Mental Health Self-check System using “Lyspect”

Mayumi Oyama-Higa

, Tiejun Miao

, Shigeo Kaizu

and Junji Kojima

Department of Systems Innovation, Osaka University, Toyonaka, Osaka 560-8531, Japan

Chaos Technology Research Laboratory, Ōtsu, Shiga 520-2134, Japan

Rakuwakai Otowa Hospital, Yamashina-ku, Kyoto 607-8062, Japan

Keywords: Plethysmograms, Largest Lyapunov Exponent, Nonlinear Analysis, HFLF Balance, Blood-vessel Balance.

Abstract: The largest Lyapunov exponent (LLE) obtained through nonlinear analysis of plethysmograms contains

information of the cerebral central system, which has been shown by mathematical model and experiments.

Especially mental experiments have shown the significant relationship between LLE and mental status of an

individual. The wave data are obtained by measuring changes in the blood flow of the capillary vessels of a

fingertip, with an infrared sensor. Then the analogue format of the data is changed into digital format for

calculation. Using “Lyspect” software, we next compute parameters including LLE, HF/LF, autonomic-

nerve balance, and blood vessel age. These parameters, as functions of time, can be displayed in graphs.

Notably, while the pulse wave is being measured, changes of LLE and HF/LF can be displayed in real time.

Furthermore, the setup of measuring time and various parameters for calculation is available. The state at

the time of the measurement can be studied by visualization. Additionally, the unsuitable wave data arising

from the accuracy of the sensor and the external noise can be eliminated by the filter of Lyspect. Currently,

a version of Lyspect that is installable on user-friendly smartphones is being developed, which will make

even easier timely self-check of mental status.

1 INTRODUCTION

In chaotic datasets, attractor plots and “divergence”

of attractor trajectories are characterized by

Lyapunov exponents. Previously, we focused on the

Lyapunov exponents of pulse waves in studying

individuals in various situations (Imanishi and

Oyama-Higa, 2006; Miao, Shimoyama and Oyama-

Higa, 2006; Oyama-Higa and Miao, 2006; Oyama-

Higa, Tsujino and Tanabiki, 2006). Our results

showed that to maintain mental health, it is

important for an individual to keep the harmony in

the functioning of the sympathetic nervous system,

which is associated the individual’s qualities such as

flexibility, spontaneity, cooperativeness, and the

ability to interact with the external environment and

society.

We also learned that the values representative of

such harmony are associated with the largest

Lyapunov exponent (LLE) obtained from nonlinear

analysis (Oyama-Higa and Miao, 2005 and 2006).

Essentially, in this research, LLE, which signifies

temporal fluctuations in the attractor trajectory, is

defined as “divergence”. When this value is

continuously low, namely, when there is no

divergence for a long period, adaptability to external

factors in daily life decreases, and thus mental health

cannot be maintained. Conversely, a value that is

continuously high for a long period represents a

continued state of extreme anxiety or stress, and also

mental health cannot be maintained in this case.

For humans, a healthy state can be defined as one

in which high and low divergences constantly

alternate. Normal human life includes a wide range

of emotions, which is probably the cause of such

changes in divergence. Using nonlinear analysis of

pulse waves, an individual’s mental health can be

measured in approximately 1 minute, with the help

of a low-cost pulse wave sensor. This offers the

potential for easy assessment of mental health that

can be performed regularly at home or workplace.

We have created a trial version of the self-check

system.

Mental health changes from day to day and hour

to hour, so it is most important to monitor these

fluctuations closely and intervene quickly when

problematic symptoms emerge. To this end, we

developed a self-check system producing a graph in

which the degree of mental health over time is

visualized as a constellation (Oyama-Higa et al.,

2007).

“Lyspect” software can perform three types of

Miao T., Kojima J., Oyama-Higa M. and Kaizu S. (2012).

Mental Health Self-check System using “Lyspect”.

In Proceedings of the Sixth International Symposium on e-Health Services and Technologies and the Third International Conference on Green IT

Solutions, pages 9-18

DOI: 10.5220/0004474600090018

 SciTePress

analysis (chaos analysis, blood-vessel balance

analysis and autonomic-nerve balance analysis)

using finger plethysmograms as input data. Besides

the version that produces a detailed result for

researchers, we also developed a simplified version

of “Lyspect”, called “Lyspecting”, for general users.

