Implementation of Machine Learning for Breath Collection

Paulo Santos

1,2

, Valentina Vassilenko

1,2

, Fábio Vasconcelos

and Flávio Gil

Laboratory for Instrumentation, Biomedical Engineering and Radiation Physiscs (LibPhys-UNL), Faculdade de Ciência e

Tecnologias da Universidade NOVA de Lisboa, Campus FCT UNL, 2896-516 Caparica, Portugal

NMT, S.A., Edíficio Madan Parque, Rua dos Inventores, 2825-282 Caparica, Portugal

Keywords: Exhaled Air, Selective Air Acquisition, Air Sampling/Monitoring, Breath Rhythm Imposition, Modelled

Breath Algorithm, Average Time of Expiration, Machine Learning Algorithm.

Abstract: Economic and technologic progresses states the analysis of human’s exhaled air as a promising tool for

medical diagnosis and therapy monitoring. Challenges of most pulmonary breath acquisition devices are

related to the substances’ concentrations that are source (oral cavity, esophageal and alveolar) dependent and

their low values (in ppb

- ppt

range). We introduce a prototype that is capable of collecting samples of

exhaled air according to the respiratory source and independent of the metabolic production of carbon dioxide.

It also allows to access the breathing cycle in real-time, detects the optimized sampling instants and selects

the collection pathway through the implementation of an algorithm containing a machine learning process. A

graphical interface allows the interaction between the operator/user and the process of acquisition making it

easy, quick and reliable. The imposition of breath rhythm led to improvements in accuracy of obtaining

samples from specific parts of the respiratory tract and it should be adapted according to their age and

physiological/health condition. The technology implemented in the proposed system should be taken into

consideration for further studies, since the prototype is suitable for selectively sampling exhaled air from

persons according to its age, genre and physiological condition.

1 INTRODUCTION

The detection and measurement of exhaled

substances is advantageous as a reliable, reproducible

and non-invasive diagnostic and prognostic tool in a

wide variety of medical conditions to assess different

vital organ functions (Miekisch et al., 2004;

Baumbach et al., 2009; Di Francesco et al., 2005;

Manolis et al., 1983; Miekisch et al., 2006; Amman

et al., 2007; Dweik et al., 2008). The human exhaled

air accommodates a complex mixture of molecules

which are expelled in every breath (~75% of nitrogen,

~15% of oxygen, ~5 of carbon dioxide (CO

) and

~6% of water vapour, inorganic compounds, volatile

organic compounds (VOCs) and aerosols). By

measuring the concentration of those molecules, it is

possible to quantify each person’s individual score

reflecting the state of health (Lourenço et. al, 2014).

There are different main targets in the analysis of

exhaled air capable to identify potential diseases, but

VOCs are the most studied and interesting to look for

as biomarkers of pathological conditions (Miekisch et

al., 2004; Baumbach et al., 2009; Di Francesco et al.,

2005; Manolis et al., 1983; Miekisch et al., 2006;

Amman et al., 2007; Dweik et al., 2008; Lourenço et

al., 2014; Mazzatenta et al., 2013). The concentration

of these compounds in the exhaled air varies

depending on the respiratory origin of exhaled air to

be analyzed, including oral cavity, esophageal and

alveolar air (Phillips et al., 1999; Di Natale et al.,

2014; Ruzsanyi et al., 2013).

Furthermore, the concentration of most of the

VOCs present in the exhaled air is very low (ppb

–

ppt

or µgl

-1

– ngl

-1

range). Thus, the detection of such

small amounts in fractions of exhaled air from

different respiratory origins has revealed itself one of

new challenges to overcome in the most recent

pulmonary breath sampling devices.

Even though there are several studies in this field,

the clinical importance of these compounds is yet to

be discovered. This work does not aim to evaluate any

group of compounds or specific VOCs. Instead, focus

will be given to the process of exhaled air sampling

according to user’s characteristics by evaluating the

influence of imposing a controlled breath rhythm.

These aspects obviously can influence the studies

involving the analyses of samples containing these

compounds.

Santos P., Vassilenko V., Vasconcelos F. and Gil F.

