Design of a Breath Analysis Device for Self-monitoring and Remote

Health-care

D. Germanese

, M. D’Acunto,

1,2

and O. Salvetti

CNR-ISTI, Institute of Information Science and Technology, National Research Council, via Moruzzi 1, Pisa, Italy

CNR-ISM, Institute of Structure of Matter, National Research Council, via Fosso del Cavaliere 100, Roma, Italy

Keywords: Self-monitoring, Breath Analysis, Home-care, Gas Sensors, Portable Device, Signal Processing.

Abstract: Technique as new as promising, breath analysis enables the monitoring of biochemical processes in human

body in a non-invasive way. This is why it is drawing, more and more, the attention of scientific

community: many studies have been addressed in order to find a correlation between breath volatile organic

compounds (VOCs) and several diseases. Despite its potential, breath analysis is still far from being used in

clinical practice. These are some of the principal reasons: (i)high costs for the standard analytical

instrumentation; (ii)need of specialized personnel for the interpretation of the results; (iii)lack of

standardized procedures to collect breath samples. Our aim is to develop a device, which we call Wize

Sniffer (WS), based on commercial gas sensors, which is: (i)able to analyse breath gases in real time;

(ii)portable; (iii)low-cost; (iv)easy-to-use also for non-specialized personnel. Another aim is to foster

homecare, that means promote the purchase and the use, also in home environment, of such device. The

Wize Sniffer is composed of three modules: signal measurement, signal conditioning and signal processing.

To satisfy the goal of developing a device by using low-cost technology, its core is composed of an array of

commercial, low cost, semiconductor-based gas sensors, and a widely employed open source controller: an

Arduino board. To promote the use of such device also in home environment, and foster its daily use, it is

programmed in order to send breath test results also to a remote pc: the pc of user’s physician, for example.

In addition, the design of the Wize Sniffer is based on a modular configuration, thus enabling to change the

type of the gas sensors according to the breath molecules to be detected. In this case, we focus our attention

to the prevention of cardio-metabolic risk, for which the healthcare systems are registering an exponential

growth of social costs, by monitoring those dangerous habits for cardio-metabolic risk itself.

1 INTRODUCTION

Nowadays, the gold standard methods for gas

analysis are techniques such as Gas Chromatography

(GC), or Proton Transfer Reaction- Mass

Spectrometry (PTR-MS), and they can be used to

analyze also breath gases with high accuracy and

sensitivity (D. Guo et al., 2010). On the other hand,

these methods are very expensive; moreover, the

analysis, very time consuming, can be performed

only by specialized personnel.

More recent approaches exploit e-noses to

analyze the breath. E-noses, based on gas sensors,

may lose in sensitivity and accuracy with respect

GC/PTR-MS, but they are able to detect in real time

some specific molecules. The most commonly used

sensors for e-noses are solid state gas sensors. Their

working principle is based on a reversible interaction

of the gas with the surface of a solid state material,

which results in a physical eﬀect, depending on the

sensing material, used to achieve the detection of

gases in solid state gas sensors. For example, optical

processes employ infra-red absorption of gases,

whilst chemical processes allow detecting the gas by

means of a selective chemical reaction with a

reagent. The detection of this reaction can be

performed by measuring the conductivity change of

gas-sensing material, or the change of capacitance,

work function, mass, optical characteristics or

reaction energy released by the gas/solid interaction.

E-noses are designed for broader applications

(environmental, industrial ones), rather than for

medical ﬁeld. Nowadays, the e-noses that are used

for breath analysis exploit very expensive approach

to detect breath compounds, and they are suitable for

one (or, at most, two) molecule only; we can

mention Bedfont’s PiCO+Smokerlyzer (able to

Germanese, D., D’Acunto, M. and Salvetti, O.

Design of a Breath Analysis Device for Self-monitoring and Remote Health-care.

In Doctoral Consortium (DCBIOSTEC 2016), pages 9-14

detect the exhaled Carbon Monoxide) and NOBreath

(able to detect the exhaled Nitric Oxide),

(www.bedfont.com/shop/smokerlyzer,

www.bedfont.com/shop/nobreath), Toshiba’s

research prototype Breathalyzer (able to detect the

exhaled Acetone)

(www.toshiba.co.jp/about/press/2014_03/pr1801.ht

m).