2 MAIN FEATURES OF LYSPECT

Lyspect possesses the following functions.

2.1 Analysis

2.1.1 Chaos Analysis

To calculate LLE of pulse waves. Since Lyspect

calculates the spectrum of Lyapunov exponents,

values of Lyapunov exponents of all embedded

dimensions are also found.

2.1.2 Blood-vessel Balance Analysis

To calculate blood-vessel balance equivalent to the

estimated vascular age from a second differential

wave pattern using Lyspect's own method.

2.1.3 Autonomic-nerve Balance Analysis

To calculate the LF and HF ratio from spectral

analysis of variability in the HR cycle.

2.1.4 Others

It can also calculate the balance of sympathetic

nerves and parasympathetic nerves using Lyspect's

own method for estimating HF from heart rate

transitions.

The analysis range and slide width for all

analyses can be specified and adjusted. When the

analysis range is shorter than the wave data length

and the slide width is not 0, multiple results are

generated by sliding along the analysis range for the

amount of the slide width.

2.1.5 Pulse Wave Filtering (Optional)

To analyze pulse waves with a band-pass filter that

removes bass-line transitions of wave data. It is

available only when the filter option is installed.

2.2 Wave Input

Other than normal text files, Lyspect reads CCI's

BACS-Advance

binary wave data files as input

wave data for analysis.

Another way is to dynamically read wave data

from a connected cuff online.

Besides, it can also import wave data records

from the DB of MSAS software, a self-check system

using the database of the pulse waves which we have

developed. In the case the DB contains records of

analysis results, they are also imported and recorded

as clips.

2.3 Analysis Result

There are two types of data to be saved.

1. Save analysis conditions and results to a file in

CSV format, with each analysis on a single row.

2. Save sequences of analysis results in multiple

rows to a file in CSV format when the slide width of

the analysis is not 0 and multiple analysis results are

generated.

2.4 Display and Save Pulse Waves

We introduce the following operations in Lyspect.

1. Save source data of displayed pulse waves

to an external file in text format. This feature can be

used, for example, to load and save BACS-Advance

binary wave data.

2. Display a 3D Takens plot in chaos analysis.

Assess the graph using zoom/pan.

Display Takens plot of wave data applied with a

band-pass filter as source data. (Optional. Available

only when the filter option is installed.)

3. Display second differential wave form of

pulse waves (and markers indicating detected “a”,

“b” and “c” peaks). This wave data can be saved to

an external file in text format.

4. Display a graph of heart-rate transition.

This graph data can be saved to an external file in

text format.

5. Display pulse waveform applied with a

band-pass filter that removes transitions of original

pulse wave data. (Optional. Available only when the

filter option is installed.) This waveform data can be

saved to an external file in text format.

6. Display waveform showing the bass-line

transition. (Available only when the filter option is

installed.)

This waveform is created simply by subtracting

the pulse waveform that has been applied with a

band-pass filter from the source pulse waveform.

The waveform data can be saved to an external

file in text format.

EHST/ICGREEN 2012

3 HOW TO USE LYSPECT

Details on the usage of Lyspect are explained here in

several steps.

3.1 Layout of the Operation Windows

The user can freely switch the layout of operation

windows. Listed below are the main operation

windows of Lyspect.

3.1.1 “Parameters”

In this window, analysis parameters are set.

Figure 1: Parameters.

3.1.2 “Wave Form”

In this window, the waveform of pulse waves is

displayed.

Figure 2: Wave form.

3.1.3 “Resultgraph”

In this window, the analysis results are displayed

using graphs.

Figure 3: ResultGraph.

3.1.4 “ClipListTable”

In this window, a log of the analysis results is

displayed in a table format.

Figure 4: ClipListTable.

3.1.5 “LogArea”

This is a message display area for text information.

Figure 5: LogArea.

The above windows appear as sub-windows

within the Lyspect main-window.

The layout is not fixed and can be freely moved

by the user.

3.2 Loading Wave Data

There are mainly two ways to load wave data for

analysis:

1. Text files from a wave data file and

BACS-Advance pulse wave binary files.

