# Validation of a Novel Traditional Chinese Medicine Pulse Diagnostic

Authors: Anson Chui Yan Tang; Joanne Wai Yee Chung; Thomas Kwok Shing Wong
Journal: Evidence-Based Complementary and Alternative Medicine
 (2012)

---

## Abstract

In view of lacking a quantifiable traditional Chinese medicine (TCM) pulse
diagnostic model, a novel TCM pulse diagnostic model was introduced to quantify
the pulse diagnosis. Content validation was performed with a panel of TCM
doctors. Criterion validation was tested with essential hypertension. The gold
standard was brachial blood pressure measured by a sphygmomanometer. Two hundred
and sixty subjects were recruited (139 in the normotensive group and 121 in the
hypertensive group). A TCM doctor palpated pulses at left and right cun, guan,
and chi points, and quantified pulse qualities according to eight elements
(depth, rate, regularity, width, length, smoothness, stiffness, and strength) on
a visual analog scale. An artificial neural network was used to develop a pulse
diagnostic model differentiating essential hypertension from normotension.
Accuracy, specificity, and sensitivity were compared among various diagnostic
models. About 80% accuracy was attained among all models. Their specificity and
sensitivity varied, ranging from 70% to nearly 90%. It suggested that the novel
TCM pulse diagnostic model was valid in terms of its content and diagnostic
ability.


---

## Body

## 1. Introduction

Traditional
Chinese medicine (TCM) pulse diagnosis is one of the major clinical diagnostic
methods in TCM. It has been used by TCM doctors to assess patients’ health
conditions for several thousand years. A TCM doctor palpates six locations, three on
each wrist, with the three points called “cun”,
“guan”, and “chi” [1, 2], and describes pulses in terms of various
characteristics. By comparing the pulses at left and right cun, guan, and chi, the
health status of individual organs and of the whole body can be determined [1–3]. As specified in the ancient texts, the Nan Jing
[1] and Mai Jing [2], the heart, liver, and kidneys are assessed at the left cun, guan, and chi,
respectively, while the lungs, spleen, and kidneys are assessed at the right cun,
guan, and chi, respectively (Figure 1).The six locations and their corresponding organs [13].Numerous pulse qualities are documented in ancient Chinese medical texts. For
example, the Nei Jing describes over 30 types [4],
and the Mai Jing documents 24 types [2]. The most
common 28 TCM pulse qualities were used in clinical practice come from the
references Bin Hu Mai Xue  [5] and Zhen Jia Zheng Yan
[6]. These qualities are floating, sunken, slow,
rapid, surging, fine, vacuous, replete, long, short, slippery, rough, string-like,
tight, soggy, moderate, faint, weak, dissipated, hollow, drumskin, firm, hidden,
stirred, intermittent, bound, skipping, and racing. These pulse qualities are
largely described qualitatively and are not clearly defined. For instance, the
slippery is compared to “beads rolling” and the string-like is like
pressing the string of a musical instrument [5]. The
magnitude of a pulse quality is, similarly, not precisely defined. The difference
between a fine pulse and a faint pulse is that the former is a “little bit
stronger” than the latter [7]. “A
little” does not precisely determine what differentiates fine from faint.
Thus, descriptions of pulse are subject to the interpretation of individual doctors,
and this lack of quantitative standardization undoubtedly contributes to the low
inter- and intrarater reliability among TCM doctors [8–10]. King et
al. [11] tried to quantify TCM pulse diagnosis but
did not reflect pulse qualities adequately for several reasons. First, the six items
included in their scale-depth, width, force, relative force, rhythm, and pulse
occlusion are not widely accepted as core items in TCM pulse diagnosis. Second,
their scale was an ordinal scale anchored with descriptors to measure the items. For
example, depth was measured at three levels: superficial, middle, and deep. However,
an ordinal scale is not a sufficiently sensitive measure, as there are an
insufficient number of available response categories to rate the items [12], and the words used to describe each ordinal level
are not universal. Further, as the items have not been well quantified, using an
ordinal scale would not reflect the actual sensation perceived by a TCM doctor.To address these issues, the research team recontextualized TCM pulse diagnosis in an
explicit and quantifiable way. This is significant because an effective treatment
regimen relies on accurate clinical diagnostic data which can only be obtained by
using an assessment method with high accuracy.To recontextualize TCM pulse diagnosis, several issues must be addressed. First, the
qualitative descriptions of pulse qualities must be quantified and comparable.
Second, the integrity of the pulse diagnosis must be preserved. Third, it must
possess adequate validity in assessing health status. We aim at introducing a novel
TCM pulse diagnostic model to recontextualize TCM pulse diagnosis and validate the
model with essential hypertension. Essential hypertension is selected in this study
because much evidence shows an association between TCM pulse qualities and essential
hypertension [14–22]. The null hypothesis is that the TCM pulse
diagnostic model is not accurate in essential hypertension diagnosis.


