ORIGINAL PAPER

The Systematical Analysis of Oriental Pulse Waveform:A Practical Approach

Junyoung Lee

Received: 21 August 2007 /Accepted: 7 September 2007 /Published online: 22 September 2007# Springer Science + Business Media, LLC 2007

Abstract With the view to set up the oriental pulsedatabase as well as objective diagnosis standards, this studyhas designed and manufactured a digital pulse diagnosissystem that uses a high-performance microprocessor on thebasis of systematic pulse diagnosis methodology. Thealgorithm for extracting significant points has been pro-posed to precisely interpret the pulse signals that havevarious kinds of noises, and pulse measurement tests wereconducted on many patients at a hospital. Much of theclinical data attained by the digital pulse diagnosis systemhas been compared with the clinical findings made by thedoctors in charge of patients. As a result of this comparisonand analysis, the study has found out that the two findingsshowed almost identical opinions. On this basis, anobjective diagnostic parameter clinical diagnosis has beenpresented.

Keywords Pulse database . Pulse diagnosis .

Significant points

Introduction

From time immemorial, in oriental medicine, diagnosis hadbeen practiced by determining the unbalance of the physi-ological function of the five viscera and six bowers in thehuman body from the condition of blood circulationperformed by the vitality of the heart, and treatment of

disease had been attempted by adjusting the thus-determinedunbalance. There were many methods of pulse feelingdiagnoses. Among these, the most outstanding method wasthat of three regions (Chon, Kwan and Chuk) and ninecomplaints with which the weakness or substantiality of thefive viscera and the six bowels and those of the 12 meridianpulses were simultaneously determined so that the weaknessand substantiality of the physiological function of visceraand bowels and meridians were simultaneously identified.When palpation is closely combined with looking, hearing,or speaking inspection, it will, most of all, be helpful indeciding the nature of a disease and inferring the realcharacter of that disease. The aforementioned four kinds ofdiagnosis methods are also referred to as the 4-diagnosismethod in Eastern medicine. Especially with a pulse that hasceased to beat, one of the methods of palpation, which isotherwise called pulse diagnosis, has held an extremelyimportant position in diagnostics [1, 2]. Such pulsediagnosis stems from the experiences acquired fromrepeated practices in the course of fighting against similardiseases over a long period of time from remote ages.Because pulse diagnosis method diagnoses diseases fromthe changes that take place in a body, it has been consideredan effective diagnosis method. Furthermore, pulse diagno-sis has been an indispensably important objective basis fordeciding the nature of a disease since old times. In thissense, many oriental doctors have highly valued and widelyused pulse diagnosis. Nevertheless, in the case of theexisting pulse wave diagnosis, only a highly experiencedoriental physician could diagnose Negative(Yin), Positive(Yang), Superficial, Deep, Cold Nature, Hot Nature,Deficient and Excessive of a disease, checking pulses bylooking at the analogue type of pulse wave signals printedby a thermo-transcript printer and combining 3-diagnosessuch as looking, hearing and speaking inspection [2]. Based

J Med Syst (2008) 32:9–15DOI 10.1007/s10916-007-9101-0

J. Lee (*)Department of Computer and Electronic Engineering,Myongji College,356-1 Hongeun3-Dong, Seodaemun-Gu,Seoul 120-776, South Koreae-mail: jylee@mjc.ac.kr

on such a systematic pulse wave diagnosis method, thepaper presents practical analysis that will be beneficial toclinical application on the basis of its analysis of thefiltering for eliminating noises from pulse wave signalsinputted from sensor group, the digital hardware dealingwith signals necessary for recognition algorithm, and thestructure of diagnosis algorithm and components of pulsewaveform [3–8].