In this work we present the first prototype of the

Wize Sniffer (WS), a portable device based on

chemical semiconductor-based gas sensor array

which represents a low-cost effort to analyze

exhaled breath in real time. In particular, the WS is

able to monitor in real time a specific number of

breath molecules related to noxious habits for

cardio-metabolic risk and oxidative stress. Not only:

an Arduino board is programmed to read sensors’

output and send breath analysis results to a remote

personal computer, which might be the one’s own,

or the physician’s one. In addition, the modular

configuration of the WS enables to change the gas

sensors to detect other types of breath molecules

thus personalizing the device. The use of a low-cost

technology, the compactness of the device and the

possibility to send the results also to a remote

personal computer, allow for a user’s daily

screening, also in home environment.

2 BREATH COMPOUNDS

DETECTED BY THE WIZE

SNIFFER AND CLINICAL

IMPLICATIONS

Breath is composed of oxygen, carbon dioxide,

water vapor, nitric oxide, and a large number of

volatile organic compounds (VOCs) which origin

can be endogenous (that means, they originate from

metabolic processes that occurs in human body and

participate to alveolar exchanges) or exogenous (that

means, they derive from food, or beverages, or

dermal adsorption) (W. Miekisch et al., 2004).

As a consequence, we can affirm that each breath

contains fundamental information about the internal

state of a person. Indeed, more than 35 of the VOCs

present in our breath have been assessed as

biomarkers for particular diseases or metabolic

disorders: for example, increased level of ammonia

in breath may be related to renal diseases (D. Guo et

al., 2010); ethane and pentane derive from lipid per-

oxygenation in case of oxidative stress (M. Phillips

et al., 2003; F. Pabst et al., 2007).

We focus our attention on a set of breath

molecules, some of which related to those noxious

habits for cardio-metabolic risk, such as smoking

and alcohol intake. The molecules detected by the

Wize Sniffer are listed here:

 Carbon monoxide (CO): it is naturally

produced by the action of heme oxygenase on

the heme for haemoglobin breakdown. This

produces carboxyhemoglobin, which is more

stable than oxyhemoglobin. Indeed, an

increase of CO leads haemoglobin to carry

less oxygen through the vessels. CO is

present in cigarette smoke, very dangerous

for cardio-metabolic risk. Its baseline value in

a healthy subject is round about 3.5ppm (up

to 14-30ppm in smokers);

 Hydrogen (H

): it derives from the

breakdown of the carbohydrates in the

intestine and in the oral cavity by anaerobic

bacteria. Its baseline value is round about

9.1ppm;

 Ammonia (NH

): an increase of NH

blood may be caused by cigarette smoke,

renal failure, cardiac failure, changes in

cardio-circulatory system. Its baseline value

is round about 0.42ppm;

 Ethanol (C

O): exhaled ethanol can be

classiﬁed as endogenous or exogenous.

Exogenous Ethanol comes from alcoholic

drink. It is recognized that ethanol breakdown

leads to an accumulation of free radicals into

the cells, a clear example of oxidative stress.

Ethanol may cause arrhythmias and depresses

the contractility of cardiac muscle. Its

baseline value is round about 0.62ppm;

 Carbon dioxide (CO

) and Oxygen (O

Their variations show how much O

retained in the body, and how much CO

produced as a by-product of cellular

metabolism. In most forms of lung diseases

and some of congenital heart disease

(cyanotic lesions-bluish-grey discoloration of

the skin, lack of O

in the body), a decrease of

exhaled rate is commonly observed. It

must be noted that the breathing rate

inﬂuences the level of CO

in the blood: slow

breathing rates cause Respiratory Acidosis

(i.e., increase of blood CO

partial pressure,

which may stimulate hypertension or heart

rate acceleration). On the contrary, too rapid

breathing rate leads to hyperventilation,

which may provoke Respiratory Alkalosis

(i.e., decrease of blood CO

partial pressure,

no longer ﬁts its role of vasodilator, leading

to possible arrhythmia or heart trouble). Their

DCBIOSTEC 2016 - Doctoral Consortium on Biomedical Engineering Systems and Technologies

baseline values are round about 40000ppm

for CO

and 13-15% for O

;

 Hydrogen Sulﬁde (H

S): it is a vascular

relaxant agent, and has a therapeutic eﬀect in

various cardiovascular diseases (myocardial

injury, hypertension). In general, H

S could

have therapeutic eﬀect against oxidative

stress due to its capability to neutralize the

action of free radicals. Its baseline value is

round about 0.33ppm.

