POSSIBILITY OF MENTAL HEALTH SELF-CHECKS USING

DIVERGENCE PROPERTIES OF PULSE WAVES

Mayumi Oyama-Higa

Department of Integrated Psychological Science, Kwansei Gakuin University, Uegahara, Nishinomiya, Hyogo, Japan

Tiejun Miao

CCI Corporation and Chaos Technology Research Laboratory, kitashinagawa, shinagawa-ku, Tokyo, Japan

Keywords: Chaotic analysis, divergence, fingertip pulse waves, Lyapunov exponent, PC mouse, mental health.

Abstract: We conducted a nonlinear analysis of fingertip pulse waves and found that the Lyapunov exponent having

the “divergence” property of attractor trajectory was an effective index for estimating human mental health.

We showed that this method is effective for an early detection of dementia and depression, as well as in the

detection of changes in mental status. In addition, based on these results obtained from time series analysis

of the recorded pulse waves, we developed an application device allowing easily installed and convenient

measurement for daily check and monitoring mental/physical status. It was an easy-to-use and cost-less

device installed in a PC mouse. Also, we studied a representation method of constellation graphs to disclose

the fluctuation details of the Lyapunov exponents. In the representation, changes in mental status were

assessed and graphically visible by using of the fluctuation factor of the Lyapunov exponents.

1 INTRODUCTION

Some serious mental health problems exist in Japan.

For example, the number of annual suicides has

reached 30,000 for three consecutive years 2004 to

2006.Most suicides are related to depressive

symptoms. In addition, although Japan has the

world’s highest longevity rate, the cases of dementia

increase along with the rapidly increase in the aged

population, thereby leading to some social problems

(ref. plala, http). Social and family responses are

essential to help those with depression and dementia,

but in most cases, these diseases progress without

self-acknowledged, and hence need the necessary

methods for an early detection and treatment.

It is generally necessary to check the status of

behavior and mental health in daily life to detect the

onset of depression and dementia. Subjective

observation alone is insufficient; it is required to

evaluate objective data using scientific methods. So

far, scientific methods include the analysis of brain

waves and image diagnosis of the brain, which

require high levels of technology and knowledge;

these are not simple measurement methods in terms

of time or cost. Therefore, easy and economical

measurement methods are required.

The Lyapunov exponent referencing the

“divergence” of an attractor trajectory in the

nonlinear analysis of fingertip pulse waves is an

effective method for assessing mental health in

humans (Tsuda 1992). In particular, it was found to

be effective for the detection of dementia and the

diagnosis of depression (Oyama-Higa 2006). In

section 2, we describe the method used to calculate

the “divergence value” using the nonlinear analysis

of fingertip waves. In section 3, we explain the

meaning of the use of fingertip pulse waves and the

relation between the “divergence value” and

cognitive psychology. In section 4, the relation

between the Lyapunov exponent and mental health

is explained. In section 5, we show the

representation method of constellation graphs

developed for mental health self-checks. Finally, we

outline our future work, and make some discussions

in relating to possible applications.

361

Oyama-Higa M. and Miao T. (2008).

POSSIBILITY OF MENTAL HEALTH SELF-CHECKS USING DIVERGENCE PROPERTIES OF PULSE WAVES.

In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 361-370

DOI: 10.5220/0001060503610370

 SciTePress

2 METHODS OF RECORDING

AND ANALYSIS OF FINGERTIP

PULSE WAVES

2.1 Recording Method of Fingertip

Pulse Wave

Fingertip pulse waves were measured by photo-

plethysmography method. Changes in the amount of

hemoglobin flowing through the capillaries were

measured by infrared photo-electric method (Fig. 1).

The waveform is naturally synchronized with the

beating of the heart. Dynamics changes in

hemoglobin levels caused by the constriction of

capillaries in the fingertip constitute time series data

from a complex system that includes information

from the sympathetic and parasympathetic nerve.

Changes in hemoglobin levels in the capillaries are

thought to be related to the baroreceptor, which are

linked to the sympathetic and parasympathetic nerve

via the brain stem and spinal cord (Figure. 2). Pulse

wave data were collected at a sampling frequency of

200 Hz with a resolution of 12 bits. The

measurement duration was variable, depending on

the experimental conditions. Time series data

consisting of 12,000 points can be obtained in 1 min

of measurement.

Figure 1: Measurement of pulse waves using infrared

irradiation of capillaries.