### 1.1. TCM Pulse Diagnostic Model

Research papers and reviews found in several databases, including Chinese Medical
Current Contents (1994–2011), CBMdisc (1980–2011), and CAJ
Full-text Database, concur that TCM pulse quality should be described by eight
elements with various intensities. The eight elements are depth, rate,
regularity, width, length, smoothness, stiffness, and strength [7, 23–27]. Rate is the
number of beats per breath. The definition of regularity is similar to that in
Western medicine, in that it describes the rhythm of a pulse [14]. Depth is defined as the vertical position of a
pulse [14]. Width and length describe the shape
of a pulse, where width is defined as the intensity of a pulsation and length is
defined as the range in which the pulsation can be sensed across the cun, guan,
and chi [7]. Smoothness is defined as the
slickness of a pulse, stiffness is defined as the sensation of arterial
elasticity, and strength is defined as the change in forcefulness of a pulse in
response to a change of applied pressure [14].
The diagnostic model proposed in this paper is based on these eight elements.As illustrated in Figure2, a die was adopted to
represent the intertwining and cascading relationship among an arterial pulse
and the eight elements at the six locations and the health status. The dotted
line in Figure 2 links the six pyramids together
to symbolize the interchanging and dynamic relationship among the organs. Yin
and Yang of each element, the eight elements at each face, and the six pyramids
of the die are connected with dotted lines. They are always interchanging and
balancing one another. The solid line outlined the die represents the absolute
of health. The absolute of health means that it is not expandable or reducible,
the only thing that can be altered is the health status which is implicated by
the interaction of Yin and Yang of the body [28].
Specifically in this study, health status is referred as the blood pressure
state of a person.Figure 2
TCM pulse diagnostic model.


### 1.2. The Six Locations

The six faces are the six locations (left and right cun, guan, chi) where pulses
are assessed by a TCM doctor. The lung and the heart, the liver and the spleen
and, the kidney and the kidney (lifegate) are arranged in opposite faces
according to the location their health reflected. Such arrangement is based on
the fact that left cun, guan, and chi assess blood which is Yin in nature and
right cun, guan, and chi assess qi which is Yang in nature in TCM pulse
diagnosis [3, 28].


### 1.3. The Eight Elements

Each face is composed of the eight elements. The enlarged face at the lower right
shows the interrelation of the eight elements. Each element is a complementary
pair of Yin and Yang. According to Yin Yang theory, Yin is always inside and
Yang is always outside [28]. So, the black square
indicating the Yin nature of the elements is at the core of the face and the
white square indicating their Yang nature is at the outer part of the face. The
intensity of the eight elements depends on the arterial pulse. The combination
of the eight elements indicates the health status of the organs.Since this is a dynamic model with many sophisticated nonlinear relationships in
between constructs, artificial neural network (ANN) is an ideal modeling
technique to model these relationships [29, 30]. Validity of the ANN models was evaluated by
its accuracy, sensitivity, and specificity.