2. Loading wave data from a connected cuff.

This operation loads data by sampling the wave data

from a cuff connected to a USB port.

3.3 Setting Analysis Conditions and

Analyzing

Analysis conditions are set in the “Parameters”

window. See Figure 1.

Data length shows the number of sampling

points of the loaded wave data.

In “Sampling sec.”, the sampling rate is set. For

example, we fill in 0.005 for 200Hz and 0.001 for

1,000Hz sampling rate.

There are three tabs that correspond to each type

of analysis.

Figure 6: Three types of analysis.

Mental Health Self-check System using “Lyspect”

“Apply filter” is effective only when the band-

pass filter is enabled. The feature is used to perform

analysis of filtered data.

3.4 Graphical Display of Analysis

Result

Analysis conditions are set in the “Parameters”

window.

Figure 7: Summary.

There are a number of tabs on the top of the

“ResultGraph” window.

3.4.1 “Summary”

This tab shows the main analysis result values of the

three types of analyses in constellation graphs.

1. Chaos:

The average value of LLE in sliding analyses is

shown as the angle of the meter and the standard

deviation is shown as a circle where the radius is the

standard deviation value.

2. Estimated vascular age:

The meter is in a range of 0 to 10 from which

one obtain the estimated vascular age by multiplying

the value with 10. For example, the value 3.2 means

the age is 32.

3. Autonomic-nerve balance:

Autonomic-nerve balance is defined as the value

of LF / ( LF + HF ). A value larger than 5 indicates

the sympathetic nerve dominance, namely, a state of

stress, while a value smaller than 5 shows the

parasympathetic nerve dominance, that is, a state of

relaxation.

3.4.2 “Chaos”

This is a graph showing the changes of LLE

resulting from sliding analyses where the number of

slides is in the horizontal axis.

Figure 8: Chaos.

In Lyapunov analysis, the spectrum is found for

the number of Lyapunov dimensions. But in the

initial display, only the first Lyapunov exponent,

namely the largest Lyapunov exponent (LLE), of the

spectrum is displayed on the graph. Click the

checkboxes of the legend on the right of the graph,

and the second, third and fourth Lyapunov

exponents will be displayed. The vertical axis of the

graph is scaled automatically.

One is able to right-click the graph to save the

graph data to an external file in CSV format.

If the chaos analysis is performed with Full range

analysis or Slide pitch is 0, only one set of the

Lyapunov spectrum will be calculated. In this case,

the graph displayed in the “Chaos” tab will be a

graph of the convergent calculation of the Lyapunov

exponents as shown below.

Figure 9: Display of four Lyapunov exponents.

3.4.3 “HFLF Balance”

A graph is displayed when one performs slide

analysis by specifying the slide pitch rather than

full-range analysis and setting the calculation

method to HF-LF method in Autonomic-nerve

balance analysis.

Figure 10: HFLF balance.

The graph shows changes of LF and HF values

for the entire analysis area with the number of slides

EHST/ICGREEN 2012

in the horizontal axis.

In the case of slide analysis, the value of

“b10HR” is calculated for the last analysis range

(near the end of data).

Click the checkboxes of the legend on the right

of the graph to enable/disable the display of HF, LF

and “b10HR”.

Right-click the graph to save the graph data to an

external file in CSV format.

3.4.4 “HFLF Power Spectrum”

This spectrum distribution graph is displayed when

the calculation method is set to HF-LF method in

Autonomic-nerve balance analysis.

Figure 11: HFLF power spectrum.

The graph shows changes of LF and HF values

for the entire analysis area with the number of slides

in the horizontal axis.

In the case of slide analysis, the value of

“b10HR” is calculated for the last analysis range

(near the end of data).

Click the checkboxes of the legend on the right

of the graph to enable/disable the display of HF, LF

and “b10HR”.

Right-click the graph to save the graph data to an

external file in CSV format.

3.4.5 “Blood-vessel Balance”

This analysis evaluates suppleness of blood-vessel.

One index value of suppleness is calculated from

every time of pulsation, and shown in the graph.

Figure 12: Blood vessel balance.

3.5 Numerical Display of Analysis

Result

3.5.1 Clip and Clip List

The following is the “ClipListTable” window.

Figure 13: ClipListTable for analysis result.

