Optimized Sleep Apnea Detector using UWB Signals

Jesús G. Servín-Aguilar

, Jorge A. Pardiñas-Mir

and Luis Rizo-Dominguez

Department of Electronics, Systems and Informatics, ITESO University, Tlaquepaque, Mexico

Keywords: Sleep Apnea, UWB Technology, Optimization, Parametric Multidimensional Search, eHealth.

Abstract: Sleep apnea is a syndrome defined as the pause of breathing for more than 10 seconds while the patient

remains asleep. Diseases such as strokes, coronary heart attacks or diabetes could be associated with an

untreated sleep apnea. UWB is an alternative technology to help treat this condition. It is a non-invasive

technology, with low power and low radiation. In this article we present a system based on UWB signals to

detect sleep apnea. It is shown that the proposed algorithm correctly detects apnea. Once this is done, an

experimental optimization is performed to find the parameters that produce the best results.

1 INTRODUCTION

The main cause of death in the world is chronic

diseases (WHO, 2014). 70 % of deaths in the world

are diseases like diabetes, heart diseases, strokes, or

cancer (WHO, 2018). Half of these deaths correspond

to people over 70 years old (WHO, 2014). It is

expected that between 2000 and 2050 the population

over 60 years old will increase from 11% to 22% with

respect to the world population (WHO, 2015). This

data indicates that the world population has become

old. If we focus on elderly people, we find that most

of our seniors prefer to live alone in their own homes,

than living with their families or in a care center (Hou,

2009). The risk is that, if they live alone, they may

have an accident caused by a health problem and there

would be no one around to help them. Therefore, the

time it takes a family member or medical assistant to

know that the elderly have had an accident can be too

long and can cause irreversible damage.

Another alarming problem around the world is the

overpopulation of hospitals. A solution to reduce the

number of patients attending hospitals every day is to

design a system to monitor vital signs and send the

information to a remote database. From there, doctors

can review, make a diagnosis, and give or modify the

treatment of a patient via internet. This could be in

fact a remote medical appointment (Hou, 2009). This

situation has represented in the last years a good

https://orcid.org/0000-0002-1908-3171

https://orcid.org/0000-0002-0972-3980

https://orcid.org/0000-0001-9393-5320

opportunity for research work aimed to develop

solutions in this area. In this scope, Wireless Sensor

Networks (WSN) and Wireless Body Area Networks

(WBAN) have been considered to be applied to

monitor patient vital signs constantly like the

patients’ breathing to detect sleep apnea.

The sleep apnea (SA) is a syndrome which affects

at least 6% of the adult population (WHO, 2017). SA

is the breathing pause for 10 seconds or more while

the patient is asleep. Occasionally, a normal person

can have apnea. However, a health damage is

considered when the patient has at least 300 sleep

apneas per night or its duration is around 5 minutes

(Varady, 2003), (AASM, 2017), (Guyton, 2011). SA

is caused by the obstruction of the airways [8] and it

is divided into two types: central sleep apnea (CSA)

and obstructive sleep apnea (OSA). In the CSA, the

nervous central system does not send the impulse

information to the airway muscle and they block the

air conduct in the pharynx. On the other hand, the

OSA is caused by soft tissues of muscle that block the

airway conduct (Varady, 2003), (Guyton, 2011). In

both cases, it could be accompanied by loud snoring

(WHO, 2017). After that, the snore is interrupted by

a long silence. Finally, the brain sends an impulse to

the patient to open the airway or move the body to

continue breathing (Guyton, 2011). A patient with SA

could present different symptoms such as feeling

sleepy or sleep during the day, be forgetful, having

strong headaches, fall asleep while the patient is

Servín-Aguilar, J., Pardiñas-Mir, J. and Rizo-Dominguez, L.

Optimized Sleep Apnea Detector using UWB Signals.

DOI: 10.5220/0007931103610369

In Proceedings of the 16th International Joint Conference on e-Business and Telecommunications (ICETE 2019), pages 361-369

ISBN: 978-989-758-378-0

361

watching TV, working, driving, reading, or waking

up tired in the morning (MedlinePlus, 2017).