Implementation of Machine Learning for Breath Collection.

DOI: 10.5220/0006168601630170

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 163-170

ISBN: 978-989-758-216-5

163

1.1 Breath Sampling Devices

Multiple apparatus and methods are used in breath

studies, which in general are performed with patients

either providing their breath for storage and

subsequent later analysis (offline analysis) or

breathing directly into the analyzer for immediate

analysis (online analysis). Different approaches are

used, depending on the breath constituents to analyze,

being gas-phase the more indicated for VOC analysis

(Beauchamp et al., 2015). Nevertheless, controlling

breath sampling is crucial to enable the identification

of part of the respiratory tract from which the sample

derived and to ensure that comparative data is

generated between studies. The control is actually

made by using CO

, flow, pressure temperature or

humidity sensors (Beauchamp et al., 2015).

However, there are several constraints while

sampling the exhaled air for analysis (Alonso et al.,

2013). Despite different breath sampling

methods/devices used and several types of analysis

developed in the last decade, most of them are lacking

in accuracy and precision in the collection of an

exhaled air sample (Alonso et al., 2013). The

technology used in exhaled air collection, introduce

high variability in breath samples due to: the way the

air is expelled, the breath frequency, the length and

depth of the breath cycle and the mental and physical

condition of the patient (Basanta et al., 2007; Droz et

al., 1986; Risby, 2008).

1.2 Respiratory Cycle Monitoring

The real time monitoring of the patient’s respiratory

cycle allows the identification of respiratory phases

and the definition of the instants for breath collection.

The identification of the alveolar air portion assumes

critical importance due to the presence of diverse

constituents from endogenous origin in equilibrium

with the alveolar capillary blood vessels.

Presently the most used method for identification

of the different phases of the breathing cycle is the

capnography which allows to monitor the

concentration or partial pressure of carbon dioxide

(PCO

) in the respiratory gases by providing

information about the production of carbon dioxide,

pulmonary perfusion, alveolar ventilation and

respiratory patterns (Bhavanishankar et al.,

1995;

Mimoz et al., 2012; Bhavanishankar et al.,

1992).

Yet, the use of capnography has some limitations

regarding the collection of selective samples of

exhaled air, because it varies with (a) the inherent

variation of breath composition and concentration of

each constituent throughout the breathing cycle; (b)

the speed of the breath, which affects the composition

of the mixture between alveolar air and dead space

air; (c) the depth and frequency of breathing, which

control changes from autonomous to conscious

breathing, when a person is asked to provide a sample

of breath.

Figure 1: Graphical comparison between respiratory flow rate (at the top) and time capnogram (at the bottom). The ab segment

represents inspiration and the ba segment represents expiration on the respiratory waveforms. The red area represents the

alveolar air region, while the shaded area under the CO

curve represents the inspiratory phase of the respiratory cycle, thus

constituting rebreathing. (Adapted from Bhavani-Shankar and Philip, 2000).

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Despite the possibility of diagnose several diseases,

the modifications in the shape of capnograms (related

with such diseases) also difficult clear identification

of the expiratory segments (Kodali et al., 2013).

The problems remaining to be solved comprise

the question of how to achieve accurate, selective and

repeatable sampling, how to ensure easy and safe

handling, and maybe most importantly, the issue of

sample stability to allow a proper chemical analysis.

Therefore, the actual research in breath analysis tries

to pursue a suitable device and a precise protocol for

sampling exhaled air independently of the subject’s

metabolic production of CO

, the smoking habits, the

type of food eaten, the stomach, esophagus and mouth

condition of the patients.

Bear that in mind, this particularly work aims to

describe the influence of the implementation of

machine learning for selective breath sampling by

using a novel technology where a respiratory cycle

model is adapted to individual breathing

characteristics of the user.

BREATH MODELLING & ITS

IMPLEMENTATION

Due to the above mentioned limitations of the

capnography and, since the measurement of the

respiratory flow rate may yield the same effect of

determining the respiratory phases in a cheaper and

easier way, both were compared to identify the region

corresponding to the alveolar air and to modulate it in

a mathematical function. By overlapping a time

capnogram with a fluxogram (Bhavani-Shankar and

Philip, 2000), the area related to the end-tidal breath

was clearly identified (figure 1). Considering that,

only a flowmeter can be used for selective assessment

to the last segment of the expiration in a fluxogram

which corresponds to the alveolar region with higher

concentration.