Materials and methods

Components of pulse waveform analyzed

a. Distinction between Slack and Rapid; distinctionbetween Cold and Hot (Fig. 1)Fifteen millimeters or less intervals to next pulse from a

pulse are Rapid pulse, and 24 mm or more ones are Slackpulse. Intervals between 15 and 24 mm are normal and canbe ignored. This analysis for pulse shows only symptoms,but has no relation to the cause.b. Distinction between Superficial and Deep pulse; dis-

tinction between Wood and Earth (Figs. 2, 3)

① Strength of a pulse : assuming big front and smallback② Scope of pulse pressure that can be felt:70∼140 mmHg③ Explanation that the change rate of flowing pulseforce is high at first and lower later.

(High change rate means that the flow of force is steep)|A|>|B| Superficial Pulse – Wood(shape: big front and

small back)|A|<|B| Deep Pulse – Earth (shape: small front and big

back)

The meanings (Wood, Earth) are endowed to Superficialand Deep for a pulse wave when they are combined withother pulse waves. In other words, such a single pulse waveas Superficial or Deep pulse cannot exist.c. Analysis of size(amplitude); distinction between Water

and Hot (Fig. 4)

Water signifies that it includes causes and symptoms.Hot signifies that symptoms exist but the cause cannot

be known.

This means that the pulse diagnosis system representspseudo-Hot, resulting from frequent differentiated amplifi-cation of 25 Hz signal, which is the average frequency of ahuman body, in manufacturing process of pulse diagnosissystem.d. Analysis of phase angle size (number of tip points);

distinction between Water and Fire (Fig. 5)Big phase angle: Change in a pulse wave is steep.

(marked as real Cold and real Hot)

Fig. 1 Distinction between slack and rapid

Fig. 2 Distinction between superficial and deep

Fig. 3 Distinction between su-perficial and deep

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When the number of vertices is 5 or more,String pulse: Connection line between vertices is slash or

chord. It has the feature of Wood and Fire at the same time.e. Whether or not there is wave interference (Are there

rough things?; Nature of Metal; Fig. 6)f. Whether or not there is wave transformation (Whether

there are bended ones or not?; Fig. 7)That a pulse wave is not acute and is bent means that

viscosity of pulse energy is high.

*Bending toward positive direction: Earth + Fire===> Hot Sputum*Bending toward negative direction: Earth + Water===> Cold Sputum

Hide Pulse Extreme bending rate. It represents thestate of which Cold Damp is extremelystagnated.

Smooth Pulse The result which appears after beingcombined with other pulse waves

Stone leek Pulse The result which appears in a singlepulse wave.

g. Special Pulse Waves (Figs. 8, 9)

(1) Urgent Pulse(Earth, Water and Wood)

Shape Width to offset from onset of a pulse wave isaround 1 mm (normal pulse waves are approximate

between 3 and 5 mm). About 2 mm does not appearin the graph. This means that some nature of apulse wave is hidden in the graph.

Nature Whereas Wood and Water nature can beimmediately known, the nature of Earth ishidden. Therefore, it is classified as a specialpulse wave.

(2) Knot pulse(Wood, Fire, Earth, Metal and Water)

Shape Lines moved parallel to center line withnegative or positive position (it could beunderstood as repetition of numerous signals).Normally, it is between 2 and 5 mm. But whatneeds to be noted here is that the size ofamplitude which Knot pulse has is ignored.Even the Cases of 1 or 0.5 mm should beprecisely distinguished.

Nature It is estimated that it is combined with the wholenature of Wood, Fire, Earth, Metal and Water.

Diagnosis It can be concluded as a cancer. (All cases ofKnot pulse can be concluded to be a cancer, but acancer does not always occur as Knot pulse)

h. Normal Pulse (Figs. 10, 11)Conditions of pulse waves that become the subject of

treatment.

Fig. 4 Analysis of amplitude

<--Slack pulse(cold) |--- Normal pulse ---| Rapid pulse(hot) -->

The number of vertices : 0 1 2 3 4 5 6 7 8 9 10 11

Nature of a pulse wave : Water Water water ................................. fire Fire Fire

J Med Syst (2008) 32:9–15 1111

① It should not be biased up or down.② Amplitude should be between 8 and 15 mm.③ The number of vertices should be between 3 and 6.④ It should not be interfered by rough force.⑤ Bended shapes should not exist.