3 WIZE SNIFFER’S HARDWARE

AND SOFTWARE

ARCHITECTURE

A block scheme of WS is shown in Figure 1. Basing

on this scheme, we can describe the framework of

WS as composed of three modules: signal

measurement, signal conditioning and signal

acquisition.

Figure 1: Schematic sketch of the WS’s architecture. The

core is an acquiring device which includes a gas sampling

box (of 600ml according to the tidal volume (D. Shier et

al., 2007) and made up of ABS and Delrin) where six gas

sensors are placed, and a micro-controller board. Since the

sensors’ output is affected by the water vapour present in

exhaled gases, a HME ﬁlter is placed at the beginning of a

corrugated tube reducing the humidity from initial 90% to

60-70%. In addition, the humidity percentage is monitored

within the sampling box, as well as the temperature. Other

two gas sensors having shorter response time work in

ﬂowing-regime by means of a sampling pump, which

works at 120 ml/s. A flow-meter monitors the exhaled

breath volume. A flushing pump purges the chamber and

recovery the sensors’ steady state between two

consecutive measures.

The measurement circuit (store chamber + gas

sensor array) detects the breath molecules and

transforms gas signals into electronic signals, which

are processed by the microcontroller board. The

microcontroller used in our system is a low cost,

widely employed open source controller: Arduino

Mega 2560 with Ethernet module. Table 1

summarizes all the VOCs detected by the WS, and

the gas sensors used. Most of the gas sensors are

manufactured by Figaro Engineering, and they are

not expensive at all. They are based on a metal-

oxide semiconductor sensing element, that is, a

variation of sensing element’s internal resistance

occurs when it detect gas particles.

Table 1: VOCs to be detected and gas sensors used.

Breath molecule Sensor and its sensitivity (ppm)

Carbon

monoxide

TGS2442 (50-1000ppm),

TGS2620 (50-5000ppm)

Ethanol

TGS2602 (1-10ppm) and

TGS2620 (50-5000ppm)

Carbon dioxide TGS4161, 0-40000

Oxygen MOX20, 0-16%

Hydrogen sulfide TGS2602, 1-10

Ammonia

TGS2444 (10-100ppm) and

TGS2602 (1-10ppm)

Hydrogen

TGS821 (10-5000ppm), TGS2602

(1-10ppm) and TGS2620 (50-

5000ppm)

As briefly described in Section 4, in order to receive

breath test results from the WS even on a remote

Personal Computer (for example, the physician’s

one), a client-server architecture is implemented. It

means, the Arduino Mega2560 processes sensors’

raw data, executes a daemon on port 23, waits a

command line from the PC and provides the data. A

measure is considered valid if the user’s exhaled

volume equals at least 600ml (store chamber’s

volume, see Figure 1). How the WS microcontroller

board analyzes row data is described in the next

section. In ﬁgure 2 the ﬁnal conﬁguration of the

Wize Sniﬀer is shown.

Figure 2: Final conﬁguration of the ﬁrst prototype of the

Wize Sniffer.

Design of a Breath Analysis Device for Self-monitoring and Remote Health-care

4 WS FUNCTIONALITY TESTS

AND DATA PROCESSING

If, on one hand, developing a device by using low-

cost technology means facilitating its purchase and

use, on the other hand, especially in this case, this

may represent a challenge. A challenge in terms of

data processing, because, on one side,

semiconductor-based gas sensors are, of course, very

low cost, very easy to be integrated and very

sensitive; on the other side, they are not selective

and they are affected by cross-sensitivity (in Table 1

we can note that the sensors TGS2602 and TGS2620

detect more than one molecule). Their non-

selectivity and their cross-sensitivity have

implications on data analysis, making it very

complicated, especially if a quantitative analysis of

the detected molecules should be done. Moreover,

the behaviour of such type of gas sensors is not

linear.

