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In recent years, several studies have shown the relationship between snoring and obstructive sleep apnea syndrome (OSAS). Besides time domain analysis of snoring signal, the spectral features and shapes of snores can be used to discriminate simple snorers and OSAS patients. In this study, we propose a method to classify simple snorers and OSAS patients based on spectral envelope estimation of snoring signals. The formant frequencies and corresponding bandwidths are computed for both group, and the variation and consistency of the formant distributions are computed. A total of 1400 snoring episodes from 7 simple snorer and 7 OSAS patients were analyzed. Significant differences are found in the formant frequencies of both groups. The results are discussed from the view point of patho-physiological aspect.
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SPECTRAL ENVELOPE ANALYSIS OF SNORING SIGNALS
Mustafa Çavuşoğlu1, Mustafa Kamaşak2, Tolga Çiloğlu3,Yeşim Serinağaoğlu3,Osman Eroğul4
1 Max Planck Instıtute for Biological Cybernetics, High Field MR Center, 72076, Tuebingen,Germany
2 İstanbul Technical University, Computer Engineering Department, 34469, İstanbul,Turkey
3 Middle East Technical University, Electrical and Electronics Engineering Department, 06530, Ankara
4 Gülhane Military Medical Hospital, Biomedical and Clinical Engineering Center 06018, Ankara
ABSTRACT
In recent years, several studies have shown the
relationship between snoring and obstructive sleep apnea
syndrome (OSAS). Besides time domain analysis of
snoring signal, the spectral features and shapes of snores
can be used to discriminate simple snorers and OSAS
patients. In this study, we propose a method to classify
simple snorers and OSAS patients based on spectral
envelope estimation of snoring signals. The formant
frequencies and corresponding bandwidths are computed
for both group, and the variation and consistency of the
formant distributions are computed. A total of 1400
snoring episodes from 7 simple snorer and 7 OSAS
patients were analyzed. Significant differences are found
in the formant frequencies of both groups. The results are
discussed from the view point of patho-physiological
aspect.
KEY WORDS
Spectral analysis, formant frequency, OSAS, snore
1. Introduction
Snores are breathing sounds produced during the sleep.
Breathing triggers mechanical oscillations of the tissues
such as soft palate or tongue around the constriction due
to the relaxation of the tissues in the upper airway. The
American Sleep Disorders Association (ASDA) defined
snoring as “Loud upper airways breathing, without apnea
or hypoventilation, caused by vibrations of the pharyngeal
tissues” [1]. In the last 15 years, snoring problem has
entered the realm of clinical medicine. It is a prevalent
symptom, and about 50% of the adult population snores
frequently [2, 3]. It has been reported as a risk factor for
the development of diseases such as ischemic brain
infraction, systemic arterial hypertension, and coronary
artery disease and sleep disturbance [4]. Sleep apnea is
the cessation of airflow to the lungs during sleep for 10
sec. or more [5, 6]. It may result from either upper airway
collapse (obstructive sleep apnea) or lack of neural input
from the central nervous system to the diaphragm (central
sleep apnea) [6]. OSA is the more common form of sleep
apnea. Common symptoms of OSA are fatigue, daytime
sleepiness, coroner problems, and systemic hypertension
[5, 6]. It is usually associated with loud, heavy snoring
[6]. In recent years, several studies have shown the
relationship between snoring and obstructive sleep apnea
syndrome (OSAS). These studies have shown that the
frequency domain analyses of snores can be used to
discriminate simple snorers and OSAS patients [7].
Analyzing snoring sounds is very useful and promising
tool in sleep related breathing disorders. Time domain
parameters of snoring sounds such as the snore rate
(number of snoring episodes per hour), intensity of the
snoring and the regularity of the snoring episodes showed
that simple snorers and OSAS patients can be classified
by using snoring sound analysis instead of
polysomnographic studies which requires spending the
whole night in the hospital.