Below graphs are different from each other in the strictsense of engineering but are identical clinically.i. Classification of pulse waves by nature

Wood; Rise Fire; Rapid, Big Earth; SinkMetal; Rough Water; Small, Slow

Diagnosis algorithm of pulse waveform

Pulse waves that record the electric activities of pulses canbe said to be important means to diagnose the state of apatient [4]. The pulse signal processing designed for pulsewave diagnosis is performed through a diagnosis process ofwhich the state of a patient is determined by recognizingthe pattern of pulse wave signals, extracting dividing pointsused for diagnosis, and applying diagnosing criteria. Themost important part of the diagnosis is a pattern recognitionprocess which classifies the pattern of each waveform afterrecognizing dividing points representing onset and offsetpoints for each waveform forming pulse waves. Only whenexact pattern recognition is performed can the diagnosisparameter used in the diagnosis be precisely drawn. Theflow chart for the system to diagnose pulse waves is seen inFig. 12. Diagnosing algorithm is the one to diagnose allkinds of diseases by using digital pulse signals taken fromthe patient. The most important aspect of diagnosisalgorithm is to recognize the patterns of pulse waves andto extract precise dividing points from them [9–12].

Results

This system uses the zerocrossing method for extractingdividing points. The derivative type used at this point hasan outstanding capability in detecting significant points,thanks to its effect of eliminating high frequency noises[10, 11]. Henceforth, it is easy to detect significant points inrelatively mild waveform. However, the zerocrossingdetection method has a defect of slightly deviating from acorrect position in the onset and offset points [7]. Thisusually occurs when there are noises in the vicinity ofsignificant points, so in order to supplement this case, theinflection rate is used. If the inflection rate is used, realsignificant points can be precisely found in all cases as wellas in the case of which noises are inserted. The significantpoints which are found by such a method are classified asonset, peak, and offset points by their shapes. In these

Fig. 5 Characteristics of frequency transformer

Fig. 6 Interference of pulse wave

Fig. 7 Various shape of transformed pulse signal

Fig. 8 Shape of urgent pulse

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points, pattern matching is carried out for each pointaccording to AND/OR layer diagram, and these points areclassified by waveform. Pattern matching is performed inaccordance with correlation expression, and its conditionsare limited to numerical value, symbol, etc with a view toestimate its conformity [10, 11]. Diagnosing algorithmsemployed to this system are largely divided into six stagesas follows:

(1) Detection of significant points(2) Calculation of property values of significant points(3) Pattern matching and classification(4) Extraction of parameters(5) Diagnosis(6) Transformation of codes

The first process of interpreting pulse waves is a processof feature extraction. This process is divided into twostages. Waveform recognition is its first process in which itis detected whether or not there is a waveform to determinethe special features in pulse wave signals of a patient andthe position and size of each waveform is analyzed out. Thesecond process is a stage to extract parameters required fordiagnosing each waveform, which is recognized throughwaveform recognition stage. The position, size, onset point,and offset point of each waveform are used to calculateparameters for diagnosis. The information about eachwaveform obtained through these processes is used asinput parameters for diagnosis. In this study, we design thealgorithm to detect significant points of pulse wave signalsso that efficient diagnosis can be made, as well as thealgorithm which can detect the conformity of significantpoint candidate extracted by the means of the aforemen-tioned algorithm.

Discussion

We have developed the digital hardware system whichperforms signal processing necessary for recognitionalgorithm, the filtering to extract diagnosing parameter,and the filtering to eliminate noises by inputting pulse wavesignals from the sensor group. With a view to obtainclinically effective information, we analyzed structuralelements of pulse waveform (Figs. 13, 14, 15 and 16)and, thus, conducted a systematic classification. What ismore, we performed the modeling of the digital filter byusing the Steiglitz-McBride iteration method in order to getthe same results with output signals coming out ofgalvanometer of analogue type of existing Pulse diagnosissystem with input signals entering into galvanometer andcoming out of amp group of Pulse diagnosis system.