A clip is a record of a single analysis:

Clip: = analysis data + analysis condition

+ analysis result (+ comment)

(1)

A clip list is a set of clips. Normally, the list contains

multiple clips.

Clip List: = clip + clip + ... + clip (2)

The clip list is displayed in the “ClipListTable”

window in table format. In detail, a clip contains:

Wave data: = wave filename (3)

Analysis conditions: = parameters set in the

“Parameters” window (4)

Analysis result: = analysis result for each of the

three types of analyses including series of values

such as chaos spectrum, HFLF power spectrum, etc.

(5)

Comment: = any character string user entered.

This item is completely optional, and used to enter

information such as data profile or others. (6)

3.5.2 Setting the Items to Display in the

“ClipListTable” Window

Since a clip contains enormous amount of numerical

data, it is not advisable to display all numerical data

in the “ClipListTable” screen.

The user may select the items to display in the

window. To do so, select menu “Clip” and then

“Clip table display columns” for details about the

item names of analysis results.

3.5.3 Entering Comment

To enter comment, first select “Comments” as an

item to display in the “ClipListTable” window. Next,

click the “Comments” column of the row to enter

Mental Health Self-check System using “Lyspect”

comment in the “ClipListTable” window. When the

“EditText” window appears, enter or edit comment.

3.5.4 Displaying Graphics of a Clip

Clicking a clip in the “ClipListTable” window will

display the following:

1. The wave data of the clip in the “Waveform”

window,

2. The analysis conditions of the clip in the

“Parameters” window and

3. The graph of the clip's analysis result in the

“ResultGraph” window.

3.5.5 Saving Clip List to a File

In Lyspect, saving analysis results is performed by

saving Clip list to a file.

Clip list may be saved by selecting “Clip” and

then “Save file” in the menu. In order to prevent

users from accidentally forgetting to save to a file,

however, Lyspect will display a message asking

“The clips have not been saved. Save?” when the

user terminates it without saving Clip list.

3.5.6 Loading a Previous Analysis Result

(Loading a Clip File)

A clip file can be loaded by selecting “Clip” and

then “Read file” in the menu. The number of loaded

clips will be displayed and Clip list will appear in

the “ClipListTable” window.

The last saved clip file can be loaded by

selecting menu “Clip” and then “Read the last file”.

Lyspect saves the names of the last saved clip files

and thus can load them quickly without finding their

paths again.

3.5.7 Exporting Analysis Results in CSV

Format

Although clip files are text files, the format is unique

to Lyspect.

In order to use the analysis results in other

assessment applications such as MS EXCEL, clip

information can be converted and saved in CSV

format with commas separations.

Each clip will be output in a single row.

Since a clip contains enormous amount of data,

information (columns) to output to a CSV file can be

specified in the same way as specifying columns to

display in the “ClipListTable” window.

To save clip files in CSV format, select “Clip”

and then “Export in CSV format” in the menu.

3.5.8 Deleting Clips from Clip List

To delete a clip from Clip list, click the clip to delete

in the “ClipListTable” window, right-click to display

the pop-up menu and then select “Delete”. To delete

all clips in Clip list, select “Clip” and then “Clear

table” in the menu.

3.6 Displaying the Pulse Waveform

The “Wave form” window displays the wave data

loaded from a file or a cuff. See Figure 2.

The wave data displayed in the “Wave form”

window is the pulse wave subject to analysis.

The vertical axis of the wave graph is scaled so

that the maximum value and minimum value of the

entire wave data extend to the upper and lower edges

of the graph.

3.6.1 Zooming the Waveform

Drag the mouse in upward direction on the graph to

zoom in the horizontal axis of the waveform. In the

same way, drag the mouse in downward direction to

zoom out.

To reset the scale, right-click on the mouse and

then select the “Reset zoom” in the pop-up menu.

Figure 14: Zooming.

3.6.2 Displaying Secondary Pulse Waves

Waveforms generated by processing the source

pulse waveform in various ways are called

secondary pulse waves. These waves can be

displayed by selecting menu “Display” and

“Secondary waveform”.

To save secondary pulse wave data to an

external file in text format, right-click on each graph

and then select “Save wave file” in the pop-up menu.