Currently, the medical method to diagnose sleep

apnea is performed with a polysomnography (AASM,

2017), (Guyton, 2011), (MedlinePlus, 2017). In this

technique, an oxygen mask is placed on the nose and

mouth of the patient while sleeping. Then, the device

records the patient’s breathing and it detects when the

sleep apnea occurs, as well as its duration. There are

other complementary tests that confirm SA, such as

electrocardiography (ECG), echocardiography,

thyroid test, or arterial blood test (MedlinePlus,

2017). Also, a non-invasive technique to detect sleep

apnea is to measure the patient chest movements. For

example, pressure transducers are placed on the

patients’ bed. When the patient inhales or exhales, the

pressure exerted on the bed changes. When a sleep

apnea occurs, the pressure of the body on the bed is

still for more than 10 seconds (Waters, 2019).

However, the patient must be in a specific position for

the breathing frequency detection. Another technique

is the Doppler radar, which measures the distances

between the radar and the chest of the patient. If

distances don’t change for more than 10 seconds, then

the sleep apnea alert is activated. Unfortunately, this

technique, in some cases, tends to be inaccurate (Lai,

2011). Recent experiments show that sleep apnea can

be detected using the Ultra-Wide Band (UWB)

technology (Fedele, 2015). UWB signals are used to

measure the distance between the target and the

device and the variations caused by the breathing

movement, (Abib, 2014), (Muller, 2015). An

advantage is that the power required to send a UWB

pulse is lower than other technologies (Muller, 2015).

In these papers, the method to detect sleep apnea is

through the detection of the breathing signal of the

patient. It is analyzed, and the sleep apnea is detected.

This process could be optimized in order to reduce the

time consumption of the process.

In this paper, a methodology to detect sleep apnea

is proposed using UWB signals without the need of

first obtaining the breathing signal. The rest of the

paper is divided as follows: Section II shows an

overview of the UWB technology to detect SA in

patients. Section III describes the proposed

methodology to detect sleep apnea. Section IV

presents the optimization of the proposal. Finally,

section V remarks some conclusions.

2 UWB TECHNOLOGY AND

BREATHING SIGNAL

The Federal Communications Commission (FCC) of

the United States of America (USA) defines UWB

technology as that employing devices that transmit

very short pulses that result in very wide transmission

bandwidths (Waters, 2009), (Pardiñas, 2017).

Typically, the largest pulse length considered as a

UWB pulse is on the order of nanoseconds. The

received energy signal is spread from close to dc to a

few GHz (Pardiñas, 2017). Another advantage of this

technology is that multipath effects can be

diminished, and it can penetrate through materials

such as walls, doors, and windows (Pardiñas, 2017).

When a signal is transmitted in an ideal

environment from point A to point B and is reflected

back to point A in a direct path without additional

reflections, it is called the direct path. The total

traveling time is known as time of flight T

. In a real

environment, the signal that arrives at point B is

composed of the direct path plus additional signals

reflected from different objects, as seen in Figure 1,

traveling longer paths and lasting for more than T

⁄2

seconds.

Figure 1: Received signal constructed from reflections of

the original signal off of scatterers.

Figure 1 shows the way that the received signal,

(t), is constructed from different reflected signals

or paths d

(t), d

(t), etc. In the case of a

transceiver acting as a radar, the signals at point B are

reflected and redirected to point A, where they are

collected. This kind of UWB radar can be used to

obtain a breathing signal of a person, as shown in

Figure 2.

The transceiver targets the person and sends one

pulse, recording the reflected signal, called a

realization, for T

seconds, ensuring that all paths

have arrived. Doing this very fast and as many times

as needed, a signal representing the breathing can be

obtained. Figure 3 shows the waveform of a UWB

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362

Figure 2: UWB system targeting a patient.

pulse transmitted and Figure 4 shows the received

signal, called realization, of around 52 nanoseconds

long.

Figure 3: The waveform of an UWB pulse produced by the

transmitter.

Figure 4: Waveform of a realization collected from a range

of 15.6 meters using the UWB device.

The time T

is dependent on the longest distance

to the object on which the signal is reflected. Hence,

corresponds to the time of flight of the longest

signal path, so the maximal distance d

max

that the

transceiver can detect is half of that time:

















(1)

where V

is the velocity of propagation. For example,

a typical value of T

for the transceiver used during

the experiments presented in this document is 54 ns,

so the maximum distance at which a target can be

from the transmitter for being recorded is around 8

meters. As stated before, in order to detect the cycle

of the breathing signal of a person, it is necessary to

analyze the signals corresponding to several cycles of

breathing, which means a set of many realizations.