For our purposes, we aim to make a quantitative

analysis of the detected molecules. It means we aim

to calculate the concentration (in ppm) of the

detected breath molecules. Such approach requires:

 an accurate reconstruction of the sensors’

sensitivity curves under our measurement

conditions (32°C+/-10% and 70%RH+/-10%,

it means, conditions that are very close to the

breath physiological ones);

 a model to describe how each input (it means,

each molecule) influences the output (it

means, the variation in sensors’ internal

resistance). The simplest model may be

represented by a linear regression;

 an accurate evaluation of sensors’ cross

sensitivity;

 an accurate evaluation on how temperature

and humidity affect sensors’ outputs;

 a model to calculate the concentration of each

molecule.

Meanwhile, since this approach is taking

considerable time, we are exploiting a more

traditional approach for data analysis based on

features extraction (by means of Principal

Component Analysis) and classification (by means

of K-Nearest Neighbour algorithm). In any case, a

signal pre-processing is needed to compensate drifts

and sample-to-sample variations. This more

traditional approach has been implemented first

using a synthetic database of 114 subjects containing

individuals of diﬀerent age (in the range 30-60 years

old), habits (moderate/heavy smokers, non-smokers,

teetotal, moderate/heavy drinkers), lifestyles

(sedentary/ sporty, etc.), and body type. This

database was implemented as if all the gas sensors

worked correctly, in ideal conditions. Plotting the

results of the analysis of the synthetic database by

the PCA (Figure 3), several clusters, highlighted by

means of an algorithm based on KNN (Figure 4) can

be identiﬁed.

Figure 3: Biplot of the synthetic database in the ﬁrst two

principal components. Several clusters can be

distinguished.

Figure 4: Score plot of the synthetic database analysed by

Principal Component Analysis; LsHd = Light Smokers,

heavy drinkers; LsVHd = Light Smokers, very heavy

drinkers; HsHd= Heavy Smokers, heavy drinkers; HsVHd

= Heavy Smokers, very heavy drinkers. For “Healthy

class” we intend subjects with low cardio-metabolic risk.

Then, a measuring protocol has been draft in order

to test the Wize Sniﬀer on a population of 26 healthy

individuals, with diﬀerent age (range 30-60 years

old), habits, lifestyles, body type. Why just healthy?

Because in this case we are focusing on cardio-

metabolic risk, not on a disease. By detecting

molecules related to those noxious habits for cardio-

metabolic risk, the Wize Sniffer should not make a

diagnosis, but should only help the user to monitor

his/her well-being and lifestyle. Indeed, as shown in

DCBIOSTEC 2016 - Doctoral Consortium on Biomedical Engineering Systems and Technologies

Figure 4, the classifier classifies the subjects

according to their habits. For a single-subject

monitoring, further statistical analysis over long time

periods should be carried out to evaluate if the

subject increases/decreases his/her cardio-metabolic

risk basing on a change of class.

For the measuring protocol, the methodological

issues about breath sampling procedure have been

taken into account (W. Miekisch et al., 2008). In

practice are used three methods of sampling:

“alveolar (end-tidal) sampling”, if only systemic

volatile biomarkers are to be assessed, “mixed

expiratory air sampling” (which corresponds to a

whole breath sample), “time-controlled sampling”

(which corresponds to a part of exhaled air sampled

after the start of expiration; this method shows large

variations of samples compositions because of wide

variations of individual breathing manoeuvers). For

our purposes, mixed expiratory air sampling method

was chosen, since our interest was focused on both

endogenous and exogenous biomarkers. In addition,

since the composition of single breaths may vary

considerably from each other, because of diﬀerent

modes and depth of breathing, in order to average

breath-by-breath fluctuations in composition due to

the irreproducible lung emptying and flow variations

(F. Di Francesco et al., 2008), we preferred a

sampling of multiple (three) quite breaths.

The KNN classiﬁer was able to correctly classify

20/26 subjects, as shown in Table 2. While an

alcohol consumption up to 1-2 Alcohol unit/ day is

often considered not dangerous (in healthy subjects),

smoking is considered very noxious in any case.