3.6.3 Second Differential Waves

Second differential waves are waveforms that

differentiate the source pulse waves twice and are

used in analyses of both blood-vessel balance and

autonomic-nerve balance since they effectively

express the characteristics of pulse wave shapes.

EHST/ICGREEN 2012

Figure 15: Second differential waves.

“a”, “b” and “c” peaks used for assessing the

blood-vessel balance are drawn in white, red and

blue lines respectively for each pulse wave.

3.6.4 Heart Rate Transitions

The “a” peak interval of the second differential

waves refers to the pulse-cycle period and the heart-

rate transitions graph shows the momentary heart

rate found by inverse calculation of the pulse-cycle

period.

Figure 16: Heart rate transitions.

3.6.5 Band-pass Filter

This is available only when the filter option is

installed.

3.6.6 Base Line Transitions

This is available only when the filter option is

installed. The graph shows a waveform created by

performing a simple subtraction of original source

waveform - filtered waveform.

It is useful to see the trend of bass-line transitions.

Figure 17: Base line transitions.

3.7 Chaos Analysis Takens Plot

The Takens plot of pulse waves can be displayed by

selecting “Display” and then “Takens display” in the

menu.

Figure 18: The Takens plot.

Although chaos analysis of pulse waves is

performed in the embedded dimensions, the plot

displayed here is a three dimensional plot.

The axis in depth direction is displayed using

line colours. The red lines indicate at the front and

blue lines indicate to the back. (All lines are

displayed in green during in the initial state.)

Besides the source waveform, the filtered

waveform can also be displayed when the filter

option is installed.

As to the attractor displayed by the method of

Takens, the following operations are possible:

1. Rotation of coordinates can be performed.

2. Zoom of coordinates is available.

3. Whether to display the sample point or not is

optional.

4. The starting point of drawing and the number

of the points of drawing can be set up.

5. Delay time and evolution time can be set up.

3.8 Names and Contents of Analysis

Result Items

3.8.1 Items Names and Contents of

Blood-vessel Balance Analysis Result

Figure 19: Output parameters of blood-vessel balance

analysis.

1. “b/a”, “c/a”, “d/a”, and “e/a”

Mental Health Self-check System using “Lyspect”

Average values for peak “a”, “b”, “c”, “d” and

“e” ratios for the entire analysis range. These are the

bases for calculating the blood-vessel balance.

2. “Pb”

Estimation of the vascular age (method 1).

3. “age”

Estimation of the vascular age (method 2).

4. “sigma”

It denotes the vascular aging index σ.

5. “Age25%”, “ Age50% and “Age75%”

In contrast with "age" value, AgeXX% values

are based on the distribultion graph of "age" values

calculated from b/a, c/a, d/a and e/a of each pulse

through the analysis area of pulse wave. Age25% is

the value of "age" of one forth counting from

smallest "age" value in the distribution graph,

Age50% is the "age" value of the half counting in

the graph, and Age75% is the "age" value of the

three forth counting in the graph.

3.8.2 Item Names and Contents of

Autonomic-nerve Balance Analysis

Result

Figure 20: Output parameters of autonomic-nerve balance

analysis.

1. “hf” and “lf”

They express each spectrum cumulative value, as

the base of LF/HF spectral analysis. The balance of

autonomic nerves is assessed by the ratio of HF and

LF. LF is in the range of 0.04Hz – 0.15Hz, while HF

is in the range of 0.15Hz – 0.40Hz.

2. “HFa”

It denotes the estimated HF found from the

average heart rate. The formula is

HFa = K1×HR×3 + K2×HR×2 + K3×HR + K4,

(7)

≦

HFa

≦

100, (8)

where K1 to K4 are coefficients for approximation

and HR is the average heart rate. These coefficients

can be changed by selecting “System” and then

“Configuration” in the menu.

A value taken between 40 and 60 indicates a

normal balance. HFa

≦

40 and 60

≦

HFa indicate

sympathetic nerve dominance and parasympathetic

nerve dominance, respectively.

