Automatic Quality Assessment of Smart Device Microphone

Spirometry

B. Pinho

1,2

, R. Almeida

, C. Jácome

, J. P. Teixeira

, R. Amaral

2,4

, F. Lopes

, T. Jacinto

1,2,4

R. Guedes

, M. Pereira

1,2

, I. Gonçalves

1,2

and J. A. Fonseca

1,2

MEDIDA - Serviços em Medicina, EDucação, Investigação, Desenvolvimento e Avaliação, LDA, Porto, Portugal

CINTESIS - Centro de Investigação em Tecnologias e Serviços de Saúde, MEDCIDS,

Departamento de Medicina da Comunidade Informação e Decisão em Saúde, Faculdade de Medicina,

Universidade do Porto, Porto, Portugal

INESC TEC Campus da FEUP, Porto, Portugal

Escola Superior de Saúde, Politécnico do Porto, Porto, Portugal

Keywords: Asthma, Microphone Spirometry, Spirometry Quality Assessment.

Abstract: Lung function tests are critical for diagnosis and monitoring of asthma and other respiratory diseases.

Monitoring of lung function, in the absence of a healthcare professional, is very challenging but may be

obtained through Smart Devices if automated quality assessment systems guarantee the proper technique

during the forced expiratory manoeuvre. This paper describes the evaluation of one such system that uses the

microphone of smart devices, regarding the initial effort of forced expiratory manoeuvres using the Back

Extrapolated Volume. A health professional recorded microphone spirometry in 55 children (5-10 years),

using a mobile game engineered for the purpose, and registered its quality. At least one acceptable manoeuvre

was achieved for 96% of the children using a featured threshold. Using a stricter threshold of 5% of forced

vital capacity, it was possible to ensure at least one acceptable manoeuvre for 69%. While the obtained results

are comparable to findings in literature for regular spirometry in this age group, further work is required before

we can determine whether the proposed algorithm is effective in real life.

1 INTRODUCTION

Spirometry is the most widely used non-invasive test

of lung function, used for detection and diagnosis of

various respiratory diseases, including asthma, in

children (Pierce, 2005). The performance of a forced

expiratory manoeuvre (FEM) involves three distinct

phases: maximal inspiration; a “blast” of exhalation;

and continued complete exhalation to the end of test

(Miller et al., 2005). On spirometers a plot called a

spirogram is generated at the end of each manoeuvre,

measuring air flow. This is typically presented to

health professionals as a volume-time (Figure 1) and

a flow-volume graph. A FEM requires the coaching

of the patient by a specialized health professional, due

to the quality and repeatability criteria that must be

met (Miller et al., 2005). Assuring that these criteria

are fulfilled is of paramount importance, as

neglecting them has led to over 25% of false-positives

in diagnosing chronic obstructive pulmonary disease

(Moger et al., 2013), and 50% of false-negatives

(Walters et al., 2011).

Figure 1: Volume-time curve showing the calculation of the

Back Extrapolated Volume.

Pinho, B., Almeida, R., Jácome, C., Teixeira, J., Amaral, R., Lopes, F., Jacinto, T., Guedes, R., Pereira, M., Gonçalves, I. and Fonseca, J.

Automatic Quality Assessment of Smart Device Microphone Spirometry.

DOI: 10.5220/0006866701150122

In Proceedings of the 8th International Joint Conference on Pervasive and Embedded Computing and Communication Systems (PECCS 2018), pages 115-122

ISBN: 978-989-758-322-3

115

One of those criteria is the satisfactory start of

exhalation, measured by the back extrapolated

volume being higher than 5% of the Forced Vital

Capacity (the total airflow in litres exhaled by the

patient) or 150mL, whichever is greater (Miller et al.,

2005). The back extrapolated volume is the exhaled

volume at the instant where the maximum derivative

of the volume-time curve crosses the abscissa axis

(Figure 1).

Due to the growing popularity of smart devices,

work has been developed to enable accurate

estimation of FEM medical parameters outside of a

clinical setting (or in otherwise resource constrained

settings) making use of their computational

capabilities and embedded sensors, especially the

microphone (Larson et al., 2012), (Stein, 2013), (Liu,

2013), (Teixeira et al., 2015), (Zubaydi, 2016).