Figure 2: Diagrammatic representation of the interaction

from brain stem to peripheral blood vessels through the

action of sympathetic and parasympathetic nerves.

This is a convenient measurement method because it

does not require special care with regard to room

temperature, place of measurement, and

measurement conditions. Moreover, because the

measurement time is very short, the collection of

data is not a burden to the subject.

2.2 Chaos Analysis of the Pulse Wave

Fingertip pulse waves were demonstrated to have

chaotic characteristics (Tsuda1992, Sumida 2000,

and Miao 2006). On the basis of chaotic analysis of

time series, we analyzed the recorded data to

determine divergence properties of the pulse waves.

In chaos analysis, the attractor was reconstructing

using time delay method (Tarkens,1981,1985). The

parameters used are delay time of 50 ms and

embedding dimension 4.

Figure 3: Procedure from measurement of pulse waves to

Lyapunov exponent computations.

Beside of the effective information obtainable from

the shape of the four-dimensional attractor, we

calculated the Lyapunov exponent, which is an

index of trajectory instability and a characteristic of

chaos, using Sano and Sawada algorithm (Sano and

Sawada 1985).

As shown in Figure 4, we used the following method

to calculate the Lyapunov exponent. We assumed

that a small sphere (hypersphere) of radius ε is the

initial value for a three-dimensional chaotic dynamic

system. After being mapped once, the sphere was

stretched in the e1 direction and compressed in the

e3 direction, and assumed the shape of an ellipsoid

(Figure. 4). We designated the logarithm of the

expansion rate per unit time along the directions e1,

e2, and e3 as λ1, λ2, and λ3, respectively. Here, λ1,

λ2, and λ3 are the Lyapunov exponents and their set

is the Lyapunov spectrum. Because four embedded

dimensions were set as the optimum number of

dimensions for the pulse wave, w obtained the four

Lyapunov exponents, λ1, λ2, λ3 and λ4, as the

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

362

Lyapunov spectrum. Of these, the largest Lyapunov

exponent, λ1, was used in the calculation to prepare

the constellation graphs.

The following equations show the method of

calculating the Lyapunov exponent. For the time

series data x(i), with i = 1, 2,…, N obtained from the

fingertip pulse waves, the phase space was

reconstructed using the method of time delays.

Assuming that we create a d-dimensional phase

space using a constant time delay τ, the vectors in

the space are generated as d-tuples from the time

series and are given by

)}({)))1((),...,(()( ixdixixi

=−−=

(1)

where

))1(()(

−−= kixix

, with k = 1, 2,..., d.

To reconstruct the phase space correctly, the

parameters of delay (τ) and embedding dimensions

(d) should be chosen optimally (Sano and Sawada,

1985). In time series data recorded from human

finger photoplethysmograms, we chose the

parameters τ = 50 ms and d = 4, as in (Tsuda, 1992)

and (Sumida, 2000).

In the reconstructed phase space, one of the

important measures of complexity is the largest

Lyapunov exponent λ1. If

)(tX

is the evolution of

some initial orbit

)0(X

in the phase space with time,

then

|)(|

limlim

→∞→

(2)

where

)()()( ttt

εε

XXX −=

and

)0()0(

XX −=

for almost all initial difference vectors

)0()0(

XX −=

. We estimated

using the algorithm

of Sano and Sawada (1985), where

describes the

divergence and instability of the orbits in phase

space.

Figure 4: Method used to calculate the Lyapunov

exponent.

The initial 8000 points of pulse wave data were

taken as one window to calculate the largest

Lyapunov exponent, λ1. In the next step, the

window was shifted by 200 points and the exponent

was calculated from the next window of 8000 points.

This procedure was repeated until the pulse wave

data were exhausted. Three minutes of measurement

yielded 36,000 data points; therefore, we can obtain

a (36,000 – 8000)/200 = 140-point time series of

Lyapunov exponents. With 1 min of measurement,

we can achieve a (12,000 – 8000)/200 = 20-point

time series of Lyapunov exponents. The variation in

the largest Lyapunov exponent is a measure of the

variation in the trajectory of the four-dimensional

attractor. The largest Lyapunov exponent is the

divergence of the attractor trajectory and is an

important value related to psychological indices

(Oyama-Higa, 2005, 2006).