Hill et. al. [8] proposed the use of acoustic crest factor of
snoring episodes (the ratio of peak amplitude to root mean
square value) to distinguish palatal from nonpalatal snore.
Abeyratne et. al. [9] proposed the measure “intra-snore-
pitch-jump” to diagnose OSAS. Sola-Soler et. al. [10]
studied the differences in spectral envelopes of simple and
OSAS snores and suggested the standard deviation of
formant frequencies as a criterion for distinction among
simple snores and OSAS snores. Miyazaki et. al. [11]
found significant differences in fundamental frequency
depending on the snore production site. Spence et. al.
found a significant correlation between median frequency
of snore and apnea/hypopnea index (AHI). Cavusoglu et.
al. [12] developed an interface system based on time
domain characteristics to differentiate the simple snorers
and OSAS patients as well as to determine the efficiency
of applied snoring treatment objectively.
Most of the studies are focused on the fundamental
frequency of the snores but spectral envelope estimation
of the snoring signals also contains information about the
pressure pulses and anatomical filtering involved in snore
production [10]. Thus, the spectral analysis values, the
“formant type” structure and the shape of the spectrum
help to distinguish simple snorers and OSAS patients
[13]. Polysomnography (PSG), performed over a full
night sleep, is presently the gold standard for diagnosis of
sleep apnea [14, 15]. Since PSG analysis requires
spending the night at a hospital with many measurement
electreodes connected to the body, it is time consuming
601-179
473
and uncomfortable for the patient. The motivation of this
study was to use spectral envelope analysis of snoring
signals to differentiate the simple snorers and OSAS
patients as an alternative to polysomnography. This
analysis may also provide information in localization of
the snoring site such as palate or tongue.
Our approach in modeling snoring sounds is motivated by
the well-known source-filter modeling framework of
speech sounds. According to the source filter-model,
speech is produced by the excitation of the vocal tract (the
acoustic filter) by an acoustic pressure wave due to the
motion of vocal folds or due to a constriction created by
the speech articulators. The resulting excitation signal has
a mixed periodic and noise content in varying proportions
for different phonemes. Source-filter model is also very
suitable for modeling snoring sounds. The mechanism of
snoring is similar to that of voiced speech. The main
difference arises at the source part. The vibration of vocal
folds is replaced by the vibration of relaxed pharyngeal
tissues and tongue. Snorer’s relaxed tissues tend to come
into contact when there is no airflow for example, around
the intake-outlet turnover instants of breathing. Following
the start of an intake or outlet, the pressure difference
across the contact causes the separation of contacting
tissues. Once the tissues are separated, the pressure
difference drops and the tissues come into contact again
and the cycle continues in this manner. The location of
the contact in the pharynx, the type of contacting tissues,
the physical properties of the tissues depending on age
and/or fat content affects the dynamics of this vibration.
The excitation of the vocal and nasal tracts as a
consequence of this vibration yields the lip and nose
radiation of the snoring sound.
The formant frequencies are the resonant frequencies of
the vocal tract filter its range depends on the shape of the
resonant cavities. Since the source of the snoring sound
differs in simple snorers and OSAS patients, it motivates
to the investigation of how the formant frequencies and
their bandwidths changes in simple snorers and OSAS
patients.
2. Materials and Methods
2.1 Recording Set up and Database
A Sennhiser ME 64 condenser microphone with a 40–
20000 Hz ± 2.5 dB frequency response was used for
recording sounds. This microphone has a cardioid pattern
which helps to suppress some of the echoes from the
environment. It was placed 15 cm over the patient’s head
during sleep. The signal was fed via a BNC cable to the
Edirol UA-1000 model multi-channel data acquisition
system connected to a personal computer via universal
serial bus. The computer was placed outside the sleeping
room not to disturb the sleeping patients. The acquired
signal was digitized at a sampling frequency of 16 KHz
with 16 bit resolution.
A total of 1400 snoring episodes were analyzed obtained
from 7 simple snorers and 7 OSAS patients equally.