However, there lies a largely unexplored problem

common to all these solutions, which is that of

assuring the validity of the manoeuvre in the absence

of a health professional. In 3 of these works, we have

Improvements in automatic spirometry quality

analysis have been recently developed for clinical

spirometers (Melia et al., 2014), (Luo et al., 2017),

but they do not take into account specific challenges

faced by FEM acquired by microphone, henceforth

referred to as microphone spirometry. To the best of

our knowledge, no work has been done so far in the

field of automatic quality evaluation in microphone

spirometry.

This paper presents a first attempt of automatic

quality evaluation in microphone spirometry,

specifically on an initial effort criterion based on the

ATS/ERS quality criteria (Miller et al., 2005).

Adequate initial effort is already difficult even with

the presence of a specialized health professional

incentivizing the child. In the absence of such

personnel, it becomes even more critical to correctly

determine if the patient exhaled with enough force.

The development of this automatic quality

evaluation module is part of a mobile serious game

called “Ar.cade”. It is a virtual pet game, with an

asthmatic dragon. Its purpose is to allow and

incentivize long term asthma monitoring in children

from 5 to 10 years old, away from their healthcare

professional, via microphone spirometry with smart

mobile devices. While more typical actions such as

feeding and cleaning a virtual pet will be available to

the player, the main focus is on the mini-games.

These revolve around the usage of the microphone as

the main game input, rewarding the player for

properly executed FEM and providing feedback on

how to improve, in case of failed quality criteria.

2 EXPERIMENTAL SETUP

The assessment of identification methods requires a

properly annotated database of microphone

spirometry recordings. Therefore, one mini-game of

Ar.cade was used for data collection and

classification.

2.1 Game Design

Ar.cade is an Android mobile virtual pet game,

developed and implemented in C# using the

FlatRedBall game engine. Among other things, inside

it can be found mini-games that use the microphone

as the main controller, for the purpose of recording

FEM’s. The selected mini-game is a physics-based

game, using the Farseer physics engine. The player

character is a dragon, which is able to make a fireball-

like projectile with its breath (Figure 2).

Figure 2: The game's idle state.

The main game loop consists of a 5 main phases:

1 Inhale phase (Figure 3): the screen zooms in on

the dragon, a countdown with visible and audible

feedback starts, and at the same time the dragon

performs an animation to inhale deeply. As the

countdown approaches the end, other background

sound effects are gradually muted.

Figure 3: The game's inhale phase.

2 Exhale phase (Figure 4): Having all existing game

sounds muted and the countdown finished, the

SPCS 2018 - International Conference on Signal Processing and Communication Systems

116

audio recording starts. The dragon performs an

exhalation animation for a total of 3.5 seconds,

after which the recording stops.

Figure 4: The game's exhale phase.

During this animation, fire particles are blown by

the dragon and a slowly expanding projectile starts to

form.

2 Upgrade phase (Figure 5): Quality of the

manouvre is reflected by upgrading or

downgrading projectile. For now, the final state is

determined by the type of test the healthcare

professional intends to perform, but in the future

the quality detection module will evaluate this

automatically.

Figure 5: The game's upgrade phase.

3 Destruction phase (Figure 6): The projectile is

launched towards a destructible structure built of

blocks with different materials.

Figure 6: The game's destruction phase.

Feedback phase (Figure 7): The player receives an

award based on his/her performance. In the future,

this will be linked with the overarching virtual pet

game’s economy, however currently just serves as

another potential instant gratification source. More

importantly, this will be the place where the player

will receive instructions on how to improve their

manoeuvre and receive the next possible ranking, in

case of a sub-optimal manoeuvre.

Figure 7: The game's feedback phase.

The dragon’s inhale and exhale animations serve

the purpose of incentivizing the child to perform

maximal effort on both phases, while the rest of the

gameplay elements are an attempt to provide the child

with instant gratification for the effort made.

