Nadi Tarangini: A Pulse Based Diagnostic System

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Nadi Tarangini A Pulse Based Diagnostic System
Observations and Discussion.
Chaos Analysis.
Introduction.

• Observation The dynamics is changing in pulse.
• Recurrence Plot
• Visually reveals information about correlations.
• Structural changes in pulse.
• where, Phase space vectors
  Cut-off distance Heaviside function

• Ayurveda Indian Traditional Medicine for
  diagnosis.
• Looking, listening, smelling, asking, touching.
• The arterial pulse contains large amount of
  information about disorders.
• Probably information in pulse under-used.
• Pulse waveform Physiological signal e.g. ECG.
• Ayurveda ??Traditional Chinese Medicine (TCM).
• Researchers already getting good results in TCM.
• Previous methods Pressure sensors , transducer,
  photoplethysmograph, infrared LED three
  photodiodes, condenser microphone, etc.

Varying Pressure.
At each pressure, the obtained pulse gives
different insights about the body.
As the pressure applied on sensor increases
Variations with age.
The patterns are different for three age-groups.

• below 25 pulses are more dominant in secondary
  peaks.
• 25-50 group is relatively stable.
• Older pulses are irregular in nature.

Our goal Pulse-based CAD system.

• Imitate the skill of feeling the pulse.
• Ayurvedic basis types and sub-types of nadi.
• Quantitative basis machine learning algorithms.
• Remove subjectiveness.

• The pulse duration increases (rate decreases) as
  the age increases

Self-similar Nature.
Set-up of Nadi Tarangini.

• Recurrence Quantification Analysis Descriptors
• Texture of a RP.
• Large small scale structures in a RP.

• Fractal forms consists of subunits that resemble
  the structure of the overall object.
• Physiological signals
• ECG (explored extensively).
• Pulse (First time -- to our knowledge).
• Step or cusp-like singularities.
• Different portions have different scaling
  properties.
• Multifractal formalism The power-law scaling
  relationship.

Fourier Analysis.

• The strength of the spectral harmonics
• Accounts for the morphology of the pulse.
• Thus, can be used in detection of pulse.
• First 100 Fourier coefficients of only vata pulse.

Important Properties.

• Sampling frequency 500Hz.
• Rich in harmonics, Complete, Reproducible.
• Time-domain features
• percussion wave (P)
• tidal wave (T)
• valley (V)
• dicrotic wave (D)

Multifractal Spectra have different mean and
range values for various patients
WTMM tree Hierarchical Organization of the
singularities
Beat-to-Beat Alterations.

• HRV has been analyzed for long to detect
  arrhythmias.
• Similar analysis on long pulse data
• Peak Detection A pre-requisite for capturing
  such beat-to-beat variations.
• accurately find the start and end of each beat
  (pulse cycle).

Distinctly observable patterns.
Final Remarks.

• We have designed a high quality pulse DAQ system.
• Incorporated recent developments in
  instrumentation technologies.
• Our pulse waveforms, in the form of time series,
  have high details.
• Pulse signal typical properties of a
  physiological signal.
• The information in arterial pulse is probably
  under-used.
• Rigorous machine learning algorithms could be
  applied on these waveforms to
• Identify of types and sub-types of Nadis defined
  in Ayurvedic literature.
• Diagnostic purposes to classify into possible
  disorders.

Angle at main peak

• Complex frequency B-spline wavelet.
• Whenever there is a peak in the pulse waveform.
• A positive and a negative spike in both the real
  and imaginary parts.
• On our database of 79 waveforms 100 accuracy.
• Steady data for longer duration is required.
• Time-domain, Frequency-domain, or using
  morphology-based features.
• Amplitudes, energies, slopes, angles, entropies,
  velocities, etc.
• It is the variations between consecutive beats
  that is critical, rather than the heart/pulse
  rate or the average values.

• Variations in amplitudes, slopes, systolic
  diastolic energies, and so on.
• Machine learning algorithms can be applied to
  distinguish major and sub-types of nadis defined
  in Ayurveda.

References.

• Patent- Dr. Ashok Bhat, Aniruddha Joshi and
  Anand Kulkarni, "A system for complete spectrum
  of the nadi pulses as a time-series", Indian
  Patent Application 197819, granted on 26th Dec
  2005.
• Paper- A.J. Joshi, A. V. Kulkarni, S. Chandran,
  V. K. Jayaraman, B. D. Kulkarni, Nadi Tarangini
  A Pulse Based Diagnostic System, In Proc. of the
  29th IEEE-EMBC Conf., pp. 2207-2210, 2007.
• Paper- A. J. Joshi, S. Chandran, V. K.
  Jayaraman, B. D. Kulkarni, Arterial Pulse
  System Modern Methods For Traditional Indian
  Medicine, In Proc. of the 29th IEEE-EMBC Conf.,
  pp. 608-611, 2007.

Aniruddha J. Joshi Research Scholar Computer
Science and Engineering Department, IIT Bombay
Dr. B. D. Kulkarni Scientist NCL, Pune
Dr. V. K. Jayaraman Scientist NCL, Pune
Dr. Anand Kulkarni Ph.D. in Chemical U.I.C.T.
Mumbai
Prof. Sharat Chandean Professor CSE, IIT Bombay
Dr. Ashok Bhat Ayurvedic Practitioner Pune