2.1 Respiratory Cycles’ Modelling

The collection of selective portions of exhaled air by

using respiratory flow measurements, implies the use

of reference respiratory rhythms for the users to

follow. By measuring the exhaled air flow of multiple

subjects and by determining the total time of each

respiratory cycle and the transition between

respiratory phases (inspirations and exhalations),

Vassilenko et. al. (2013) were able to calculate

average signals that best describe three breathing

rhythms (slow, normal and fast). The average signals

for the three respiratory rhythms are shown in figure

2 and were used as references for development of

mathematical models which precisely characterises

breath rhythms of every patient (Vassilenko et al.,

2013).

Since it overcomes the disadvantages regarding

the imprecise identification of selective portions of

exhaled air (associated with variable depth and

frequency of breathing), the proposed method for

monitoring and selective sampling of exhaled air

through respiratory flow sensing represents a reliable

alternative method to capnography approaches.

2.2 Implementation of Respiratory

Cycles’ Models

The prototype developed by the authors performs real

time flow measurements and captures alveolar air by

synchronizing modelled respiratory cycles with the

user’s breathing cycle. The prototype comprises

hardware cells controlled by an intelligent control

software loaded on several computing devices

Figure 2: Representation of the average signals obtained for each pace imposed to tested individuals (Vassilenko et al., 2013).

Implementation of Machine Learning for Breath Collection

165

(laptops, desktops, smartphones, tablets, etc.). The

hardware module is responsible for data acquisition,

processing and its transmission to the software, and

for channeling the portion of exhaled air through the

sampling or elimination outlets.

The software comprises a graphical interface

that imposes a breathing pace to the user according to

its age, gender and physiological state and, by using

an algorithm identifies the instants of sampling and

communicates with the hardware to trigger the

sample to be stored either in a bag or go directly to an

analytical analyser. Both of that software components

were updated with the implementation of a machine

learning process in the algorithm and the imposition

of breath rhythm to the user according to its age.

The algorithm implemented in the intelligent

control software was configured to: measure the

user’s respiratory flow; detect user’s breathing

frequency; distinguish inspiratory and expiratory

breath phases; synchronize the user’s respiratory

cycle with the representative and modeled respiratory

cycle; and to calculate the average time of expiration

of the user. The average time of expiration allows the

selection the fraction of exhaled air to sample.

The machine learning process implemented in

the algorithm of the software is based on the

continuous calculation and saving of the average

exhalation time values, allowing the prediction of the

time of occurrence of a new expiration and,

consequently, the prediction of the precise time-frame

for the acquisition of the fraction of exhaled air to

sample. By this way, the machine learning process

learns the respiratory cycle of the user, and test it on

the modelled respiratory intrinsically contained in the

algorithm of the software.

In addition to the definition of global variables of the

operation and from the subject (genre, age and

physiological/health condition), the graphical

interface also provides a feedback mechanism for

communicating with the user/operator. This feedback

mechanism presents a central part of the system

because it provides multiple indicators for showing

the breathing rhythm to be followed by the user or if

the moments of breath air acquisition are occurring.

According to the information defined in the

graphical interface, the user is asked to breathe

according to a specific respiratory rhythm (figure 3).

When the breathing pace of the user is matched the

representative and modelled respiratory cycle, the

initial and final instant of the exhaled air’s fraction of

interest is identified in the respiratory cycle. This

information is communicated with the remaining

system of device in order to sample the portion of

interest of exhaled air between these instants. This

process ensures that only a fraction of exhaled air is

diverted to a collection reservoir or directly analysed

by an analytical analyser.

3 TESTS OF PERFORMANCE

To evaluate the effectiveness of the machine learning

process implemented on the software of the prototype

and the influence of a breath rhythm imposition, two

groups of individuals with different age groups (15

patients between 2 and 5 years old – children – and

30 within 18 and 27 years old – university students)

were asked to make breathing test in the prototype.