2.2 Audio Processing Pipeline

To extrapolate the flow-time chart from an audio

capture, the processing pipeline (Figure 8) as

presented in (Teixeira et al., 2015) was implemented

in C# for integration with the Ar. cade project. In that

work, an attempt was made to measure and classify

lung function based on signal processing,

constructing the flow-time curve. This would then be

followed by a machine learning stage that enabled the

regression of typical spirometry parameters. To

perform this regression, a previously obtained

database from adults was used for model training

purposes. Given that we have no identical database

for users in our target age group, we were unable to

attempt a similar approach – that is, to try and

establish absolute medical parameters. We have

chosen to rely only on relative spirometry criteria for

this work.

Automatic signal segmentation precedes the pre-

processing stage. This serves to remove non-

expiration sounds from the input to be analysed: a

modified version of the back-extrapolation algorithm

was used to determine the initial instant, and a sliding

window algorithm based on the magnitude ratio

threshold to determine the end (Teixeira et al., 2015).

Automatic Quality Assessment of Smart Device Microphone Spirometry

117

The pre-processing stage attempts to transform

the raw pressure data obtained from the microphone

into the airflow measured at the lips. Afterwards, the

envelope of the signal is extracted, with smoothing

being applied on the post-processing stage.

3 DATA COLLECTION

3.1 Participants

The target population were children with/without

asthma, aged between 5 to 10 years old. Data

collection occurred in an informal environment,

namely with the collaboration of a school.

Data was gathered anonymously, with written

permission of legal guardians of all the children.

Besides the FEM audio recordings, self-reported

data comprised of sex, ethnicity, age, and if they had

asthma. Each child was assigned an internal random

ID, enabling anonymous same-child recording

analysis and comparison.

3.2 Procedures

A specialized healthcare professional performed the

data collection. After introducing the child to the

game’s concept and performing a demonstration of

the game, two different tests were made:

- “Hot air” test: Have the child exhale with a wide

open mouth, focusing on achieving a good

aperture and not emphasizing the need to exhale

with maximal force.

- Maximal force test: Have the child exhale with the

same mouth aperture, only this time with the

added requirement and emphasis of maximal

force, evaluated by the healthcare professional.

For each of these tests, the goal was to achieve at least

one successful recording. At the end of each

maneuver, the healthcare professional registered its

quality with an in-game form assessing the maneuver

on 6 different criteria in a yes/not sure/no format:

- Good mouth aperture

- Good initial effort

- Good continuous effort

- Good finish

- No cough/outside interference

- No glottis closure

3.3 Algorithm Development and

Evaluation

Official guidelines defined by the ATS (Miller et al.,

2005) state that BEV should be lower than 150mL or

5% of the FVC, whichever is higher. As a first attempt

to automatically determine if the manoeuvre’s initial

effort was acceptable or not, the official guidelines

were used in as much as possible, and we

implemented an algorithm for BEV calculation

according to ATS standards (Miller et al., 2005).

Given that we do not have access to absolute

values in our implementation of the processing

pipeline, we chose to only use the relative criterion of

BEV < 5% of FVC. We then compared the results

obtained by this classifier with the healthcare

professional’s classification of the manoeuvres,

acting as our ground truth.

Figure 8: In orange, the acceptable BEV ranges using the

ATS guidelines; in blue, the ranges using the implemented

algorithm (The ATS ranges overlap with the algorithm’s

ranges).

In figure 8 is shown that for FVC values under 3

litres, the ATS guidelines are increasingly more

lenient as the FVC decreases compared to just using

the relative criteria implemented in the algorithm. To

evaluate how relevant this issue is for our collected

data, we used children’s age specific reference

equations for FVC developed by (Koopman et al.,

2011). According to these equations, FVC varies with

age, height and sex. We used the self-reported age and

sex, while for height the World Health Organization’s

height-for-age charts were considered (de Onis et al.,

2007) (“WHO | Height-for-age (5-19 years),” n.d.).

To illustrate the FVC boundaries of our targeted

population the mean FVC, along with the lower and

upper limit of normal (LLN and ULN) for a 5% cut-

off, are shown in tables 1 and 2.

Table 1: FVC percentiles (5% cut-off) for 10-year-old

children in the 99

height percentile.

LLN Mean ULN

Male 2.52L 3.02L 3.65L

Female 2.35L 2.86L 3.41L

This allows us to conclude that, for our target age

group, it is more likely to be dealing with cases where

SPCS 2018 - International Conference on Signal Processing and Communication Systems

118

the expected FVC is under 3L, making our algorithm

more stringent than the official ATS guidelines.