3 ESTIMATING

PHYSIOLOGICAL AND

PSYCHOLOGY STATUS

3.1 Outline of Self-checking Method

The subjects were asked to answer some simple

questions to ensure the normal measurement of pulse

waves. This information was used to interpret the

observed divergence in measured values. The

questions were status-checking items regarding

physical conditions and a simple assessment of their

mood at the time of measurement. Answers were

chosen from one of three available levels (Table 1).

In addition to these questions, the subjects were

asked to describe their mood and condition at the

time of the measurement in more definite terms

(Table 1). This enabled the person to identify factors

that can affect divergence values. Because these

records were made in free-form style, key words

alone could be used. However, when a subject is

allowed to write freely, for example, about things

that he or she had communicated to a friend, music

he or she enjoyed, positive results in a sporting

activity, and good or bad news that had been

received, it is easier to study the relationships

between these events and the divergence value.

The types of situations that elicit emotions such

as delight, anger, sorrow, and pleasure differ from

person to person. For example, a condition that

creates a suitable level of divergence, such as

listening to music or having a conversation with

someone, must be recorded as data unique to that

subject. In addition, extreme tension, fatigue, and

low spirits also cause changes in divergence.

Therefore, comparing the status recorded at the time

POSSIBILITY OF MENTAL HEALTH SELF-CHECKS USING DIVERGENCE PROPERTIES OF PULSE WAVES

363

of measurement with the corresponding divergence

values helps a person to assess his or her own mental

status. The responses and simple comments on the

subject’s condition are stored so that they can be

seen by clicking the corresponding divergence value

on the graph. We plan to vary the simple questions

asked according to the category of the subject, e.g.,

child, adult, or aged person.

Table 1: Checking items of subjective evaluation of

subject’s state.

Freestyle reporting: The subject enters a note on his or her

condition at the time of measurement. These notes can be in the

form of a descriptive comment on the subject’s condition,

keywords, and other comments.

Comment example 1: [Had a pleasant chat with a friend about

hobbies.]

Comment example 2: [Feeling low after failing a test.]

3.2 Divergence Analysis for Various

Physiological and Psychological

Status

Biological systems are considered to be complex and

fluctuating, with chaotic characteristics. Although

chaotic systems appear to be extremely complicated

and to behave in a random and unstable manner,

they in fact change according to deterministic rules.

Biological signals emanating from humans or vital

signs come in many types, such as body

temperature, blood pressure, and pulse rate.

Fingertip pulse waves are biological signals that

produce time series data with chaotic characteristics.

Moreover, unlike cardiac waves, fingertip pulse

waves contain various types of information,

including information from the nervous system. In

the field of psychology, several methods have been

suggested as indices for assessing mental health.

However, these methods are generally subjective

and therefore intrinsically lacking in objectivity.

Questionnaires have often been used as relatively

simple psychological tests, and the measurement and

analysis of brain waves can be used to objectively

assess the neurological state at the time of

measurement. However, the measurements are not

simple and the analysis methods are not yet suitable

for analyzing detailed psychological changes.

Another possible method for measuring biological

information is to analyze the R–R intervals of

heartbeats and pulse waves. However, no analysis

has attempted to take into account the chaotic

characteristics of biological information.

The Lyapunov exponent is a property of chaotic

systems that expresses the attractor and represents

the “divergence” of the attractor trajectory. We

focused on the Lyapunov exponent, which has not

previously been evaluated quantitatively as an index

of psychological change in humans, and investigated

its relationships to dementia and communication

skills (an ADL index) in aged persons (Oyama-

Higa,2006), its relationship to error rate at work

(Imanishi,2006), its relationship to diurnal changes

and indices of cumulative fatigue in employees

(Miao,2006),(Oyama-Higa,2006), and time series

fluctuations in divergence in 0- to 5-year-old

children, as well as the effects of parental affection

toward children (Oyama-Higa,2006).

It became clear that suitable functioning and

harmony of the sympathetic nerves, which are

related to adaptability to the external environment

and to society, as well as to flexibility, spontaneity,

and cooperativeness of the mind, are important for

humans to live in a mentally healthy state. These

values were related to the largest Lyapunov

exponent obtained using nonlinear analysis (Oyama-

Higa, 2005, 2006). The largest Lyapunov exponent,

which represents the time series variation in the

attractor trajectory, is defined as the “divergence.”