The patients had to achieve the beginning of breath

collection and, simultaneously, the minimum number

Figure 3: Prototype for alveolar air collection used on experimental tests (on the left) and the graphical interface related with

breath rhythm imposition to the user (on the right).

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of cycles, the time required to start breath sampling

and the average time of exhalation (ATE) were

registered. This method was applied two times for

each individual, in which firstly patient’s autonomous

breath rhythm was suggested and then with a

respiratory pace imposition to the subject through the

feedback present on the graphical interface.

3.1 Number of Respiratory Cycles

The results show that the number of cycles needed to

begin breath sampling are lower when a breath

rhythm was imposed to both groups of patients. More

specifically, when the university students breathed

autonomously, the number of cycles registered till the

acquisition started are higher (9.70 ± 2.22; mean ±

standard deviation) comparably with the same

number for an imposed breathing (8.56 ± 2.12). The

results are more evidently with children when

comparing the number of respiratory cycles needed to

initiate the sampling by an autonomous breathing

(17.61 ± 3.31) and an imposed one (13.93 ± 2.49).

3.2 Average Time of Exhalation

The results presented in figure 4 illustrate the

comparison of the average time of exhalation (ATE)

between an imposed/controlled breathing rhythm and

an autonomous rhythm of the patients, and the

relationship of such feature for two aging groups.

For both aging groups (children and university

students), when respiratory rhythm was autonomous,

the distribution of ATEs is significantly uneven when

compared with the distribution of ATEs for an

imposed breathing rhythm. The values of the standard

deviation for an autonomous breathing (424 and 966

ms, for children and university students, respectively)

are almost 4 times higher when compared with the

corresponding values of standard deviation for the

imposed rhythm (120 and 250 ms, for children and

university students, respectively). For both

acquisition methods, the average values of ATEs are

also significantly lower for children (974 and 1032

ms, for autonomous and controlled breathing,

respectively) comparably to the older university

students (2031 and 1752 ms, for autonomous and

controlled breathing, respectively).

3.3 Time Required to Start Breath

Sampling

Figure 5 displays box and whiskers plots related to

the time necessary to begin sampling the portion of

interest of exhaled air in order to distinguish both

procedures of breath sampling (with and without an

imposed rhythm) for both aging groups.

The time required to start breath sampling

presents several differences regarding the type of

breathing applied to the patients. For children (2-5

years old), the time needed to begin sampling the

portion of interest of exhaled air with a controlled

rhythm of breathing (32.89/31.88-34.60/s;

median/interquartile range/) is lower when compared

with an autonomous breath rhythm of the patient

(35.51/33.65-41.06/s). However, this decrease in the

Figure 4: Distribution of results of average time of exhalation (ATE)) for free breath cycles (autonomous breathing) and for

an optimized imposed rhythm (controlled breathing) with children (on the left) and university students (on the right).

2 - 5 years

18 - 27 years

Implementation of Machine Learning for Breath Collection

167

Figure 5: Time required to start breath sampling for free breath cycles (autonomous breathing) and for an optimized imposed

rhythm (controlled breathing) with children (on the left) and university students (on the right). Data are displayed in box and

whiskers plot (box depicts median with first and third quartiles, whiskers show first quartile – 1.5 interquartile range and third

quartile+1.5 interquartile range).

time required to start the sampling is not so evidently

for university students (18-27 years old) where, for an

autonomous breathing (33.59/29.04-43.73/s), this

required time is similar compared with time obtained

for an imposed breathing rhythm (30.41/27.48-

34.79). Of note, the decreased interquartile range of

the time need for breath sampling with the controlled

rhythm for both groups of patients compared with the

time obtained for an autonomous rhythm.