Table 2: FVC percentiles (5% cut-off) for 5-year-old

children in the 1

height percentile.

LLN Mean ULN

Male 0.73L 0.91L 1.11L

Female 0.82L 1.00L 1.19L

As an initial attempt to overcome this limitation,

we defined an age adjusted BEV threshold for each of

the target ages. This threshold was calculated as thus:

BEV

= 0.15/FVC

(1)

where FVC

is the mean expected FVC value for the

given children’s age (i), assuming the 50

height

percentile of the WHO standards. The resulting BEV

thresholds for males and females can be seen on table

3 and 4, respectively. By doing this, we are trying to

have the acceptable BEV for a specific age match the

static 150mL criterion defined by the ATS, but by

using the FVC percentage relative criterion.

The value found for 5-year-old males is in

complete agreement with the findings of (Aurora

2004) in pre-schoolers observed BEV/FVC ratio. In

that work, a possible quality control cut-off of 12.5%

is suggested.

Table 3: Age adjusted BEV thresholds for the 50

height

percentile of each presented age in males.

Age 5 6 7 8 9 10

BEV

(%)

12.5 10.7 9.3 8.2 7.0 6.4

Table 4: Age adjusted BEV thresholds for the 50

height

percentile of each presented age in females.

Age 5 6 7 8 9 10

BEV

(%)

12.9 11.1 9.8 8.6 7.5 6.6

Considering that the target is the classification

made by the healthcare professional, where positives

indicate an acceptable manoeuvre, the prediction is

the resultant classification by the algorithm for the

specified BEV threshold can be evaluated.

To compare different settings for the algorithm,

two measures were used, namely the F

score and

accuracy. The F

score is the harmonic mean of

precision and sensitivity given by:

= 2TP / (2TP + FP + FN)

(2)

and the accuracy, which measures how often is the

classifier correct, with the following formula:

A = (TP + TN) / (TP + TN + FP + FN)

(3)

4 RESULTS AND DISCUSSION

4.1 Participants

A total of 55 children between 5 to 10 years old have

participated, 52 within 8 to 10 years of age. Out of

these children, 31 were females and 24 were males.

Only 4 (7.2%) of these children reported to suffer

from asthma.

In Table 5 we present the distribution of the

classifications by the healthcare professional, along

with the total amount of recordings obtained for each

type of test. In this case, “yes” represents a positive

evaluation of the child’s initial effort. Table 6

characterizes the amount of manoeuvers required by

the healthcare professional to obtain a positive

classification.

Table 5: Total amount of recordings obtained per test, and

the distribution of acceptable BEV quality classification by

the healthcare professional registered quality.

Test Type Total Yes Not Sure No

Hot Air Test 89 65 2 22

Max Force Test 144 108 3 33

Table 6: Number of attempts until a positive classification

by the healthcare professional.

Test type Max Median Min

Hot Air Test 3 1 1

Max Force Test 5 1 1

4.2 Quality Assessment

Tables 7 and 8 show the confusion matrix for the

algorithm using ATS guidelines relative BEV

threshold and the healthcare professional, for the two

tests performed.

Table 7: Confusion matrix for the hot air tests using the

BEV threshold <5% FVC.

Hot Air Test

BEV < 5%

Predicted

Yes No

Target

Yes 27.3% 47.6%

No 7.1% 17.8%

Table 8: Confusion matrix for the maximal effort tests using

the BEV threshold <5% FVC.

Max Force Test

BEV < 5%

Predicted

Yes No

Target

Yes 36.2% 41.3%

No 18.1% 13.0%

Automatic Quality Assessment of Smart Device Microphone Spirometry

119

At a first glance, the high false negative rates

indicate that the algorithm is too strict with respect to

the healthcare professional. Nevertheless, given that

the only information available to the healthcare

professional to produce an evaluation was the visual

observation of the child, and not an objective

measurement obtained by a spirometer as would

happen on a regular spirometry, it is certainly possible

that there was some mislabelling – specifically by

accepting manoeuvres that otherwise would not have

been accepted.

