Ayurveda is a traditional medicine and natural healing system in India. Nadi-Nidan (pulse-based diagnosis) is a prominent method in Ayurveda, and is known to dictate all the salient features of a human body. In this paper, we provide details of our procedure for obtaining the complete spectrum of the nadi pulses as a time series. The system Nadi Tarangini1 contains a diaphragm element equipped with strain gauge, a transmitter cum amplifier, and a digitizer for quantifying analog signal. The system acquires the data with 16-bit accuracy with practically no external electronic or interfering noise. Prior systems for obtaining the nadi pulses have been few and far between, when compared to systems such as ECG. The waveforms obtained with our system have been compared with these other similar equipment developed earlier, and is shown to contain more details. The pulse waveform is also shown to have the desirable variations with respect to age of patients, and the pressure applied at the sensing element. The system is being evaluated by Ayurvedic practitioners as a computer-aided diagnostic tool.

Ayurveda is one of the most comprehensive healing systems in the world and has classified the body system according to the theory of Tridosha to overcome ailments. Diagnosis similar to the traditional pulse-based method requires a system of clean input signals, and extensive experiments for obtaining classification features. In this paper we briefly describe our system of generating pulse waveforms and use various feature detecting methods to show that an arterial pulse contains typical physiological properties. The beat-to-beat variability is captured using a complex B-spline mother wavelet based peak detection algorithm. We also capture--to our knowledge for the first time--the self-similarity in the physiological signal, and quantifiable chaotic behavior using recurrence plot structures.

Heart rate variability (HRV) provides an estimate of sympathetic and parasympathetic influences on the heart rate. Although HRV has been extensively studied, sustained clinical use is still outstanding.The noninvasive, convenient, and inexpensive arterial pulse originate from heartbeats, but has not been studied in a systematic fashion except in rudimentary ways. In this paper, we present Pulse Rate Variability (PRV) as an alternative to HRV. We give evidence for the detection of disorders in patients using PRV, paving the way for future clinical use.

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Extensive research has been done to show that heartbeats are composed of the interaction of many physiological components operating on different time scales, with nonlinear and self-regulating nature. The more direct, and easily accessible manifestation of the heartbeat is the pulse; however, it has not been studied anywhere near as extensively.In this paper, we establish the relevance of the multifractal formalism for the arterial pulse, which has long been used as a fundamental tool for diagnosis in the Traditional Indian Medicine, (Ayurveda). The finding of power-law correlations through detrended fluctuation analysis indicates presence of scale-invariant, fractal structures in the pulse. These fractal structures are then further established by self-affine cascades of beat-tobeat fluctuations revealed by wavelet decomposition at different time scales. Finally, we investigate how these pulse dynamics change with age, and disorder. The analytic tools we discuss may be used on a wide range of physiological signals.

Abstract. We propose a novel hybrid Hölder-SVM detection algorithm for arrhythmia classification. The Hölder exponents are computed efficiently using the wavelet transform modulus maxima (WTMM) method. The hybrid system performance is evaluated using the benchmark MIT-BIH arrhythmia database. The implemented model classifies 160 of Normal sinus rhythm, 25 of Ventricular bigeminy, 155 of Atrial fibrillation and 146 of Nodal (A-V junctional) rhythm with 96.94% accuracy. The distinct scaling properties of different types of heart rhythms may be of clinical importance.

Human heart rate fluctuates in a complex and non-stationary manner, due to continuous influences from autonomic nervous system and other factors (harmones,temp,etc) on the sinoatrial node (S.A) (Pacemaker of the heart). ANS dysfunction is known to be associated with various cardiovascular and lifestyle diseases. The importance of traditional ancient Indian practice like Yoga has increased significantly due to the observed beneficial effects of it in various lifestyle diseases. Preliminary studies have shown that yoga may have its beneficial effect by influencing autonomic nervous system. Heart rate variability (HRV) is a most promising predictive and prognostic marker of autonomic (ANS) activity. HRV is analyzed by time and frequency domain parameters (Fast Fourier Transform). Being linear parameters these are not able to extract full information regarding the non linear behavior of heart rate fluctuations.In this paper, we propose to analyze HRV by using linear as well as non-linear methods during different yogaasanas. These mathematical models will be useful to understand the underlying physiological mechanisms during different yogasanas.

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