4 DISCUSSION

The results of performance tests applied to the

prototype show that, when concerning the number of

respiratory cycles and time needed begin the breath

sampling, the imposition of breath rhythm to the

patients (children and university) is more efficient

since less time is spent and the user is not required to

make unnecessary long breaths, which can lead to

fatigue (Roussos et al., 1996). This increased

efficiency in start of selective exhaled air sampling is

related with a quicker prediction of the time of

occurrence of a new expiration and, consequently, the

prediction the precise time-frame for its collection

which are ultimately related with the machine

learning process implemented in the prototype. These

results of the prototype’s performance tests are more

evident for children, where the suggestion of an

appropriated breath rhythm have crucial importance

due to their inability of autonomously maintain a

breath rhythm.

The results obtained for average time of

exhalation (ATE) show that the breath rhythm

imposed to patients should be adapted according to

their aging group and physiological/health condition.

Moreover, the stabilized values of ATE and the lower

interquartile range of the time required to begin

sampling the portion of interest of exhaled air, for

both aging groups, indicates the imposition of an

aging-suitable breath rhythm as the reliable way of

using the prototype for collection of exhaled air.

The majority of the existent breath samplers and

ventilators comprise several algorithms to analyse

respiration cycles of the user in order to detect

inspiration and expiration phases and to,

consequently, determine the time-window for breath

sampling. However, and for cases of dyspnea with

erratic respiratory rhythms, that determined time-

window for sampling can be too short and can

brought up multiple difficulties when obtaining such

small portions of exhaled air. Only the system

patented by Capnia, Inc. (patent number

WO2015143384 A1, 2015) is configured to impose a

breath frequency to users (young children and non-

cognizant patients) in order to avoid those erratic

respiratory episodes. Even so, that imposed frequency

does not adapts and “learns” with the user’s breathing

pace such as the proposed system does.

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The protocol necessary to use the prototype, in

which the patient has to follow the directions given

by the system and try to maintain the breathing with

the same rhythm that appears on the graphical

interface, also suggests the introduction of

improvements in the accuracy and precision on

obtaining samples of a specific part of the respiratory

tract, which consequently led to the increase in the

repeatability of the analysis applied to these samples.

Furthermore, it is excluded the introduction of

variability during breath sampling related with

breathing frequency, amplitude of the respiratory

cycle, the mental and physical condition of the

patient, as well as, the method applied by the person

who asks for the patient to breathe.

5 CONCLUSIONS

The research work demonstrated herein presents a

suitable and novel technology and related protocol of

using it for selectively sampling exhaled air regarding

the subject’s: metabolic production of CO

, smoking

habits, type of consumed food, stomach, esophagus

and oral cavity conditions. Moreover, the

implementation of a user-dependent’s respiratory

cycle model on the prototype used in this work could

allow a more accurate way to collect portions of

exhaled air according to the exhaled air’s respiratory

origin. This collection is done from single or multiple

exhalations, for online or posterior analytical analysis

for medical diagnosis and/or therapy monitoring, in a

quick, reliable, non-invasive way, applied at any

stage of life.

The imposition of a respiratory rhythm

according to the characteristics of the user (age,

gender and physiological/health condition) and the

machine learning process implemented on the

prototype led to improvements in the accuracy in

sampling breath from specific parts of the respiratory

tract and decreases the variability of the samples

related with breath frequency, amplitude of the breath

cycle, mental and physical condition of the patient.

However, the implemented algorithm have to be

optimized for better performance in real healthcare

environments and the respiratory rhythm appearing in

graphical interface should be interactively adapted

according to all age groups, especially to the elderly

and children who have more difficulty to follow this

method. We also believe that future and similar

applications for mobile devices should be developed

to help the patients to learn and train the respiratory

rhythm while the respective portable sampling

equipment for analysis is not commercially available.

The final application should be suitable to different

group stages simplifying the breath sampling process.

ACKNOWLEDGEMENTS

The authors would like to thank all volunteers that

offered their time to perform tests for the acquisition

of their respiratory cycles and for the tests of

performance of the prototype. We thank to

parents/guardians of the children who executed the

same tests, for authorize their participation and to the

daycare of FCT (center of pre-school education) for

providing space and conditions to its implementation.

The authors would also thank the Fundação para a

Ciência e Tecnologia (FCT, Portugal) for co-

financing the PhD grant (PD/BDE/114550/2016) of

the Doctoral NOVA I4H Program.

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