When this value remains low continuously (i.e.,

when a long period with low divergence persists),

the person has low ability to adapt to the external

world in daily life and is incapable of maintaining a

mentally healthy state. However, a continuously

high level of divergence indicates an extremely tense

or stressful state. A mentally healthy state also

cannot be maintained in this situation. Normally in

humans, a healthy state is indicated by the condition

in which constant variation occurs in the divergence.

Status Good Normal Poor

Sleep

∨

Appetite

∨

Health

∨

Will to work

∨

Mental health

∨

Current mood

∨

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364

Emotions are a part of being human, and these are

believed to cause the variation in divergence.

Physical immunity is critical for the maintenance of

human health, and lowered immunity causes various

diseases. Therefore, to prevent the lowering of

physical immunity and to increase resistance and

prevent diseases, we pay attention to what we eat

and we rest, take medicine when necessary, and

exercise to improve our stamina. However, mental

toughness, as reflected in the ability to communicate

in a positive manner, willingness to perform a given

job, and the ability of mental toughness to withstand

dramatic changes in the external world, are also very

important. We can call these “mental immunity,” but

no methods have been developed to scientifically

investigate this kind of immunity. We analyzed

fingertip pulse waves using nonlinear analysis,

examined their relationships to various

psychological indices, and found that the largest

Lyapunov exponent obtained through chaos

analysis, which corresponds to the “divergence” of

the attractor, was closely related to mental

immunity. This value was also closely linked with

the functioning of the sympathetic nerves of the

autonomic nervous system.

For humans, a mentally healthy condition means

having the ability to cope flexibly with external

changes in “divergence.” This can be considered

mental flexibility or mental immunity, in contrast to

physical immunity. Mental immunity represents

adaptability to the external changes that a person has

to face in his or her everyday life, including a

person’s ability to communicate and express oneself,

and the suitability of psychological flexibility. When

expressing themselves, humans skilfully fend off

various types of changes, contacts, and assaults from

the external environment, and deal with or cope with

them. This is the essence of mental immunity.

Change occurs constantly in day-to-day life.

“Divergence,” which represents a change in the state

of mental immunity, is a critical index. At the same

time, divergence varies depending on the condition

of the person. For example, a long period without

“divergence” suggests that the person is not in a

normal state. In examples of the attractors of a

mentally healthy person and patients with depressive

psychosis, the depressed patients have low

divergence (Figure. 5). In patients with dementia,

the divergence becomes smaller as dementia

advances (Figure. 6).

In a normal state, the level of divergence

fluctuates constantly. During times of extreme

tension and stress, the divergence will be

continuously high. Afterward, however, a mentally

healthy person naturally finds a way to relax, which

brings the divergence back to its normal state. A low

level of divergence would continue when a person is

in a depressed state or when age-related dementia is

present. This suggests that the person is incapable of

bringing the divergence back to its natural level on

his or her own, indicating decreased adaptability to

the external environment.

Healthy person Depressed patient

Figure 5: Attractors of a healthy person and a depressed

patient prepared from 30 s of measurements.

Figure 6: Attractors in elderly subjects with dementia of

(severity = 0) and (severity = 4) severity.

4 CASE STUDIES AND A SYSTEM

FOR MENTAL HEALTH

All measurements were taken after obtaining the

informed consent of subjects.

4.1 Studies of Aged Subjects with

Different Communication Skills

Subjects: Data were obtained from 179 subjects (40

males; 139 females) at three nursing homes for the

aged in Shiga prefecture, Japan.

Date of measurement: August to November 2003.

Measurement method: Fingertip pulse waves were

measured three times for 3 min each. Systolic blood

pressure, diastolic blood pressure, pulse, and body

temperature were measured with the patient in a

relaxed state at 25ºC (room temperature) prior to the

measurement of pulse waves.

Indices: Five grades indicating the severity of

dementia judged by a doctor. We obtained data for

the ADL index of communication skills (three-

POSSIBILITY OF MENTAL HEALTH SELF-CHECKS USING DIVERGENCE PROPERTIES OF PULSE WAVES

365

graded evaluation), composed of seven items and

estimated by a care manager. We examined the

relation between the data and the maximum

Lyapunov exponent calculated from the fingertip

pulse waves.

Results: There was a significant relation between

the maximum Lyapunov exponent and

communication skills (Figure 7 A) and severity of

dementia (Figure 7 B). t-student test was used.

Figure 7: Relation of the Lyapunov exponent and (A)

communication skills and (B) severity of dementia in

elderly patients.