## 1.1. TCM Pulse Diagnostic Model

Research papers and reviews found in several databases, including Chinese Medical
Current Contents (1994–2011), CBMdisc (1980–2011), and CAJ
Full-text Database, concur that TCM pulse quality should be described by eight
elements with various intensities. The eight elements are depth, rate,
regularity, width, length, smoothness, stiffness, and strength [7, 23–27]. Rate is the
number of beats per breath. The definition of regularity is similar to that in
Western medicine, in that it describes the rhythm of a pulse [14]. Depth is defined as the vertical position of a
pulse [14]. Width and length describe the shape
of a pulse, where width is defined as the intensity of a pulsation and length is
defined as the range in which the pulsation can be sensed across the cun, guan,
and chi [7]. Smoothness is defined as the
slickness of a pulse, stiffness is defined as the sensation of arterial
elasticity, and strength is defined as the change in forcefulness of a pulse in
response to a change of applied pressure [14].
The diagnostic model proposed in this paper is based on these eight elements.As illustrated in Figure2, a die was adopted to
represent the intertwining and cascading relationship among an arterial pulse
and the eight elements at the six locations and the health status. The dotted
line in Figure 2 links the six pyramids together
to symbolize the interchanging and dynamic relationship among the organs. Yin
and Yang of each element, the eight elements at each face, and the six pyramids
of the die are connected with dotted lines. They are always interchanging and
balancing one another. The solid line outlined the die represents the absolute
of health. The absolute of health means that it is not expandable or reducible,
the only thing that can be altered is the health status which is implicated by
the interaction of Yin and Yang of the body [28].
Specifically in this study, health status is referred as the blood pressure
state of a person.Figure 2
TCM pulse diagnostic model.


## 1.2. The Six Locations

The six faces are the six locations (left and right cun, guan, chi) where pulses
are assessed by a TCM doctor. The lung and the heart, the liver and the spleen
and, the kidney and the kidney (lifegate) are arranged in opposite faces
according to the location their health reflected. Such arrangement is based on
the fact that left cun, guan, and chi assess blood which is Yin in nature and
right cun, guan, and chi assess qi which is Yang in nature in TCM pulse
diagnosis [3, 28].


## 1.3. The Eight Elements

Each face is composed of the eight elements. The enlarged face at the lower right
shows the interrelation of the eight elements. Each element is a complementary
pair of Yin and Yang. According to Yin Yang theory, Yin is always inside and
Yang is always outside [28]. So, the black square
indicating the Yin nature of the elements is at the core of the face and the
white square indicating their Yang nature is at the outer part of the face. The
intensity of the eight elements depends on the arterial pulse. The combination
of the eight elements indicates the health status of the organs.Since this is a dynamic model with many sophisticated nonlinear relationships in
between constructs, artificial neural network (ANN) is an ideal modeling
technique to model these relationships [29, 30]. Validity of the ANN models was evaluated by
its accuracy, sensitivity, and specificity.


## 2. Materials and Method

This was a cross-sectional study with two parts. Part 1 was the content validation
test. Part 2 focused on comparing accuracy, sensitivity, and specificity of the ANN
models for differentiating essential hypertension from normotension.


### 2.1. Content Validation

A TCM pulse assessment form was designed to measure the intensity of eight
elements at six locations. There were six sections in the form, corresponding to
the six locations. In each section, depth, rate, width, length, smoothness,
stiffness, and strength were rated using a visual analog scale (VAS).For depth, the scale ran from “deepest” to “most
floating”. Rate was scaled from “slowest” to
“fastest”, width from “smallest” to
“largest”, length from “shortest” to
“longest”, smoothness from “roughest” to
“smoothest”, stiffness from “least stiff” to
“stiffest”, and strength from “least forceful”
to “most forceful”. As length was measured across cun, guan, and
chi, it contained only two items-length at the left side and length at the right
side. Regularity was a categorical variable that was either regular (0) or
irregular (1).The content validation was performed by five TCM experts in a local university.
They commented on the relevancy of the items and the use of the anchoring words
at the ends of the VAS and rated the relevancy of the items on a four-point
scale. The four-point scale comprised “irrelevant”,
“somewhat relevant”, “relevant”, and
“very relevant”. A Content Validation Index (CVI) was calculated
as the percentage of agreement over the list of items among the five
experts.


### 2.2. Criterion Validation

Data collection took place at a TCM laboratory in the School of Nursing of the
university from June to October 2008. Ethical approval was obtained from the
Research Committee of the School of Nursing. The laboratory temperature was kept
at 22 degrees Celsius throughout the study period, as fluctuations in
ambient temperature are known to affect the pulse [7].