Snoring episodes were selected by an automatic detector
[15] and validated by a medical doctor. Apnea/Hyponea
Index (AHI) of simple snorers and OSAS patients are
4.29 and 39.21 respectively.
2.2 The Formant Type Spectrum Structure of
Snoring Sounds
Unlike the highly non-stationary character of speech
because of the continuous motion of the articulators (jaw,
lips, tongue, and velum) along the vocal tract, snoring
signal bears non-stationarity at a much lower level.
Articulators keep their positions unchanged most of the
time during snoring. Spectral envelope estimations of
snoring signals are shown in Figure 1. The spurious peaks
with very low amplitude are rejected by 3dB threshold.
Inspecting the snoring intervals clearly indicates the
highly stationary behavior of the signal. This is much like
the production of sustained vowels.
Figure 1 shows that the greater part of the energy content
is below 2500 Hz and the main components lie in the low
frequency range, at about 150 Hz. The envelope peaks
also at the frequencies about 500, 1800 Hz, and 2200 Hz.
The spectrum shows a fundamental frequency and a
“formants type” structure.
00.5 11.5 22.5 33.5 44.5 5
-140
-130
-120
-110
-100
-90
-80
-70
-60
-50
-40
frequency (kHz)
Power (db)
Figure 1. Spectral envelope estimations of snoring signals
In the context of speech processing, formants are the
frequencies around which the peaks of the spectral
envelope are observed. They are the resonant frequencies
of the vocal tract filter. Each formant is characterized by
frequency, bandwidth and amplitude level, and its range
depends on the shape of the resonant cavities. The
different conditions in which the subjects and the patients
who snore can affect the formants range in the frequency
spectrum. The formants of a snoring signal including 5
snoring episodes is shown in Figure 2.
474
Figure 2. The formants of a snoring signal including 5
snoring episodes
Automatic spectral envelope estimation of the snores in
the database shows that formants stay almost unchanged
within and across the snoring intervals and the formant
frequencies of snoring episodes have almost common
formants in some frequency range.
2.3 Formant Frequency Analysis
We compute the formant frequencies based on linear
prediction coefficients (LPC) of the snoring episodes.
These coefficients are computed using auto-correlation
method with Levinson-Durbin recursions.
=
+=
p
iiinxanenx
1
)()()( (1)
In equation (1), represents the snoring signal and p
is the degree of linear prediction. In addition, and
corresponds to the linear prediction coefficients
and prediction error respectively. We can express the
signal in frequency domain as follows:
)(nx
i
a
)(ne
=
+=
p
i
i
izXzazEzX
1
)()()( (2)
we can modify (2) in order to obtain the transfer function
that is expressed by (3).
)(
1
1
1
)(
)(
)(
1
zA
za
zE zX
zH p
i
i
i
=
==
=
(3)
The sound signal is generated by stimulating
the , location of the production (uvula, soft palate,
etc), with .The transfer function, is
determined by minimizing the linear prediction
coefficients.
)(zX
)
)(zH
(zE )(zH
The formant frequencies are computed by finding the
roots of the linear prediction polynomial obtained from
LPC analysis. The prediction polynomial can be
expressed as follows:
∑∑
==
==
p
k
p
kkkk zczczazA
1
2/
1
1*11 )1)(1(1)( (4)
where p is the order of the LPC filter. Resonance
frequencies are found using only the complex roots of
prediction polynomial. The complex root of the
is expressed in equation (5).
th
k
)(zA
k
jw
kk ecc = (5)
The formant frequency and the corresponding
bandwidth can be written as:
th
k
s
k
kT
w
f
π
2
= (6)
s
k
kT
c
b
π
ln
= (7)
where is the sampling period. In this study, the order of
the linear prediction filter is selected as 10.
s
T
3. Results
We investigate the inter- and intra-patient variation of
formant frequencies and their bandwidths for simple
snorers and OSAS patients. To visualize these variations,
we have randomly selected 10 snoring episodes from each
of the 7 simple snorers and 7 OSAS patients and compute
the formant frequencies. Figure 3 and Figure 4 shows the
inter- and intra- patient variation of formant frequencies
in simple snorers and OSAS patients respectively.