In constellation graphs, the right side indicates small

Lyapunov exponents and the left side indicates large

Lyapunov exponents (Figures. 7, 8). Because of the

large quantity of data, five cases that were similar to

the median of data for each rank in index (i.e.,

dementia, 0–4; communication skills, a–c) are

shown.

Fifteen subjects with high cognition were selected

and measurements were retaken after 9 months, in

August 2004 (Figure. 10). Values of the Lyapunov

exponent increased in some subjects and decreased

in others compared to the first measurements taken

in November 2003. These results indicate that

changes in the Lyapunov exponent always occur.

However, attention is needed to understand the

causes of very low values.

Figure 8: Relation between severity of dementia (0–4) and

the Lyapunov exponent. One line indicates one subject.

Figure 9: Relation between communication skills (a–c)

and the Lyapunov exponent. One line indicates one

subject.

Figure 10: Results of the re-measurement of the Lyapunov

exponent after 9 months (15 subjects). Subject e7 had died

prior to the second measurement.

4.2 Case Studies of Maternal

Attachment of Children

Subjects: Data were obtained from 242 children 0-

to 5 years old from nurseries in Osaka and Himeji.

Date of measurement: January 2004–March 2005.

Measurement method: Fingertip pulse waves were

measured twice for 1 min each.

Pulse waves were measured in a relaxed

environment at 25ºC (room temperature). Within the

age range of children tested, 3-year-olds had lower

mean values in the largest Lyapunov exponent than

ones of the other ages. There was a significant

relation between mean values in the largest

Lyapunov exponent and children ages (p < 0.05

using t-student test). Divergence was highest in 0-

year-olds, followed by 1-year-olds and 2-year-olds,

and was lowest in 3-year-olds (Fig. 11). For 3-year-

old children, some widely held beliefs concerning

their states and attachment seemed to correspond

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scientifically to the divergence of the attractor

trajectory in pulse waves.

Table 2: Relation between the age and number of children.

Males Females Total

0-year old 2 5 7

1-year old 13 10 23

2-year old 19 13 32

3-year old 27 27 54

4-year old 44 25 69

5-year old 34 23 57

Total 139 103 242

3.5

4.5

Mean Lyapunov exponent

0 1 2 3 4 5

Age

Figure 11: Relation between the Lyapunov exponent and

the age of children (242 subjects).

Figure 12: Relation between the Lyapunov exponent and

maternal attachment to the child.

Additionally, questionnaires were completed by the

mothers to study maternal attachment to the children

(Index: Maternal Attachment Inventory MAI

(Muller, 1994). After measurements were taken, the

children were divided into two groups: a group with

high maternal and a group with low maternal

attachment. There was a significant relation between

attachment and the Lyapunov exponent (p < 0.05

using t-student test; Fig. 12). These results indicate

that problems of maternal attachment are also related

to divergence in children, and could therefore be of

help to mothers in child rearing.

4.3 Studies of Employees and the

Tiredness Index

The Lyapunov exponents of 12 employees of a

specific company were measured three times during

the day: in the morning, immediately after arriving

at the office; in the afternoon, 1 h after lunch; and in

the evening, before leaving the office for the day. At

the same time, the subjects were questioned to

determine their tiredness index. We then examined

the relation between the Lyapunov exponent and the

tiredness index. Changes in the Lyapunov exponent

with the time of day differed among the employees

(Figure. 13). Because the management of mental

health in business has caused many problems,

including occurrences of depression, the Lyapunov

exponent is a useful index not only for employees’

self-management, but also for employers.

Figure 13: Changes in the Lyapunov exponents of

employees of a specific company in the morning,

afternoon, and evening.

The relation of the Lyapunov exponent to the

tiredness index indicated that subjects with a low

Lyapunov exponent in the afternoon tended to have

depressive tendencies and strong anxiety (Table 3).

4.4 Experiments of Arithmetic

Operations

Kraepelin tests that is addition work of numerical

value were conducted twice for 15 min each on

subjects in their 20s and 40s, and changes in the

Lyapunov exponent were studied before and after

the tests. The Lyapunov exponent increased in all

subjects after the Kraepelin test. The subjects gave

the impression that they felt better after the

Kraepelin test than they did before the test (Fig. 14).

POSSIBILITY OF MENTAL HEALTH SELF-CHECKS USING DIVERGENCE PROPERTIES OF PULSE WAVES

367

Table 3: Relation between the Lyapunov exponent in the

afternoon and the tiredness index of employees. An

inverse correlation greater than –0.7 means that a low

Lyapunov exponent indicates a depressive tendency and a

strong tendency toward anxiety.

