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Biomedical Signal Processing and Control 7 (2012) 256– 264

Contents lists available at SciVerse ScienceDirect

Biomedical Signal Processing and Control

j o ur nal homep a ge: www.elsev ier .com/ locate /bspc

utomated sleep breath disorders detection utilizing patient sound analysis

haralampos Doukasa,d, Theodoros Petsatodisb, Christos Boukisc, Ilias Maglogiannisd,∗

University of the Aegean, Samos, GreeceUniversity of Aalborg, Aalborg, DenmarkAccenture Interactive, GreeceUniversity of Central Greece, Lamia, Greece

r t i c l e i n f o

rticle history:eceived 6 April 2011eceived in revised form 7 February 2012ccepted 3 March 2012vailable online 10 April 2012

a b s t r a c t

Results of clinical studies suggest that there is a relationship between breathing-related sleep disordersand behavioral disorder and health effects. Apnea is considered one of the major sleep disorders withgreat accession in population and significant impact on patient’s health. Symptoms include disruption ofoxygenation, snoring, choking sensations, apneic episodes, poor concentration, memory loss, and daytimesomnolence. Diagnosis of apnea and breath disorders involves monitoring patient’s biosignals and breath

eywords:leep breath disorder detectionleep apnea detectionobile sound processing

during sleep in specialized clinics requiring expensive equipment and technical personnel. This paperdiscusses the design and technical details of an integrated low-cost system capable for preliminary detec-tion of sleep breath disorders at patient’s home utilizing patient sound signals. The paper describes theproposed architecture and the corresponding HW and SW modules, along with a preliminary evaluation.

nore signalsoice activity detection

. Introduction

Sleep is a basic human need in which there is a transienttate of altered consciousness with perceptual disengagement fromne’s environment. Sleep Disordered Breathing describes a groupf disorders characterized by abnormalities of respiratory pat-ern or the quantity of ventilation during sleep. Sleep Disorderedreathing causes disruptions in sleep, yielding waking somnolence,iminished neurocognitive performance, adverse cardiovascularutcomes, insulin resistance and other metabolic dysfunctions. Oneajor sleep disorder is obstructive sleep apnea (OSA), which is a

leep disorder characterized by pauses in breathing during sleep.t can occur due to complete or partial obstruction of the airwayuring sleep. Sleep apnea is also known to cause loud snoring,xyhemoglobin desaturations and frequent arousals. Each apneapisode lasts long enough so that one or more breaths are missed,hile such episodes occur repeatedly throughout sleep. The stan-ard definition of an apneic event includes a minimum of 10 s

nterval between breaths, with either a neurological arousal, alood oxygen desaturation of 3–4% or greater, or both arousal and

esaturation. Clinically significant levels of sleep apnea are defineds five or more episodes per hour of any type of apnea. Therere three distinct forms of sleep apnea: central, obstructive, and

∗ Corresponding author at: Department of Computer Science and Biomedicalnformatics, University of Central Greece, Papasiopoulou 2-4, 35100, Lamia, Greece.el.: +30 22310 66931; fax: +30 22310 66939.

E-mail address: imaglo@ucg.gr (I. Maglogiannis).

746-8094/$ – see front matter © 2012 Elsevier Ltd. All rights reserved.oi:10.1016/j.bspc.2012.03.002

© 2012 Elsevier Ltd. All rights reserved.

complex (i.e., a combination of central and obstructive) constituting0.4%, 84% and 15% of cases respectively [1]. Breathing is interruptedby the lack of respiratory effort in central sleep apnea. Regardlessof type, the individual with sleep apnea is rarely aware of havingdifficulty breathing, even upon awakening.

Symptoms may be present for years (or even decades) with-out identification, during which time the sufferer may becomeconditioned to the daytime sleepiness and fatigue associated withsignificant levels of sleep disturbance. As a result, affected personshave unrestful sleep and excessive daytime sleepiness [1,2]. Thedisorder is also associated with hypertension impotence and emo-tional problems [2]. Because obstructive sleep apnea often occursin obese persons with comorbid conditions, its individual contri-bution to health problems is difficult to discern. The disorder has,however, been linked to angina, nocturnal cardiac arrhythmiasmyocardial infarction stroke and even motor vehicle crashes [3–7].