### 2.3. Selection and Descriptions of Participants

Volunteer subjects were recruited through quota sampling using e-mails, posters
displayed on the university campus and in community health centers and
hospitals, and newspaper advertisements. Inclusion and exclusion criteria
governed the subject selection, as summarized in Table1, for normotensive and hypertensive groups.Table 1
Selection criteria of normotensive and hypertensive subjects.
Selection criteriaNormotensionHypertensionInclusion criteria(i) Aged 18 or above(i) Aged 18 or above(ii) SBP <120 mmHg and DBP
<80 mmHg at
rest* [31](ii) Diagnosis of essential hypertension(iii) SBP≥140 mmHg/DBP ≥90 mmHg or
both* [31]Exclusion criteria(i) Pregnant(i) The same as those in normotension except the
current use of antihypertensive drugs(ii) Loss of upper extremities(iii) Chronic diseases(iv) Infectious diseases(v) Current use of any medication including prescription
from a medical doctor, herbal medicine and OTC drugs* SBP: systolic blood pressure; DBP: diastolic blood
pressure.Subjects eligible for the hypertensive group were required to stop taking
antihypertensive drugs one day before their appointment. The purpose, procedure,
and potential effects of the study were described and explained to the subjects,
who were told that they could withdraw from the study at any time without
penalty. Written consent was obtained before the data collection.At the beginning of data collection, the subjects were asked to lie on a bed for
20 minutes before data collection, as it has been demonstrated that hemodynamic
modification stabilizes after 20 minutes in a new position [32]. After a 20-minute rest, their demographic
data, blood pressure, and pulse readings were collected.The TCM doctor assessed pulses at left and right cun, guan, and chi and rated the
intensity of the eight elements on the validated TCM pulse assessment form while
the subject was in a supine position. The whole process took less than 30
minutes.Descriptive statistics were computed to determine the distribution of the data
with Statistical Package for Social Sciences (SPSS) version 15. Mann WhitneyU and independent t-tests were used to
compare mean difference of subjects’ clinical data. ANN was used to
compute the nonlinear relationship with MatLab 8.0.


### 2.4. Artificial Neural Network

Three factors were manipulated in the modeling: the number of hidden layers, the
number of hidden neurons, and the training algorithms used. As there is no
protocol in ANN studies to guide the training process, the three factors were
systematically adjusted during the modeling to generate the best results.To increase the efficiency of the training, the input data and target data were
preprocessed before training commenced. The input and target data were first
normalized to a zero mean and a unity standard deviation, and the numbers of
neurons in the input and output layers were fixed. The dependent variable was a
group (either normotension or hypertension), which was assigned a value of 0 for
normotension and 1 for hypertension. The eight elements at the six locations
were the independent variables; the input neurons were the eight elements at the
six locations, and the output neuron was a group.A three-layer ANN was initially used, and the number of hidden neurons was set at
10, and increased at five-neuron intervals until the performance of the model
leveled off or decreased. Both backpropagation and radial basis networks were
tried during modeling. Bayesian Regularization, Levenberg-Marquardt, and
Resilient Backpropagation algorithms were used for the backpropagation.
Log-sigmoid function and pure linear function were used as the transfer
functions to connect the input layer to the first hidden layer and the hidden
layer to the output layer, respectively. If the performance was not
satisfactory, then a four-layer ANN was tried.A probabilistic neural network is a radial basis network that uses a Bayesian
classifier to estimate the probability. There is only one hidden layer in the
network, and the radial basis function is used as the transfer function. The
transfer function connecting the hidden layer to the output layer is a compete
function that chooses the class with the largest probability [33].The performance of the models was evaluated according to their sensitivity,
specificity, and predictive accuracy.


## 2.1. Content Validation

A TCM pulse assessment form was designed to measure the intensity of eight
elements at six locations. There were six sections in the form, corresponding to
the six locations. In each section, depth, rate, width, length, smoothness,
stiffness, and strength were rated using a visual analog scale (VAS).For depth, the scale ran from “deepest” to “most
floating”. Rate was scaled from “slowest” to
“fastest”, width from “smallest” to
“largest”, length from “shortest” to
“longest”, smoothness from “roughest” to
“smoothest”, stiffness from “least stiff” to
“stiffest”, and strength from “least forceful”
to “most forceful”. As length was measured across cun, guan, and
chi, it contained only two items-length at the left side and length at the right
side. Regularity was a categorical variable that was either regular (0) or
irregular (1).The content validation was performed by five TCM experts in a local university.
They commented on the relevancy of the items and the use of the anchoring words
at the ends of the VAS and rated the relevancy of the items on a four-point
scale. The four-point scale comprised “irrelevant”,
“somewhat relevant”, “relevant”, and
“very relevant”. A Content Validation Index (CVI) was calculated
as the percentage of agreement over the list of items among the five
experts.