Figure 14: Changes in the Lyapunov exponent before and

after the Kraepelin test.

4.5 Studies of Operation Error in

Monitoring and Judgment Work

An apparatus used to simulate monitoring on a

personal computer was developed to examine the

relation between the Lyapunov exponent and the

human error rate. The experiment was conducted by

increasing the number of monitoring images from

three to six, and then to nine images. In all cases, the

error rate was high when the Lyapunov exponent

was low (Figure. 15).

12345678910111213

Blocks

Errors by Block (

)

2.8

3.2

3.4

3.6

3.8

Ly apunov Exponent s by Bl oc

Errors b

Block L

unov Ex

onent s b

Block

Figure 15: Relation between the Lyapunov exponent and

human error rate in monitoring work over 30 min.

Symbols and line indicate the Lyapunov exponent by

block (3min); bars indicate the human error rates by block.

4.6 Studies of Painting Work

We measured the Lyapunov exponent when a certain

artist did nothing and again 3 min after he began

painting. The Lyapunov exponent increased while

the artist painted (Figure. 16).

Figure 16: Changes in the Lyapunov exponent while

painting. Orange, before painting; blue, during painting.

4.7 Studies of giving Birth Processes

The Lyapunov exponent was measured in seven

pregnant women before and after giving birth

(obstetrics and gynaecology in Nara city; Figure 17).

Comparisons were made between the values

measured within 1.5 h before birth and after birth.

The Lyapunov exponent was significantly higher

before birth than after birth (Student t-test, p < 0.05).

Giving birth increased the functioning of the

sympathetic nervous system.

Lyapunov

After Before

‚a‚`

ƒyƒA‚²‚Æ

Student‚ÌtŒŸ’è

0.05

Figure 17: Comparison of the Lyapunov exponent

measured in pregnant women within 1.5 h before and 1.5 h

after giving birth.

5 SELF-CHECK SYSTEM

5.1 Equipment Components

A device that is easy to use and gives minimum

burden on the subject is needed to measure the pulse

t-student test

significance 0.05

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

368

waves. It is not possible to check the mental health

of a person through just one round of measurements.

For these reasons, the device must be convenient to

use. We took note of the fact many people often do

their work with PCs, and therefore developed a

device that can make these measurements using a

mouse. The pulse wave sensor is installed on one

side of the pulse wave mouse; measurements can be

made by simply touching the sensor with a finger.

The mouse is connected to the PC through a USB

port and can also be used as an ordinary mouse

(patent pending).

Software installed on the PC starts and ends the

measurements, and sets their duration.

5.2 Representation System using

Constellation Graphs in Mental

Health Self-Checks

Previous studies indicated the possibility of using

the Lyapunov exponent as a new psychological

index. However, as noted above, it is dangerous to

judge mental health using only one measurement.

Therefore, even over the period of a single minute,

several measurements are necessary to assess daily

fluctuations. It is also necessary to prepare a self-

feedback system to determine when changes in the

values of the Lyapunov exponent can be observed.

Figure 17: Example of a time series constellation graph for

a self-diagnosis system. The right area of the graph

indicates large Lyapunov exponents; the left area indicates

small Lyapunov exponents. Changes among seven

measurements are shown; circles indicate the standard

deviation of the Lyapunov exponent for each

measurement. If the point constitutes a change, the self-

stated status at that point is shown.

To do this, a time series of the Lyapunov exponent

must be recorded over several days and weeks to

monitor natural variation, and the status of the pulse

wave data should be recorded using simple words or

keywords. To accomplish these measurements, the

development of apparatus capable of taking

measurements easily and of software that can

indicate changes in mental health is necessary (Fig.

17).

5.3 Future Plan and Some Problems

There are three potential types of self-diagnosis

system that use the divergence of fingertip pulse

wave attractor. The first type is the personal

computer (PC)-completed type, in which all

operations from measurement to display are

performed on one PC. In the second type, pulse

wave data stored on a PC are transferred to a server

via the Internet to construct a database. The software

used to analyze the pulse waves on the server is

either downloaded or pulse wave data are sent

through the server. In the third type, a sensor for

taking pulse wave measurements is installed on a

cellular phone and the display of the results is

provided as an image on the cellular phone. In this

case, the Internet is also used. In the second and

third types, results are accumulated in a database via

the Internet, and a system is constructed for an

available search. We expected that data will be

accumulated through Internet use, enabling further

advanced study. However, sufficient caution should

be taken to protect the security of personal

information.

