life Sci Pvt Ltd, Mumbai
Svoboda R (1992) Ayurveda: life, health and longevity. Penguin Books, India
Suoboda RE (2002) Prakriti: your ayurvedic constitution. Lotus Press, New Delhi
Sharma AK, Kumar R, Mishra A, Gupta R (2010) Problems associated with clinical trials of Ayurvedic medicines. Rev Bras Farmacogn 20:276–281
SSB M (2011) Yoga Ratnakara. vol. I. Varanasi Chowkhamba Sanskrit Ser Off
Dattatray LV (2007) Secrets of the pulse. Motilal Banarsidass Publishers, New Delhi
Walia R, Singh M (2010) Pulse based diagnosis system using the concept of ayurveda. Electr Electron Dep Lingaya’s Univ Faridabad, Haryana
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
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
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
Basavarajeeyam Rangacharya V (2007) Central council of research in Ayurveda and Siddha. New Delhi
Murthy PHC (2007) Sarngadhara samhita of sarngadharacarya. Madhyamakhanda Churna Kalpana 2007:26–36
Srikantha Murthy KR (2008) Bhavaprakasa of Bhavamisra, vol I. Chowkhamba Krishnadas Academy, Varanasi, India
Joshi RR (2005) Diagnostics using computational nadi patterns. Math Comput Model 41:33–47
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
Joshi RR (2004) A biostatistical approach to Ayurveda: quantifying the Tridosha. J Altern Complement Med 10:879–889
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
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
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
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
Murthy KRS (2000) Ashtanga Hridaya (Sanskrit with English Translation). Krishnadas Acad Varanasi
Shokawa T, Imazu M, Yamamoto H et al (2005) Pulse wave velocity predicts cardiovascular mortality. Circ J 69:259–264
McIntyre CW, Crowley LE (2017) Avoidance and treatment of cardiovascular disease in dialysis. Handbook of dialysis therapy. Elsevier, New York, pp 640–651
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
Blacher J, Guerin AP, Pannier B et al (1999) Impact of aortic Stiffness on survival in end-stage renal disease. Circulation 99:2434–2439
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
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
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
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
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
Korpas D, Halek J, Doležal L (2009) Parameters describing the pulse wave. Physiol Res 58:473–479
Millasseau SC, Ritter JM, Takazawa K, Chowienczyk PJ (2006) Contour analysis of the photoplethysmographic pulse measured at the finger. J Hypertens 24:1449–1456
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
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
Suguna GC, Veerabhadrappa ST (2019) A review of wrist pulse analysis. Biomed Res 30:538–545
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
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
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
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
Vasimalla K, Challa N, Naik SM (2016) Efficient dynamic time warping for time series classification. Indian J Sci Technol 9:1–7
Cha S-H (2007) Comprehensive survey on distance/similarity measures between probability density functions. City 1:1
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
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
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
Rangaprakash D, Dutt DN (2015) Study of wrist pulse signals using time-domain spatial features. Comput Electr Eng 45:100–107
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
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
Parikh K, Thakker B (2015) Wrist pulse classification system for healthy and unhealthy subjects. Int J Comput Appl 124:1–5
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
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
Thakker B, Vyas AL (2011) Suppressed dicrotic notch pulse classifier design. Int J Mach Learn Comput 1:148
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
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
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
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
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
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
Kalange AE, Gangal SA (2007) Piezoelectric sensor for human pulse detection. Def Sci J 57:109
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
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
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
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
Lukman S, He Y, Hui S-C (2007) Computational methods for traditional Chinese medicine: a survey. Comput Methods Programs Biomed 88:283–294
Zhang D, Zuo W, Li N (2016) Medical biometrics: computerized TCM data analysis. World Scientific, Singapore
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
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
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
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
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
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
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
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
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
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
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
Acknowledgements
None declared.
Funding
Not available.
Author information
Authors and Affiliations
Corresponding author
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.
About this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40010-022-00800-0