Fig. 10 Shape of normal pulse—I

Fig. 11 Shape of normal pulse—II

Fig. 12 Flowchart of pulse diagnosis system

Fig. 9 Shape of knot pulse

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In the future, we plan to improve performance bykeeping up with the study of setting up large-scale Pulse-Graph database on the basis of developed system throughcontinued clinic experiments along with the optimization,simplification, and lightening of system as well as thecomplementation of algorithms.

In addition, in order to build the clinical pulse database,which is the standard of objective diagnosis, this study hasdeveloped the web-based pulse database system with whichthe diagnosis parameter necessary for clinics can be analyzedby checking the pulse wave signals of patients which uses thedigital pulse wave system on the internet and by sending themeasured data through designated database server.

Fig. 13 Analysis of pulse waveform I

Fig. 14 Analysis of pulse waveform II

Fig. 15 Analysis of pulse waveform III

Fig. 16 Analysis of pulse waveform IV

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(1) By developing bio-signal acquisition system, includingthe pulse signal acquisition module and data interfacemodule, the study has made it possible to digitally storeand effectively handle signals of pulse data.

(2) The system has been developed on the basis of clinicalstudies on patients. It has operated very stably, andcould record and restore the pulse signals of patientssuccessfully.

(3) The points that have important information on outputwaveform were first selected. And then the secondoutput waveform was extracted by applying interpo-lation method on the selected points. After that, by usingthe Steiglitz-Mcbride method on the second outputwaveform, the convergent digital filter was designed.

(4) By eliminating noise signals existing between the peakpoints by means of merge algorithm, the study hasclarified the classification of peak points, which arethe major points of pulse waves.

(5) The study has confirmed that the attribute of potentialsignificant points determined by the threshold level ofslope is used as a variable point in judging the suitabilityin the merge algorithm that extracts noise data.

(6) The signal classification of peak point and valley,which have significant meaning in oriental medicine,can be expressed as the function of attributes thatshow six patterns.

(7) The study has proposed that the comparison betweenupper and lower parts of amplitude in pulse diagnosis,the size of amplitude, the number of vertexes, theinterval between pulse waves, the existence ofinterfering waves, and the existence of curved formare important parameters in diagnosing the patients inoriental medicine. It has also proposed that the changerate of pulse signals, the frequency sizes, the existenceof interfering waves, time delay, and the shape of peakpoints are also another important parameters inanalyzing the relationship with relevant organs.

(8) If the threshold level set by the tests is treatedoptimally by the waveform, the highly reliablealgorithm can be implemented. Furthermore, thedatabase can be effectively managed by reducing thesize of data compression and transmission data bymeans of the coordinates of peak points extracted byapplying significant point extraction algorithm.

(9) By setting up the Web-based database managementsystem, the treatment record of patients as well asstatistical diagnosis data can be exchanged through the

Internet even from remote areas. This has promptedthe establishment of database of diagnosis results.

As such, the clinical application and analysis of thedigital pulse diagnosis system proposed in this study are ofgreat significance in that they have enhanced the objectivityand preciseness of pulse diagnosis. There should becontinued clinical studies on building a large-capacity pulsedatabase, which will open a way for the comparison andanalysis of diagnostic finding and clinical results. This willin turn propel continuous study on standardization ofsymptoms that will clearly set up the clinical diagnosticcriteria. One of the ways to utilize this study results islinking the pulse database system with hospitals through theInternet, making telemedicine and consultation possible. Onthe basis of numerous clinical data obtained through thisprocess, the standardized database of pulse waveform canbe distributed on a regular basis by being manufactured asCD-ROM once or twice a year.

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