If the self-management of mental health and

early detection and treatment of diseases can be

accomplished using this system, many people might

be saved from terrible situations resulting from

mental problems or instability. In addition, sending

data regarding the mental indexes of humans using a

network may lead to innovations in communication.

However, sufficient care should be taken in the data

management because of recent problems concerning

the protection of personal information. However, in

terms of the effectiveness in promoting further

research, the accumulation of information would be

extremely helpful for various future studies.

6 CONCLUSIONS

Mental management in humans is increasingly

important as society continues to change. Accurate

measurements have been difficult to obtain in the

past using both subjective and objective methods,

and the time and cost required to take brain

measurements have imposed heavy burdens on

patients. Measurement of pulse waves is simple and

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369

has the merit of imposing a comparatively minor

burden on the subjects.

The complete realm of information obtainable

from pulse waves has not yet been fully elucidated,

but we have found that the information is deeply

related to the behavior of the autonomous system

networked throughout the body via the spinal cord

from the part of the brain stem responsible for much

of human activity and responses. The divergence of

pulse waves is thought to be the value most related

to the function of the nervous system, including the

sympathetic and parasympathetic nerves that are

integrated with the brain stem. In the future, we plan

to further document these relations through various

experiments.

ACKNOWLEDGEMENTS

We thank all of the people who cooperated with us

in taking measurements.

REFERENCES

http://www9.plala.or.jp/rescue/social.html,

www.chihoucare.org/, http://dementia.prit.go.jp/

Oyama-Higa M., Miao T., and Mizuno-Matsumoto Y.,

2006. Analysis of dementia in aged subjects through

chaos analysis of fingertip pulse waves. 2006 IEEE

Conference on Systems, Man, and Cybernetics,

Taipei, Taiwan, 2863–2867.

Tsuda I., Tahara T., and Iwanaga I., 1992. Chaotic

pulsation in capillary vessels and its dependence on

mental and physical conditions. Int. J. Bifurcation and

Chaos 2: 313–324.

Sumida T., Arimitu Y., Tahara T., and Iwanaga H., 2000.

Mental conditions reflected by the chaos of pulsation

in capillary vessels. Int. J. Bifurcation and Chaos 10:

2245–2255.

Sano M., and Sawada Y., 1985. Measurement of the

Lyapunov spectrum from a chaotic time series. Phys.

Rev. Lett. 55: 1082.

Miao T., Shimoyama O., and Oyama-Higa M., 2006.

Modelling plethysmogram dynamics based on

baroreflex under higher cerebral influences. 2006

IEEE Conference on Systems, Man, and Cybernetics,

Taipei, Taiwan, 2868–2873.

Oyama-Higa M., and Miao T., 2005. Representation of a

physio-psychological index through constellation

graphs. ICNC’05–FSKD’05, http://dx.doi.org/

10.1007/11539087_109.

Oyama-Higa M., and Miao T., 2006. Discovery and

application of new index for cognitive psychology.

2006 IEEE Conference on Systems, Man, and

Cybernetics, Taipei, Taiwan, 2040–2044.

Imanishi A., and Oyama-Higa M., 2006. The relation

between observers’ psychophysiological conditions

and human errors during monitoring task. 2006 IEEE

Conference on Systems, Man, and Cybernetics,

Taipei, Taiwan, 2035–2039.

Oyama-Higa M., Tsujino J., and Tanabiki M., 2006. Does

a mother’s attachment to her child affect biological

information provided by the child? Chaos analysis of

fingertip pulse waves of children. 2006 IEEE

Conference on Systems, Man, and Cybernetics,

Taipei, Taiwan, 2030–2034.

Takens F., 1985. In: Braaksma B. L. J., Broer H. W., and

Takens F., eds. Dynamical Systems and Bifurcations,

Lecture Notes in Math. Vol. 1125. Springer,

Heidelberg.

Takens F., 1981. Detecting Strange Attractors in

Turbulence, Lecture Notes in Math. Vol. 898.

Springer, New York.

Muller, M.E., 1994. Questionnaire to Measure Mother-to

Attachment. Journal of Nursing Measurement, 2(2),

129-141.

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