It is estimated that 20 million Americans are affected by sleepapnea [8,9]. That would represent more than 6.5%, or nearly 1 in 15Americans, making sleep apnea as prevalent as asthma or diabetes.It is also estimated that 85–90% of individuals affected are undi-agnosed and untreated. The Wisconsin Sleep Cohort Study foundthat, among the middle-aged, nine percent of women and 24% ofmen had sleep apnea. 2500 patients in average per year are exam-ined at sleep disorder centers in Greece and almost 80% of them arediagnosed with obstructive sleep apnea [10]. The costs of untreated

sleep apnea reach further than just health issues. It is estimated thatthe average untreated sleep apnea patient’s health care costs $1336more than an individual without sleep apnea. If approximationsare correct, 17 million untreated individuals account for $22,712

C. Doukas et al. / Biomedical Signal Processing and Control 7 (2012) 256– 264 257

eep ap

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fpimacatataenapm[

a

Fig. 1. Patients being assessed for obstructive sl

illion, or almost 23 billion in health care costs [11]. All the aboveacts prove the significance of sleep apnea as a medical problemnd justify the research done in this field.

Polysomnography (PSG, see Fig. 1) is the most common methodor diagnosing obstructive sleep apnea. In this technique, multi-le physiologic parameters are measured while the patient sleeps

n a laboratory. Typical parameters in a sleep study include eyeovement observations (to detect rapid-eye-movement sleep),

n electroencephalogram (to determine arousals from sleep),hest wall monitors (to document respiratory movements), nasalnd oral airflow measurements, an electrocardiogram, an elec-romyogram (to look for limb movements that cause arousals)nd oximetry (to measure oxygen saturation). Apneic events canhen be documented based on chest wall movement with noirflow and oxyhemoglobin desaturation. PSG requires specialquipment of high cost to be installed and specialized person-el to be present, while it offers limited resources for patientssessment (e.g., sleeping beds). In addition, elderly or sickatients often find the PSG equipment too cumbersome, and

ay be reluctant to spend the night in the sleep laboratory

12].Recent studies have shown the potential advantages of using

coustical snore signal properties as a reliable and non-invasive

Fig. 2. Standard polysomnography equipment (requires also a PC for analyzing an

nea (OSA) using polysomnography equipment.

alternative to conventional PSG [13–18] for assessment of patientsthat present both OSA and snoring. This paper presents the con-cept and the technical implementation of MORFEAS; an integratedmobile platform for remotely and automatically diagnosing sleepapnea based on snore analysis of sleep sounds collected at user’ssite. The basic feature of the proposed systems is the capability ofunobtrusive monitoring of patients at home improving this way thereliable detection of sleep disorders in home environments offer-ing comfort and time saving to patients. The utilized methodologyfor sound processing in the MORFEAS system is based on the appli-cation of short discrete Fourier transform (SDFT) and modeling ofsnore signal by a two-sided Gamma distribution. The accuracy ofthe analysis is enhanced using voice activity detection (VAD) tech-niques and features extraction eliminating artifacts of backgroundnoise.

The rest of the paper is structured as follows: Section 2 presentsrelated work in the context of snore analysis and backgroundinformation in this area, while Section 3 describes the proposedarchitecture of the integrated system and the hardware specifica-

tions of the corresponding modules. Sound processing and analysisdetails are presented in Section 4, while Section 5 presents a pre-liminary evaluation of the system. Finally, Section 6 concludes thearticle (Fig. 2).

d managing data) – screenshot of polysomnography data analysis software.

258 C. Doukas et al. / Biomedical Signal Processing and Control 7 (2012) 256– 264

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ig. 3. Microphones established at “Euagelismos Sleep Disorder Clinic” for capturingnd analyzing snore sounds.