The aim is to identify, in those signals, the

information associated with the x axis value to the

time position that indicates where the target is

positioned. In order to standardize the identification

concept, this method will be called the target position.

The analysis of the target position is made through

all realizations. The normal breathing frequency of a

patient is approximately 12 times per minute, which

means that the patient inhales and exhales every 5

seconds. However, the breathing frequency could

change according to the patient and his status, where

the minimum breathing frequency to live for a short

time is 2 times per minute and the maximum is 40

times per minute (Guyton, 2011).

According to the sampling theorem, the minimum

sampling frequency must be at least twice the

frequency of the signal. In this case, the breathing

frequency is 0.2 Hz and the sampling frequency is

normally set to 8 Hz, which is the repetition

transmission frequency of the set of pulses produced

by the UWB system. One set usually consists of 20

realizations in order to detect one respiratory cycle of

around 5 seconds. Figure 5 illustrates three

realizations from a set, where the time between them

is 0.125 s.

Figure 5: Three realizations from a set of signals from the

UWB system.

Optimized Sleep Apnea Detector using UWB Signals

363

3 APNEA DETECTOR

PROPOSAL

The method presented in this section is based on a

UWB transceiver acquiring the reflected signals from

a human body.

3.1 Detection Methodology

Instead of trying to identify the breathing cycle, the

received signals (realizations) are processed to detect

signal variations corresponding to a change of

breathing. This is made by computing its variance and

comparing the values from one realization to the next

one, no matter at what point in the signal this variation

occurs. While a person is breathing, the variance of

realizations does not change suddenly, but when the

person stops breathing, the variance has a big change,

signalling that apnea has occurred if it lasts for more

than 10 seconds. Note that the breathing frequency of

a patient is around 0.2 Hz or a breathing period of 5

seconds. The flowchart of this method is shown in

Figure 6.

Figure 6: Flowchart of sleep apnea detection using the

variance of realizations.

The variance of a signal, as used in this work,

provides a measure of the amplitude dispersion of the

signal with respect to its average in a determinate time

(Leon-Garcia, 2008), and is given by:













 



































(2)

where m

(t) is the mean function of the random

process X(t), and f

x(t)

(x) is the probability density

function (PDF) of X(t).

For signal processing, a set Y

(n) consisting of a

certain number of realizations, WL, is grouped into a

“window” and its variance V

is calculated. A new

window Y

(n) is analyzed by grouping other WL

realizations located WS realizations apart from the

start of the previous window: Y

(n) = Y

(n+WS). Its

variance is V

. WS corresponds to the number of

realizations or window step where the new window is

defined. Construction of the grouping of realizations

and window step is illustrated in Figure 7.

Figure 7: Construction of realization windows and

definition of window step: a) Three consecutive

realizations. b) Windows shifting.

This procedure, computing the variance of shifted

windows, is executed continuously. The result is a

signal containing the amplitude of variances, as it is

shown in Figure 8.

In this figure, note that there is a time interval

when breathing ceased, and the amplitudes of the

variances decreased abruptly. A comparison between

consecutive variance amplitudes is also continuously

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364

Figure 8: Elements of variance computation by windows.

computed, searching for this abrupt change by

calculating:

 









(3)

When the relation r between them is greater than

a threshold value ThV, this point of time is considered

as a potential start of apnea. Then, if the relation

between subsequent variance amplitudes and the one

at the start point is kept greater than the threshold

value for more than 10 [Guyton-11], [Servin-Aguilar-

18], apnea is declared. Computation of the relation

factor r for a signal lasting 60 seconds and apnea at

25 seconds is shown in Figure 9.

Figure 9: Relation of variance amplitudes with a sleep

apnea at 25 seconds.

Finally, when the relation r is lower than the

threshold, then the patient is breathing again. This

process is repeated until the complete signal is

analyzed.

3.2 Detection Results

In order to evaluate the performance of the algorithm,

we analysed signals corresponding to two

environments: a laboratory and a dormitory. We used

a UWB monostatic radar module (MRM) model 410

from PulsOn which works in a frequency band

between 3.1 and 4.8 GHz, transposing the UWB pulse

to a center frequency of 4.3 GHz. This device has two

antennas (Tx and Rx) in the same package. The UWB

transmitter sends a pulse of 5.6 nano-seconds every

0.125 seconds targeting directly the chest of a patient.