Given the already explained influence of age, sex

and height on the FVC, it is worth noting that the

presented results are based off a sample database that

is heavily biased towards the higher end of the age

spectrum. Given the positive correlation between age

and height to expected FVC values, this means that it

would be reasonable to expect our results to be worse

with a more balanced database, for any static BEV

threshold.

Table 9: Confusion matrix for the hot air tests using the

BEV threshold <12.5% FVC.

Hot Air Test

BEV < 12.5%

Predicted

Yes No

Target

Yes 64.2% 10.7%

No 22.6% 2.4%

Table 10: Confusion matrix for the maximal effort tests

using the BEV threshold <5% FVC.

Max Force Test

BEV < 12.5%

Predicted

Yes

Target

Yes 68.8% 8.7%

o 18.1% 4.3%

As a test, we repeated the analysis for the BEV

threshold suggested by (Aurora et al., 2004), that

coincided with the estimated male BEV threshold for

5-year olds on Table 3, 12.5% (Tables 9 and 10).

While it did improve the true positives and false

negatives, it came at a cost of true negatives, and false

positives in the case of the hot air test. However, it is

important to note that the hot air test is of lesser

importance compared to the maximal effort test, not

only for the scope of this paper, but for Ar.cade’s

scope: the players are expected to have been coached

to perform maximal effort manoeuvres, whereas they

were not in the hot air test. This is due to specific

instructions and coaching for maximal force on

exhale being only given on the maximal effort test,

and not before. In terms of gameplay, a false negative

would mean asking the child to repeat the manoeuvre,

while a false positive would promote poor form.

Taken to the extreme, both of these would lead to the

failure of the “Ar.cade" project, even if due to

different reasons: high difficulty causing frustration

and a loss of interest in the game, or useless gathered

results from a medical standpoint. Therefore, a hybrid

approach might be worth exploring: starting out with

a lower BEV threshold, but raising the threshold after

several failed attempts.

The Tables 11 and 12 show the confusion

matrixes for the age adjusted BEV thresholds. When

compared to the results of the static BEV thresholds,

they are somewhere in between them. The differences

between these are cleared when looking at Tables 13

and 14, which present the F

1 score and accuracy

measurements for the 2 presented static BEV

thresholds along with the age adjusted approach. As

expected, the 5% threshold gives the worst results.

The 12.5% threshold appears to be best, and while

previously mentioned research does point to this

threshold as appropriate for the younger children in

our target group, it is important to note this: F

1 score

and accuracy can provide falsely inflated results in

unbalanced classes, such as the ones presented in our

confusion matrices.

Table 11: Confusion matrix for the hot air tests using the

age adjusted BEV thresholds.

Hot Air Test

Age adjusted BEV

Predicted

Yes No

Target

Yes

50.0% 25.0%

15.5% 9.5%

Table 12: Confusion matrix for the maximal force tests

using the age adjusted BEV thresholds.

Max Force Test

Age adjusted BEV

Predicted

Yes No

Target

Yes 55.1% 22.4%

No 14.5% 8.0%

Table 13: F

scores, accuracy measurements for different

BEV thresholds in the hot air test.

BEV F

score Accuracy

5% 50.0% 45.2%

12.5% 79.4% 66.6%

Age adjusted 71.1% 59.5%

Table 14: F

scores, accuracy measurements for different

BEV thresholds in the maximal force test.

BEV F

score Accurac

5% 58.8% 49.2%

12.5% 83.7% 73.2%

Age adjusted 74.9% 63.0%

Aside from this, according to (Koopman et al.,

SPCS 2018 - International Conference on Signal Processing and Communication Systems

120

2011) the direct correlation between FVC and age is

small compared to FVC’s correlation with height, as

can be seen on Figure 9. Given this notable

discrepancy, and the fact that we assumed the average

height for each age shows another significant

limitation in our work, as the height for a child varies

around 20% from the 3

percentile to the 97

, at any

given age in our target group according to WHO

standards.

Figure 9: Plot illustrating the importance of the predictors

for FVC estimation as they vary with age or height. The red

and blue lines represent the impact of children’s height,

measured in cm, for females and males respectively (target

group bounded between 100 and 150cm, using WHO’s

height-to-age standards). The green line represents the

impact of age, measured in months, for both females and

males (target group between 60 and 120 months of age).