## 2.2. Criterion Validation

Data collection took place at a TCM laboratory in the School of Nursing of the
university from June to October 2008. Ethical approval was obtained from the
Research Committee of the School of Nursing. The laboratory temperature was kept
at 22 degrees Celsius throughout the study period, as fluctuations in
ambient temperature are known to affect the pulse [7].


## 2.3. Selection and Descriptions of Participants

Volunteer subjects were recruited through quota sampling using e-mails, posters
displayed on the university campus and in community health centers and
hospitals, and newspaper advertisements. Inclusion and exclusion criteria
governed the subject selection, as summarized in Table1, for normotensive and hypertensive groups.Table 1
Selection criteria of normotensive and hypertensive subjects.
Selection criteriaNormotensionHypertensionInclusion criteria(i) Aged 18 or above(i) Aged 18 or above(ii) SBP <120 mmHg and DBP
<80 mmHg at
rest* [31](ii) Diagnosis of essential hypertension(iii) SBP≥140 mmHg/DBP ≥90 mmHg or
both* [31]Exclusion criteria(i) Pregnant(i) The same as those in normotension except the
current use of antihypertensive drugs(ii) Loss of upper extremities(iii) Chronic diseases(iv) Infectious diseases(v) Current use of any medication including prescription
from a medical doctor, herbal medicine and OTC drugs* SBP: systolic blood pressure; DBP: diastolic blood
pressure.Subjects eligible for the hypertensive group were required to stop taking
antihypertensive drugs one day before their appointment. The purpose, procedure,
and potential effects of the study were described and explained to the subjects,
who were told that they could withdraw from the study at any time without
penalty. Written consent was obtained before the data collection.At the beginning of data collection, the subjects were asked to lie on a bed for
20 minutes before data collection, as it has been demonstrated that hemodynamic
modification stabilizes after 20 minutes in a new position [32]. After a 20-minute rest, their demographic
data, blood pressure, and pulse readings were collected.The TCM doctor assessed pulses at left and right cun, guan, and chi and rated the
intensity of the eight elements on the validated TCM pulse assessment form while
the subject was in a supine position. The whole process took less than 30
minutes.Descriptive statistics were computed to determine the distribution of the data
with Statistical Package for Social Sciences (SPSS) version 15. Mann WhitneyU and independent t-tests were used to
compare mean difference of subjects’ clinical data. ANN was used to
compute the nonlinear relationship with MatLab 8.0.


## 2.4. Artificial Neural Network

Three factors were manipulated in the modeling: the number of hidden layers, the
number of hidden neurons, and the training algorithms used. As there is no
protocol in ANN studies to guide the training process, the three factors were
systematically adjusted during the modeling to generate the best results.To increase the efficiency of the training, the input data and target data were
preprocessed before training commenced. The input and target data were first
normalized to a zero mean and a unity standard deviation, and the numbers of
neurons in the input and output layers were fixed. The dependent variable was a
group (either normotension or hypertension), which was assigned a value of 0 for
normotension and 1 for hypertension. The eight elements at the six locations
were the independent variables; the input neurons were the eight elements at the
six locations, and the output neuron was a group.A three-layer ANN was initially used, and the number of hidden neurons was set at
10, and increased at five-neuron intervals until the performance of the model
leveled off or decreased. Both backpropagation and radial basis networks were
tried during modeling. Bayesian Regularization, Levenberg-Marquardt, and
Resilient Backpropagation algorithms were used for the backpropagation.
Log-sigmoid function and pure linear function were used as the transfer
functions to connect the input layer to the first hidden layer and the hidden
layer to the output layer, respectively. If the performance was not
satisfactory, then a four-layer ANN was tried.A probabilistic neural network is a radial basis network that uses a Bayesian
classifier to estimate the probability. There is only one hidden layer in the
network, and the radial basis function is used as the transfer function. The
transfer function connecting the hidden layer to the output layer is a compete
function that chooses the class with the largest probability [33].The performance of the models was evaluated according to their sensitivity,
specificity, and predictive accuracy.