Average formant frequencies and corresponding
bandwidths are calculated for the snoring episodes in the
database. Table 1 and Table 2 show the average values of
formant frequencies and corresponding bandwidths
respectively for simple snorers and for OSAS patients.
475
Figure 3. Inter- and intra- patient variation of formant
frequencies in simple snorers
Figure 4. Inter- and intra- patient variation of formant
frequencies in OSAS patients
Table 1
Average values of formant frequencies for Simple snorers
and OSAS patients
Subject F1 F2 F3 F4
Simple
snorer 512.8 1678.2 4326.5 6728.9
OSAS
patient 112.4 1386.6 4203.8 6404.2
Table 2
Average values of formant bandwidths for Simple snorers
and OSAS patients
Subject BW1 BW2 BW3 BW4
Simple
snorer 9.4 18.6 19.8 28.6
OSAS
patient 27.5 64.3 44.6 31.9
4. Discussion and Conclusion
The production mechanism of snoring sound is similar to
that of voiced speech; hence we can apply the well-known
source-filter model to analyze the snoring sounds.
Spectral envelope analysis of snoring signals provides an
efficient way in simple snorer/OSAS classification which
is very important both in the diagnostic and treatment or
uvulo-palatopharyngoplasty (UPPP) type surgery process
of patients. Filtering characteristics of upper airway and
the location of pressure production are strongly related to
the spectral envelope. For example, the shape of resonant
cavities in the upper airway changes the range and value
of formant frequencies [10].
In simple snorers (Figure 3), while the formant
frequencies are consistent in different snoring episodes of
same individual, there may be some shifting in different
patients. The magnitude of these shifting is less in the first
formant frequency than those of other three formant
frequencies. The intra-patient consistency of formant
frequencies is highest in the first formant and lowest in
the second formant. For each patient, the formant
frequencies may vary. However, from figure 2, it can be
seen that the formant variations are correlated among the
patients.
In OSAS patients (Figure 4), there is no considerable
coincidence in formant frequency locations both inter-
and intra patient snoring episodes. The significantly
lower variability of formant distributions in simple
snorers than in OSAS patients can be explained as
follows:
i) The source of the snoring sound (vibrating tissue in
the upper airway) is in a multi-segmental structure in
OSAS patients.
ii) In OSAS patients, upper airway resistance is
relatively lower compared to simple snorers. The
reduction in the upper airway resistance is related to
the site of snoring.
In this study, we propose a method to discriminate the
simple snorers and OSAS patients based on spectral
envelope estimation of snoring signals. The experimental
results are promising and show that the approach given
here can be used not only for source localization of
476
snoring but also for detection of the obstructive sleep
apnea.
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The differentiation of palatal from non-palatal snoring is very important for ENT surgeons trying to determine whether palatal surgery would be curative. At present sleep nasendoscopy is the accepted method. Palatal vibration produces marked modulation of sound loudness at low frequency (below ~100 Hz). We calculate a crest factor for the sound waveform (ratio of peak to root mean square (rms) value in any given epoch), as a measure of the degree of modulation. Free-field snore sounds were recorded from 11 supine adult patients under intravenous sedation (midazolam), using a digital tape recorder. Recordings were transferred to a PC (sampling frequency 11 kHz), and analysed using code written by us. Direct visual confirmation of the site of snoring was gained from simultaneous sleep nasendoscopy, taken as the gold standard. In six patients the dominant site was the soft palate. The non-palatal group (five patients) comprised one epiglottic, two hypopharyngeal and two tongue base snorers. The crest factor was found to be significantly higher for palatal snorers (p<0.01, Student-t or Mann-Whitney tests). Furthermore, palatal could be distinguished from non-palatal snorers on the basis of crest factor alone in all 11 cases, making this a promising non-invasive diagnostic technique.