We can note that the classifier seems to be not able

to recognize smoker subjects. Actually this may be

due to Carbon Monoxide sensor which has a

minimum LOD of 50ppm (very high), thus resulting

not able to detect Carbon Monoxide even in heavy

smoker subjects. Indeed, we are testing the

performances of another Carbon Monoxide

semiconductor-based gas sensor, MQ-7, which

should be more sensitive and it has a lower LOD

(round about 20ppm, but we are evaluating its

response also to lower concentration, for example 2-

5ppm). In addition, its cost is lower than the one of

CO Figaro gas sensor.

5 CONCLUSIONS

Here we described the first prototype of a low-cost,

portable device for the self-monitoring of those

noxious habits for cardio-metabolic risk by detecting

in the breath molecules principally related to

Table 2: Outcome of the KNN classifier used for classify

WS data. In the I column, the subject’s ID; in the II

column subject’s habits are reported; in the III column the

outcome of the KNN classifier; in the IV column the

right/wrong classification is commented.

AU= Alcohol unit; CIG.= cigarette; D= day; W= week

Subj

Habits Classification Comment

215

1 AU/D

No risk ok

218

1 AU/twice a W

No risk ok

211

12-13 CIG / D; 1

AU / 4-5 times a W

No risk

heavy

smoker

201

1 AU /once a W

No risk ok

207

1 AU/1-2times a W

No risk ok

213

teetotal, non smoker

No risk ok

208

1 AU / 3 times a W

No risk ok

221

teetotal, non smoker

Heavy Drinker No risk

220

1 AU /2 times a W Heavy Drinker

No risk

214

1 AU < once a W

No risk ok

206

1 AU/5-6times a W

No risk ok

205

1 AU 3 times a W

No risk ok

223

2 CIG /D; 1 AU /

twice a W

No risk ok

212

1 AU / D

No risk ok

216

1 AU / once a W

No risk ok

217

teetotal, non smoker

No risk ok

203

1 AU < once a W

No risk ok

209

1 AU < once a W

No risk ok

202

1 AU/5-6times a W

No risk ok

210

1-2 AU / D

No risk ok

225

4 CIG /D; 1 AU <

once a W

No risk

Light

smoker

224

1 AU < twice a W

No risk ok

204

1-2 CIG /D; 1 AU <

2 times a W

Heavy Drinker

Light

smoker

219 1 AU/2 times a W No risk ok

226 1 AU /once a W No risk ok

222

10-15 CIG /

D; 1

AU / once a

No risk

heavy

smoker

smoking habit and alcohol intake. Anyway, its

modular conﬁguration and its ease of use allow

changing the type of gas sensors according to the

breath molecule to be detected, and the disorder to

be monitored. The low cost and compactness of the

device allow for a daily screening that, even if

without a real diagnostic meaning, could represent a

pre-monitoring, useful for an optimal selection of

more sophisticated and standard medical analysis.

Further studies will be addressed in order to

improve the performances of the WS, and to

investigate the criticalities of such type of device

and of breath analysis in general. In particular:

 the metabolic pathway of the breath molecules

to be detected will be studied in depth;

Design of a Breath Analysis Device for Self-monitoring and Remote Health-care

 breath sampling methods will be further

investigated. A standardized procedure for

breath sampling may be very useful, since the

composition of each breath is largely

influenced by many factors, such as lung

volume (Jones J.G., 1967), posture

(Anthonisen N.R. et al., 1970), flow rate

(Jones J.G. and Clarke S.W., 1969), ambient

air (F. Di Francesco et al., 2008);

 as described in Subsection 5.3, a model to

calculate the concentration of the detected

molecules, based on the non-linear behaviour

of semiconductor gas sensors, will be

implemented; then, the quantitative analysis

performed by the Wize Sniffer may be

compared to the one performed by the gold

standard (Mass Spectrometry, for example);

 the number of functionality tests will be

increased, and more experimental results will

be provided;

 the possibility to develop ad-hoc gas sensors

based on semiconductor polymers as sensing

element will be investigated (Ding B. et al.,

2009).

ACKNOWLEDGEMENTS

This work was funded in the framework of the

Collaborative European Project SEMEOTICONS

(SEMEiotic Oriented Technology for Individuals

CardiOmetabolic risk self-assessmeNt and Self-

monitoring), grant N. 611516.

Massimo Magrini, Paolo Paradisi, Marco Righi,

and COSMED s.r.l. are warmly acknowledged for

useful support.

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