In a first experiment, the laboratory environment

is analysed. The distance between the patient and the

UWB transceiver is varied from 20 cm to 100 cm.

Some results are presented in Figure 10 for values of

comparison factor r.

Figure 10: Relation of variance amplitude for signals at

different distances between the UWB device and the

patient: a) 20 cm and b) 80 cm.

Optimized Sleep Apnea Detector using UWB Signals

365

Here, a threshold ThV of 5 is chosen. We correctly

detected apnea at distances of 20 cm, 60 cm, 80 cm,

and 100 cm. For distances greater than 100 cm, the

signal is not suitable for processing.

When the threshold value ThV is changed to 2,

sleep apnea is wrongly detected at 50 seconds. If

distance between the UWB device and the patient is

grater, then the threshold ThV must be lower to detect

sleep apneas, because the amplitude of variances are

lower.

In a second experiment, the signal processed

corresponded to a patient wearing two different

clothes: a shirt and a jacket. In order to illustrate the

results, we present in Figure 11 the relation of

variance amplitude at a distance of 60 cm from the

patient.

Figure 11: Relation of variance amplitudes for signals in

two scenarios: a) the patient is wearing a shirt, b) the patient

is wearing a jacket.

When the patient wears a shirt, a sleep apnea

occurring at 23 seconds is correctly detected using a

ThV equal to 10, while when the patient wears a

jacket, a sleep apnea at 42 seconds is correctly

detected with a threshold ThV equal to 3. The range

of values is different for each case, so the amplitudes

must be normalized to achieve a reliable threshold

regardless of, in this case, the type of clothing that the

patient uses.

In a third experiment, the signal to process is

acquired in a dormitory, where the patient is laying

down on his side in a bed. The UWB device is

directed toward to the chest of the patient. Two

scenarios are considered: the patient wearing a shirt

and the patient covered with a thick blanket. In this

case, an apnea present at 45 seconds for the patient

wearing a shirt is detected correctly, while for the

patient with a thick blanket, an apnea present at 47

seconds is correctly detected but another non-existent

is detected at 75 seconds. Once the proof of concept

of the method has been carried out, it is necessary to

optimize it to detect apnea with greater precision.

4 DETECTION OPTIMIZATION

In order to find the conditions with the best

performance of the proposed apnea detection method,

the main parameters involved in the computation are

changed in a series of executions of the algorithm.

The targeted parameters are the window length, WL,

the shift or step between consecutive windows, WS,

and the threshold value, ThV.

4.1 Optimization Methodology

The experiments, carried out with a UWB signal

corresponding to the chest movement of the patient,

have two main objectives. First, is to evaluate the

parameters that produce the best accuracy of the

apnea detection method. Second, is to identify the

parameters with shortest processing time.

The proposed optimization methodology

essentially consists of a parametric multidimensional

search based on actual physical measurements. Each

execution of the searching process begins with the

values of two parameters fixed, WL and WS, and the

threshold value varied. Once the whole process of

detection over the signal is carried out, a new

execution is run by fixing WL and WS to new values

and then varying ThV. The executions are repeated by

modifying the parameters until their ranges of

variation are covered. For each execution, the values

of the parameters, the number of detected apneas, and

the processing time are saved.

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366

4.2 Optimization Results

The set of experiments are performed over five

signals presenting different characteristics. Each

signal corresponds to a series of around 750

realizations, lasting 94 seconds in total. As a

reference for the experiments, the average breathing

frequency of a patient is considered to be 0.2 Hz or 5

seconds. Parameter ranges considered are: for the

window length WL, from 4 realizations, which

represents 0.5 seconds, to 128 realizations,

corresponding to 16 seconds; for the window step

WS, from 1 realization to the maximum length of the

window; and for the threshold value ThV, from 1 to

10. This makes a total of 18,544 combinations of

parameters tested for each signal.

Table 1: Number of combinations of parameters that

produced correct detections.

Signal Characteristics

Number of

Correct

Detections

Number of

Apneas

Distance between the

UWB Transceiver and

the Patient (cm)

17,731

1,291







The difference between the signals used in the

experiments are the number of apneas and the

distance between the UWB transceiver and the

patient. Table 1 shows the characteristics of the

signals and the number of combinations of parameters

that presented a correct detection out of the 18,544

possible combinations. We analysed all the results for

each signal, and compared them in order to identify

the ranges of common values of combinations of

parameters that presented correct detections.