. Related work and background information

Additional methods to polysomnography have been proposedn literature for sleep disorders detection or Apnea assessment.

endez et al. present in [19] a method for screening OSA based oningle ECG signals. Signal processing is used for the detection of RRntervals and QRS complexes and then the latter are classified usingeural networks. The accuracy of the method in identifying patientsith OSA is up to 88% according to authors. This method however

equires from the patient to wear specific equipment and there-ore cannot be characterized totally non-invasive. Furthermore the

ethod relies on the existence of a training set of healthy patientsnd patients diagnosed with OSA. EEG arousal is utilized by Sugit al. in [21] for sleep apnea syndrome detection. A sensitivity of6% was achieved in successfully detecting apneic cases using thisethod. Still, the patient needs to be assessed in the Sleep Cliniqueearing some uncomfortable equipment. A body-fixed accelerom-

ter sensor is used in [20] for acquiring vibration sounds duringatients’ sleep. The latter technique is less invasive than PSG buttill can cause discomfort to the patient and results can be eas-ly biased by the sensor placement. In [28] Brunt et al. present aneumatic bio-measurement method installed on patient’s bed foronitoring heartbeat, respiration, snoring and body movements.

he latter achieves maximum patient comfort but still requires spe-ialized hardware, a lot of data preprocessing and training and cannly be used in Sleep Clinics.

Less invasive methods that have been used more extensivelytilize sound processing of breath and snore sounds generated byatients during sleep. The feasibility of sleep apnea characterizationhrough specific snore signal features has been proved in previouslyublished studies [22–25]. Sound data acquisition is performedhrough microphones that are installed near patient’s beds at Sleeplinic. For example the bed of the collaborating in this work “Evage-

ismos Sleep Disorder Clinic” is depicted in Fig. 3. Proper processingor noise removal, and feature extraction for further characteriza-ion of the snore as apneic or benign follow sound capturing in suchound analysis systems. Noise removal can be performed by apply-ng adaptive cancellation filters [24], Linear Predictive Coding forpeech removal [25], Kalman filtering [26] and Wavelet transfor-ation [27]. The extracted features can include the magnitude of

he signal (see Fig. 4) and signal pitch frequencies analysis [25,26].

All the aforementioned works that utilize snore signal process-

ng for OSA characterization are based on microphone installationss already mentioned at Sleep Clinics. The proposed system is basedn a mobile device that can be installed at patient’s home and

Fig. 4. Illustration of the magnitude of the snoring signal.

can transmit snore sound data to the Sleeping Clinics remotely.Maximum patient comfort during sleep is achieved and a greaternumber of patients can be examined, resulting in better andfaster prognosis of the sleep disorders. The following sectionspresent technical details regarding the proposed system archi-tecture, hardware specifications and the proposed snore soundanalysis methodology.

3. Proposed system architecture and setup

In this section we discuss the architecture and the major com-ponents of the MORFEAS system as illustrated in Fig. 5. The coreof the proposed system is the mobile acquisition device, whichis placed next to patient’s bed and records all sounds generatedduring sleep. The hardware consists of a small LCD display for inter-action with user, microphones for capturing sounds, appropriatenetworking modules (with 3G and/or WLAN interfaces), a mem-ory module for storing the acquired sounds and finally the maindigital signal processing (DSP) board. The latter hosts appropriatefirmware that interconnects all the aforementioned componentsand is also responsible for performing a number of sound process-ing steps before the sound data is stored or transmitted to themonitoring unit at the Sleep Laboratory. These steps can includeinitial filtering of the sound (e.g., in order to remove backgroundnoise or start transmission only when snoring sounds are detected,etc.), appropriate coding of the sound (e.g., compression with MP3encoder for optimizing storage and transmission) and encryption ofthe data for privacy protection (e.g., using a symmetric encryptionalgorithm). The DSP board stores the captured data in the storagemedia and transmits the data to the monitoring units using anyavailable network interface. When no transmission is possible, datacan be delivered manually to the medical experts using portablestorage media (e.g., SD memory cards).