Table 15: Number of children capable of performing at least

one maximal force test with acceptable BEV for different

thresholds, in absolute and relative units respectively.

Criterion

Children with at least

one positive

classification

Healthcare

professional

54 (98%)

5% FVC 46 (84%)

12.5% FVC 55 (100%)

e ad

usted 52 (95%)

Despite the limitations described above, it is

important to note. On Table 15, we can see the

number of children that managed at least one positive

detection with the current algorithm, set at different

thresholds, considering the maximal force tests which

are more relevant to us as already explained. Even for

our worst performing criterion, we obtained

reasonably similar results to (Tomalak et al., 2008),

where 80.4% of 117 children between the ages of 4

and 10 years old were able to pass the ATS standard

for BEV acceptability, using clinical spirometers.

This shows that while much work is still needed, for

a first approach on the unexplored field of

microphone spirometry automatic quality assessment

(at least to the best of our knowledge), the results look

promising.

4.3 Limitations and Future Work

Given the source of children for this study, only 7.2%

of them had asthma. It is important to have a larger

representation of these cases to establish how much

can we extrapolate from studies in healthy children,

and what are the specific challenges present in

asthmatic children.

The fact that our ground truth was established by

a single healthcare professional may have introduced

a bias in our database, and to reduce this risk further

data collection events should be performed with

different healthcare professionals, with them cross-

evaluating the same manoeuvre.

As was already mentioned, the database was

heavily biased in terms of age distribution. Therefore,

in further data collection events, there should be an

increased focus on gathering audio samples from

children under 7 years of age. This is especially

important to further test the validity of the age

adjusted BEV approach, to evaluate whether it has

any merit to it.

5 CONCLUSIONS

The automatic evaluation of the FEM through

BEV estimated from microphone spirometry allowed

the assessment of the manoeuvre’s quality, with

respect to the start of exhalation.

Using the alternative less strict BEV thresholds of

12.5% and the Age Adjusted version, the quality was

correctly assessed for over 70% of the manoeuvres.

At least one acceptable manoeuvre was achieved for

96% of the children. Even using the stricter criteria of

5% of FVC it was possible to ensure at least one

acceptable manoeuvre for 69%, which is slightly

lower with the reported spirometry quality ratio in the

literature for this age group

.

While this leads us to conclude that our results are

acceptable, at the same time we recognize the need of

improvements for an automated system like this to

become feasible in a real-world application.

Automatic Quality Assessment of Smart Device Microphone Spirometry

121

ACKNOWLEDGEMENTS

We would like to thank the Externato das Escravas do

Sagrado Coração de Jesus for their hospitality and

cooperation, along with the parents of the 42 children

for allowing them to participate in this study, enabling

us to further the work in microphone spirometry.

The authors of this paper thank Susana Fonseca,

R&D director of MEDIDA, for her continued

support.

C. Jácome and R. Amaral have a post-doctoral

(SFRH/BPD/115169/2016) and a PhD grant

(PD/BD/113659/2015), respectively, funded by

Fundação para a Ciência e a Tecnologia (FCT),

Portugal.

R. Almeida, R. Guedes and J. P. Teixeira are part

of NANOStima - Project NORTE-01-0145-FEDER-

000016 (NanoSTIMA), financed by the

NorthPortugal Regional Operational Programme

(NORTE 2020), under the PORTUGAL 2020

Partnership Agreement, and through the European

Regional Development Fund (ERDF).

REFERENCES

Aurora, P., Stocks, J., Oliver, C., Saunders, C., Castle, R.,

Chaziparasidis, G., Bush, A., 2004. Quality Control for

Spirometry in Preschool Children with and without

Lung Disease. Am. J. Respir. Crit. Care Med. 169,

1152–1159. https://doi.org/10.1164/rccm.200310-

1453OC

C. Larson, E., Goel, M., Boriello, G., Heltshe, S.,

Rosenfeld, M., N. Patel, S., 2012. SpiroSmart: Using a

microphone to measure lung function on a mobile

phone. UbiComp12 - Proc. 2012 ACM Conf.

Ubiquitous Comput. https://doi.org/10.1145/2370216.