## 3. Results

### 3.1. Content Validation

There were 44 items in the TCM pulse assessment form. Thirty-two items were
agreed upon by all five experts, but two experts disagreed on items 3, 7, 10,
14, 17, 21, 25, 29, 32, 36, 39, and 43. “Agreed” refers to a
rating of “relevant” or “very relevant” and
“Disagreed” to a rating of “irrelevant” or
“somewhat relevant.” The two experts that disagreed on the
twelve items explained that although there was no standard for assessing pulse
qualities in TCM, they did not think that the twelve items were good enough to
represent TCM pulse diagnosis. However, as the majority of the experts on the
panel agreed on these items, the researcher retained these items on the TCM
pulse assessment form.To compute the percentage of agreement, the number of “agreed”
items was first calculated and then divided by the total number of items
retained in the content validity assessment rating form.(1)%ofagreement(CVI)=numberofagreeditems/totalnumberofitemsretained=3244=0.73.The content validity index was thus 0.73, which is acceptable.


### 3.2. Criterion Validation

260 subjects were recruited, of which 139 were in the normotensive group and 121
were in the hypertensive group. A Mann WhitneyU-test was used
to examine the differences in gender, age, and body mass index between the
groups. The differences in pulse rate, left-side blood pressure, and right-side
blood pressure were tested by using an independent t-test. The
demographic information, blood pressures, body mass index, and pulse rate of the
subjects, is presented in Table 2. There was
no statistically significant difference in gender, age, or body mass index
between the groups (P>0.05), but a statistically significant difference in pulse rate, left-side
blood pressure, and right-side blood pressure was found (P<0.01).Table 2
Background information on the subjects(N=260).
Normotensive groupψ(n=139)Hypertensive groupψ(n=121)Significance*(P<0.05)GenderMale5553–-Female8468Age (yrs)18–34439–-35–648693≥651019BMI (kg/m2)<18.50125–-18.50–22.9910396≥23.002420LBP (mmHg)112 (13)/68 (7)150 (16)/95 (11)0.01RBP (mmHg)113 (13)/68 (7)150 (15)/95 (11)0.01Pulse Rate (bpm)65 (10)70 (11)0.01BMI: Body mass index; LBP: Left-side blood pressure; RBP:
Right-side blood pressure.ψThe mean (SD) is reported for the continuous variables
and the frequency for the categorical
variables.*P
< 0.05 denotes statistical significance.Table3 compares the results of three
back-propagation training algorithms with 10, 15, 20, and 25 hidden neurons. The
value reported is the average of 10 trainings. The accuracy of the three
algorithms increased slightly with an increasing number of hidden neurons, and
ranged between 0.74 and 0.79. Their specificity, however, dropped with an
increasing number of hidden neurons, and their sensitivity stopped increasing at
15 hidden neurons.Table 3
Comparison of the specificity, sensitivity, and accuracy of the three
back-propagation training algorithms with different numbers of
hidden neurons(N=260).
AlgorithmNumber of hidden neuronsSpecificity(%)Sensitivity(%)Accuracy(%)Bayesian regularization1069.9676.5773.501573.4684.4979.272073.2084.8079.302569.3384.8077.51Levenberg-Marquardt Algorithm1068.2986.7578.061563.1790.8877.832063.6788.0476.562562.9387.4075.87Resilient backpropagation1072.1983.9178.411563.6791.3378.282066.6191.3079.672565.8590.2278.74The specificity, sensitivity, and accuracy of the ANN generated with the
probabilistic neural network was 0.68, 0.78, 0.74, respectively (Table4). Table 4
compares the best model for each ANN algorithm and the model developed by
logistic regression.Table 4
Comparison of the specificity, sensitivity, and accuracy of the best
results of the models using different ANN training algorithms and
the logistic regression.
AlgorithmSpecificity(%)Sensitivity(%)Accuracy(%)RemarksBayesian regularization73.4684.4979.2715 hidden neuronsLevenberg-Marquardt68.2986.7578.0610 hidden neuronsResilient backpropagation72.1983.9178.4110 hidden neuronsProbabilistic neural network68.4978.3273.76—A model with a high true positive rate is preferable, which means that the
sensitivity should be sufficiently high while the specificity is preserved. The
models developed by Bayesian regularization and resilient back-propagation
achieved a similar specificity and sensitivity, which establishes that either
Bayesian regularization or resilient back-propagation is the best training
algorithm for developing the diagnostic model. The results rejected the null
hypothesis that the TCM pulse diagnostic model cannot differentiate essential
hypertension from normotension.