3. “HR Avr”

It means the average heart rate within the

analysis range

4. “Ln(LF)/Ln(HF)”

This parameter signifies the autonomic-nerve

balance assessment value found from the log ratios

of LF and HF. The formulas are:

B = Ln(LF) / Ln(HF), (9)

B10 = B × 10 / 3.5, (10)

≦

B10

≦

10. (11)

B10 < 5 indicates parasympathetic nerve dominance,

while B10>5 indicates sympathetic nerve dominance.

5. “b10”

b10 shows the autonomic-nerve balance

assessment value found from the ratios of LF and

HF. The formulas are:

hflf = HF / ( HF + LF ), (12)

b10 = ( 1 – hflf ) × 10 = LF / ( HF + LF ) × 10,

(13)

≦

b10

≦

10. (14)

b10 < 5 indicates parasympathetic nerve dominance,

while b10>5 indicates sympathetic nerve dominance.

6. “b10HR”

It means the autonomic-nerve balance

assessment value found from the HR value. The

formulas are:

HFa = the HF value ( ×100 ) estimated from HR,

(15)

b10HR = ( 1 – Hfa / 100 ) × 10, (16)

≦

b10HR

≦

10. (17)

b10HR < 5 indicates parasympathetic nerve

dominance, while b10HR > 5 indicates sympathetic

nerve dominance.

4 EXAMPLES OF APPLICATION

We give three examples involving the calculation

using Lyspect.

4.1 Example of a Healthy Subject

Below is a mentally healthy person's data. The

subject is measured for three times, and for each

time the duration of measurement is three minutes.

EHST/ICGREEN 2012

The blood vessel age of this subject is about 40

years.

LLE and autonomic-nerve balance are also in

good condition.

Figure 21: ResultGraph of a middle-age healthy subject.

4.2 Example of an Elderly Subject

The subject is around 60 years old. From the three

times of measurement, we found the LLE is low, but

the autonomic-nerve balance is normal.

Figure 22: ResultGraph of an elderly healthy subject.

4.3 Example of a Depression Sufferer

In the following we present the result of a 49-year-

old dysthymia-related obstacle (depression) sufferer.

To increase the accuracy, we performed the

measurement for 18 minutes in total. The result

shows that LLE is low and the value of autonomic-

nerve balance is not less than 5 for each time. Thus

the sympathetic nerve is higher in than

parasympathetic nerve. This result agrees with the

general tendency that the LLE of a mental disease

sufferer is low and his or her autonomic-nerve

balance is higher in than 5.

Figure 23: ResultGraph of a mentally ill subject.

5 DISCUSSION

With the help of Lyspect, individuals can perform

self-checks and self-management for mental health.

Mental health can be possibly maintained, if it is

possible to ascertain exactly what kind of day-to-day

conditions bring about high and low divergence. We

are confident that even for those potential mental

disease sufferers who regularly go to consult a

counsellor or a psychiatrist, this system can aid in

the early detection of depression or dementia, and

thus prevent further deterioration of mental health.

Furthermore, we think it highly original to send

and receive data related to mental health indicators

across networks. However, it is essential to take

great care in data management in light of

confidentiality, which has become more important in

recent years.

With the purpose of enabling users to perform

the measurement at any time and anywhere as well

as to compare their past health conditions on the

Internet, we developed the software which can be

used on the smart phone of Android loading . This

software can also display the state of a pulse wave,

LLE, autonomic-nerve balance, and blood vessel age.

A measurement result can be saved via individual

PC or the Internet with security management.

Mental Health Self-check System using “Lyspect”

What is more, whenever necessary, the past

measurement results can be retrieved from the

database and investigated, and it therefore becomes

possible to learn the change in one's mental state.

We expect that a small and cheap pulse wave

sensor connectable with a mobile phone or a small

PC will be developed early.

REFERENCES

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,

2035–2039.

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,

2868-2873.

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

Physiopsychological Index Through Constellation

Graphs. Lecture Notes in Computer Science, 3610,

811-817.

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

Application of New Index for Cognitive Psychology.

2006 IEEE Conference on Systems, Man, and

Cybernetics, 2040-2044.

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, 2863–2867.

Oyama-Higa, M., Miao, T., Sato, K., Tanaka K. and

Cheng, H. (2007). Development of a Self-check

System for Mental Health Using a Pulse Wave Mouse.

ICSOFT 2007, The 3rd International Conference on

Software and Data Technologies.

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,

2030-2034.

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