2370261

de Onis, M., Onyango, A.W., Borghi, E., Siyam, A.,

Nishida, C., Siekmann, J., 2007. Development of a

WHO growth reference for school-aged children and

adolescents. Bull. World Health Organ. 85, 660–667.

Koopman, M., Zanen, P., Kruitwagen, C.L.J.J., van der Ent,

C.K., Arets, H.G.M., 2011. Reference values for

paediatric pulmonary function testing: The Utrecht

dataset. Respir. Med. 105, 15–23.

https://doi.org/10.1016/j.rmed.2010.07.020

Liu, X., 2013. mCOPD: Mobile Phone Based Lung

Function Diagnosis and Exercise System for COPD.

UCLA.

Luo, A.Z., Whitmire, E., Stout, J.W., Martenson, D., Patel,

S., 2017. Automatic characterization of user errors in

spirometry, in: 2017 39th Annual International

Conference of the IEEE Engineering in Medicine and

Biology Society (EMBC). Presented at the 2017 39th

Annual International Conference of the IEEE

Engineering in Medicine and Biology Society (EMBC),

pp. 4239–4242. https://doi.org/10.1109/EMBC.2017.

8037792

Melia, U., Burgos, F., Vallverdú, M., Velickovski, F.,

Lluch-Ariet, M., Roca, J., Caminal, P., 2014. Algorithm

for Automatic Forced Spirometry Quality Assessment:

Technological Developments. PLOS ONE 9, e116238.

https://doi.org/10.1371/journal.pone.0116238

Miller, M.R., Hankinson, J., Brusasco, V., Burgos, F.,

Casaburi, R., Coates, A., Crapo, R., Enright, P.,

Grinten, C.P.M. van der, Gustafsson, P., Jensen, R.,

Johnson, D.C., MacIntyre, N., McKay, R., Navajas, D.,

Pedersen, O.F., Pellegrino, R., Viegi, G., Wanger, J.,

2005. Standardisation of spirometry. Eur. Respir. J. 26,

319–338.

https://doi.org/10.1183/09031936.05.00034805

Moger, A., Holton, K., Hill, S., Kearney, M., Winter, R.,

2013. Quality Assured Diagnostic Spirometry(QADS)

- Performance And Competence, in: B45. CHRONIC

OBSTRUCTIVE PULMONARY DISEASE:

DIAGNOSIS AND EVALUATION, American

Thoracic Society International Conference Abstracts.

American Thoracic Society, pp. A2836–A2836.

https://doi.org/10.1164/ajrccm-

conference.2013.187.1_MeetingAbstracts.A2836

Pierce, R., 2005. Spirometry: an essential clinical

measurement. Aust. Fam. Physician 34, 535–539.

Stein, B. van, 2013. A Mobile Smart Care platform Home

spirometry by using the smartphone microphone.

Teixeira, J.F., Teixeira, L.F., Fonseca, J., Jacinto, T., 2015.

Automatic Analysis of Lung Function Based on

Smartphone Recordings, in: Biomedical Engineering

Systems and Technologies, Communications in

Computer and Information Science. Presented at the

International Joint Conference on Biomedical

Engineering Systems and Technologies, Springer,

Cham, pp. 390–402. https://doi.org/10.1007/978-3-

319-27707-3_24

Tomalak, W., Radliński, J., Latawiec, W., 2008. Quality of

spirometric measurements in children younger than 10

years of age in the light of the recommendations.

Pneumonol. Alergol. Pol. 76, 421–425.

Walters, J.A., Walters, E.H., Nelson, M., Robinson, A.,

Scott, J., Turner, P., Wood-Baker, R., 2011. Factors

associated with misdiagnosis of COPD in primary care.

Prim. Care Respir. J. J. Gen. Pract. Airw. Group 20,

396–402. https://doi.org/10.4104/pcrj.2011.00039

WHO | Height-for-age (5-19 years) [WWW Document],

n.d.. WHO. URL http://www.who.int/growthref/

who2007_height_for_age/en/ (accessed 4.3.18).

Zubaydi, F.K., 2016. A mobile Based Platform for

Monitoring Respiratory Diseases (Thesis).

SPCS 2018 - International Conference on Signal Processing and Communication Systems

122