## 3.1. Content Validation

There were 44 items in the TCM pulse assessment form. Thirty-two items were
agreed upon by all five experts, but two experts disagreed on items 3, 7, 10,
14, 17, 21, 25, 29, 32, 36, 39, and 43. “Agreed” refers to a
rating of “relevant” or “very relevant” and
“Disagreed” to a rating of “irrelevant” or
“somewhat relevant.” The two experts that disagreed on the
twelve items explained that although there was no standard for assessing pulse
qualities in TCM, they did not think that the twelve items were good enough to
represent TCM pulse diagnosis. However, as the majority of the experts on the
panel agreed on these items, the researcher retained these items on the TCM
pulse assessment form.To compute the percentage of agreement, the number of “agreed”
items was first calculated and then divided by the total number of items
retained in the content validity assessment rating form.(1)%ofagreement(CVI)=numberofagreeditems/totalnumberofitemsretained=3244=0.73.The content validity index was thus 0.73, which is acceptable.


## 3.2. Criterion Validation

260 subjects were recruited, of which 139 were in the normotensive group and 121
were in the hypertensive group. A Mann WhitneyU-test was used
to examine the differences in gender, age, and body mass index between the
groups. The differences in pulse rate, left-side blood pressure, and right-side
blood pressure were tested by using an independent t-test. The
demographic information, blood pressures, body mass index, and pulse rate of the
subjects, is presented in Table 2. There was
no statistically significant difference in gender, age, or body mass index
between the groups (P>0.05), but a statistically significant difference in pulse rate, left-side
blood pressure, and right-side blood pressure was found (P<0.01).Table 2
Background information on the subjects(N=260).
Normotensive groupψ(n=139)Hypertensive groupψ(n=121)Significance*(P<0.05)GenderMale5553–-Female8468Age (yrs)18–34439–-35–648693≥651019BMI (kg/m2)<18.50125–-18.50–22.9910396≥23.002420LBP (mmHg)112 (13)/68 (7)150 (16)/95 (11)0.01RBP (mmHg)113 (13)/68 (7)150 (15)/95 (11)0.01Pulse Rate (bpm)65 (10)70 (11)0.01BMI: Body mass index; LBP: Left-side blood pressure; RBP:
Right-side blood pressure.ψThe mean (SD) is reported for the continuous variables
and the frequency for the categorical
variables.*P
< 0.05 denotes statistical significance.Table3 compares the results of three
back-propagation training algorithms with 10, 15, 20, and 25 hidden neurons. The
value reported is the average of 10 trainings. The accuracy of the three
algorithms increased slightly with an increasing number of hidden neurons, and
ranged between 0.74 and 0.79. Their specificity, however, dropped with an
increasing number of hidden neurons, and their sensitivity stopped increasing at
15 hidden neurons.Table 3
Comparison of the specificity, sensitivity, and accuracy of the three
back-propagation training algorithms with different numbers of
hidden neurons(N=260).
AlgorithmNumber of hidden neuronsSpecificity(%)Sensitivity(%)Accuracy(%)Bayesian regularization1069.9676.5773.501573.4684.4979.272073.2084.8079.302569.3384.8077.51Levenberg-Marquardt Algorithm1068.2986.7578.061563.1790.8877.832063.6788.0476.562562.9387.4075.87Resilient backpropagation1072.1983.9178.411563.6791.3378.282066.6191.3079.672565.8590.2278.74The specificity, sensitivity, and accuracy of the ANN generated with the
probabilistic neural network was 0.68, 0.78, 0.74, respectively (Table4). Table 4
compares the best model for each ANN algorithm and the model developed by
logistic regression.Table 4
Comparison of the specificity, sensitivity, and accuracy of the best
results of the models using different ANN training algorithms and
the logistic regression.
AlgorithmSpecificity(%)Sensitivity(%)Accuracy(%)RemarksBayesian regularization73.4684.4979.2715 hidden neuronsLevenberg-Marquardt68.2986.7578.0610 hidden neuronsResilient backpropagation72.1983.9178.4110 hidden neuronsProbabilistic neural network68.4978.3273.76—A model with a high true positive rate is preferable, which means that the
sensitivity should be sufficiently high while the specificity is preserved. The
models developed by Bayesian regularization and resilient back-propagation
achieved a similar specificity and sensitivity, which establishes that either
Bayesian regularization or resilient back-propagation is the best training
algorithm for developing the diagnostic model. The results rejected the null
hypothesis that the TCM pulse diagnostic model cannot differentiate essential
hypertension from normotension.