The first signal used had a length of 85.63 seconds

with zero apneas and a distance between the UWB

device and the patient of 60 cm. A total of 17,731

combinations correctly detected the absence of

apneas. The highest number of correct results were

produced for combinations of parameters that

comprise the following values: WL from 16 to 40

realizations, WS varying from 10 to 27, and ThV from

1.5 to 10.

The second signal used had a length of 81.3

seconds with one apnea and a distance of 20 cm. The

number of combinations that detected correctly the

apnea is 1,291. Table 2 shows, as an example, a subset

of the parameters with the highest number of correct

detections. It is seen that the range for the WL

parameter is from 8 to 40 realizations, the WS range

is from 8 to 19, and the value of ThV varies from 1 to

10.

The third signal had a length of 113.4 seconds. It

has one sleep apnea and an acquisition distance of 30

cm. The number of correct detections is 2,383

combinations. The subset of parameters with the

highest number of correct detections has a value

range of WL from 16 to 40, the WS range is from 11

to 15, and the ThV maximum range is from 1 to 10.

The fourth analyzed signal had a length of 89.5

seconds, with two apneas, and an acquisition distance

of 30 cm. The total number of combinations

producing correct detections is 1,564 (see Table 1). In

this experiment, the subset of parameter combinations

with the highest number of correct detections

corresponds to WL with a range from 16 to 40

realizations, WS from 9 to 17, and ThV with a

variation from 1.5 to 8.

Finally, the fifth signal had a length of 118.3

seconds, with one apnea, and an acquisition distance

of 80 cm. In this experiment, the total number of

correct detections is obtained from 866 combinations.

The range of WL is from 48 to 72 realizations, WS

varies from 8 to 40 realizations, and the ThV range is

from 1.5 to 6. In this case, it is found that the ranges

of parameters values stepped away from the group of

values obtained in the previous results.

The few coincident results of the fifth signal, in

comparison with the first four signals, show that the

distance has an important effect in the detection

algorithm. In addition, it is also observed that the

number of correct detections decreased by half. In this

case, we cannot define a range of parameters values

good enough for all tested signals. We conclude that

the signal tested at a distance of 80 cm is not suitable

for the proposed algorithm, since it makes the

algorithm unreliable.

Taking into account the results obtained for the

first four signals, we can determine an appropriate set

of reliable values, valid for all four signals. This set

comprises a WL equal to 24, a WS ranging from 12 to

15 realizations and a ThV range from 2 to 4.5. This

means that these selected values can be used for the

detection of apneas within the first four signals. On

the other hand, it can easily be found that, for a fixed

value of parameter WL, the minimum number of

operations to be computed is obtained when the value

of WS is the largest. We can then define that the WS

value to be used for all tested signals is 15. In

summary, we conclude that a set of values that allows

Optimized Sleep Apnea Detector using UWB Signals

367

the detection algorithm to have a good performance

with the least number of calculations, is a WL of 24,

a WS of 15 and a range ThV from 2 to 4.5.

Table 2: Detection results for several parameters using a

signal with one apnea, acquisition distance = 20 cm, and

length = 81.3 seconds.

PARAMETERS

ThV Range



































5 CONCLUSIONS

An algorithm to detect sleep apnea using the relation

of the variance of signals obtained from a UWB

transceiver was presented in this paper. It was

demonstrated that the algorithm detects sleep apnea

with a maximum distance of 100 cm between the

UWB device and the patient. The experiments also

showed that the algorithm is able to detect sleep apnea

considering different scenarios, with some

limitations.

An optimization methodology to improve the

detection was also presented. The experimental

evaluation allowed the identification of the

combinations of parameters that produce the best

results and the smallest number of operations. The

parametric optimization of the algorithm showed that

the best detection results were achieved by using a

WL of 24 realizations, WS of 15 realizations, and a

ThV between 2 and 4.5. It was also found that the

detector was not accurate at a distance of 80 cm

between the UWB device and the patient.

ACKNOWLEDGEMENTS

This work was supported in part by CONACYT

(Consejo Nacional de Ciencia y Tecnología, Mexican

Government) through a scholarship granted to J. G.

Servin-Aguilar.

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