## 4. Discussion

This is the first study proposing a model quantifying TCM pulse diagnosis with the
eight elements at the six locations. The result of content validation suggests that
the model fits into the content of TCM pulse diagnosis. As compared to our study,
the one proposed by King et al. [11] did not meet the
assessment criteria of TCM pulse diagnosis which is the eight elements at the six
locations. Our result is therefore more relevant to TCM pulse diagnosis.In our study, VAS was used to measure the intensity of the eight elements. VAS is a
method used to assess subjective experience, which in this study was the intensity
of the eight elements [34]. A VAS consists of a
10 cm line with both ends marked with anchors. The method is simple to use
and its validity and reliability have been established [35]. It is congruent with the usual practice of pulse assessment in TCM
and provides various ratings for a TCM doctor to choose that do not mask the actual
sensation felt by the doctor. A numerical or ordinal scale would compress the
information collected, because the choice would be restricted and the descriptors
and numerals anchored to the numerical or ordinal scale could potentially introduce
subjective judgment. As a VAS is administered in paper and pen format and requires
the researcher to measure a mark made on the line with a ruler, measurement error
can occur. However, it has been showed that the VAS has good construct validity in
clinical and research use [36, 37] and fits the concept of TCM pulse diagnosis. It was thus used to
measure the intensity of the eight elements.The results of criterion validation demonstrated that the proposed model is valid
enough, as reflected in its accuracy, sensitivity, and specificity, to discern
essential hypertension from normotension. The hypothesis set in this study is
therefore rejected. Nonetheless, it must also be noted that several hypotheses in
the model are yet to be falsified. The basis of TCM pulse diagnosis is Yin Yang
theory and the Eight Principles. The bipolar Yin Yang nature of each element has not
been examined in this study. As the ultimate purpose of TCM pulse diagnosis is to
help in syndrome differentiation which is based on the Eight Principles, the model
should therefore comply with the Eight Principles. Chinese medical texts do mention
the correspondence of each element with the three bipolar pairs (exterior/interior,
heat/cold, excess/deficiency) in the Eight Principles. Depth indicates the
interior-exterior pair; rate and regularity indicates the heat-cold pair; width,
length, smoothness, stiffness, and strength indicate excess-deficiency pair [7, 14]. But no evidence
is so far available to verify these claims. Future studies are recommended to focus
on falsifying these hypotheses. In addition, the weight of each element and location
on a specific health status is not readily retrieved due to the black-box nature of
ANN model [38]. With more advanced and powerful
computers, we believe that more information could be retrieved from the model.This study has several limitations. The subjects recruited were in a stable
condition, and thus the model developed is confined to stable hypertensive cases and
cannot be extrapolated to other conditions such as severe hypertension and
hypotension as described by National Heart Lung and Blood Institute [31]. It is recommended to recruit subjects with severe
hypertension or hypotension in future studies to increase the generalizability of
the model. Also, we cannot generalize to other disease differentiation because the
results in this study only validated the model with essential hypertension. It is
recommended to validate the model by comparing its accuracy with the differentiation
of other diseases in future studies. Finally, because only one TCM doctor was
recruited to perform pulse diagnosis, individual bias may have affected the results.


## 5. Conclusions

This was the first study presenting a quantifiable TCM pulse diagnostic model with
the eight elements at the six locations. The results demonstrated the
model’s content matched with the context of TCM pulse diagnosis and it
attained an acceptable accuracy, sensitivity, and specificity in the context of
essential hypertension. Further works are required to verify other hypotheses in the
model as pointed out in previous paragraphs.



---

*Source: 685094-2011-09-13.xml*