At the monitoring unit (i.e., a Sleep Disorder Clinic), appropri-ate software is installed that decodes accordingly the transmittedsound data (i.e., decrypts and decompresses data) for further pro-cessing. Further processing could include the identification andextraction of snoring sounds, the quantification of breath intervalsand sound features extraction that could help the identification ofOSA. The following hardware modules are proposed for creating themobile device that can capture; perform initial processing, code andtransmit/store snore signal data:

• DSP board: The main “heart” of a sound analysis system is the DSPboard. In the described implementation the TMS320C6713 DSPboard by Texas InstrumentsTM is proposed. The latter featuresa 225 MHz processor, embedded JTAG support via USB, high-quality 24-bit stereo codec for audio capturing and processing,four 3.5 mm audio jacks for microphone, line in, speaker and lineout. The specific hardware uses 512 K words of Flash and 16 MBSDRAM, while expansion port connectors for additional plug-inmodules are available. This board with the available Software

Development Kit (SDK) is capable of performing the appropriatesound pre-processing and coding for transmission and storage.

• User interface: A 16 × 2 LCD module may be used as a sampleinterface for displaying basic functionality to user (e.g., device is

C. Doukas et al. / Biomedical Signal Processing and Control 7 (2012) 256– 264 259

orm illustrating major components and processing steps.

•

•

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Fig. 5. Proposed architecture of the MORFEAS platf

on and capturing, snores are detected, data transmission is initi-ated, etc.). The module can be connected to the DSP board throughan analog interface.Microphone modules: A variety of microphone devices can beused. Ideally, two or three omni-directional microphones ofsensitivity around −44 db can be used. The higher number ofmicrophones the better the system is able to suppress back-ground noise more efficiently.Storage module: An SD card module connected to the digitalI/O interface of the DSP board can be utilized for storing theacquired snore signal. Storing data into SD cards facilitates thedata delivery process in case no high-speed wired/wireless net-work is available. The required memory size of the SD card is2 GB.

In the implemented system the Store and Forward (S&F)ethodology is adopted, which is usual in biomedical applications

hat require the transmission of large medical files. More specifi-ally, the sound signals are stored locally in the mobile device andhey are transmitted in batch mode to the responsible clinic at thend of a sleep session. Fixed or wireless network interfaces maye used as a communication medium. In case of wireless transmis-ion a 3G modem in conjunction to a WLAN interface can be usedor transmitting captured sound data. The collected snore soundignals from all patients are stored in a repository that residesn the sleep clinic for further processing. Since the capture snor-ng sounds consists of significant amounts of data more advancedata storage architectures may be exploited, such as Grid or Cloud

nfrastructures.

. Sleep breath disorder detection methods

This section describes the developed algorithms for identify-ng sleep breath disorder episodes during patient’s sleep, utilizing

dvanced sound analysis. The adopted approach is based on thepplication of SDFT and modeling of snore signal by a two-sidedamma distribution. A second approach based on voice activityetection and features extraction is also incorporated in ordero improve accuracy of detection and eliminate artifacts of back-round noise.

Fig. 6. An illustration of snore amplitude distribution in time.

4.1. Initial approach for breath and snore detection

According to the clinical protocol, an apnea incident occurswhen patient breath is interrupted for more than 6 s [2]. In addi-tion, the majority of the patients suffering from OSA, snore duringsleep and present apneic events during the pause of snore events[1,3]. Thus, in order to detect apnea during patient’s sleep fromthe acquired snore signal, snore events have to be identified andquantified.

Based on conducted experiments, when analyzing the capturedsnore sound signal, and applying short-term (i.e., frame lengthsbelow 100 ms) discrete Fourier transform (DFT), the distribution ofreal and imaginary parts of snore coefficients can be modeled by atwo-sided Gamma distribution (T�D) [31,32], as illustrated in Fig. 6.Same properties apply for anechoic voice distribution modeling[33].

The distribution of source snore in the frequency domain isapproximated by a T�D pdf for most of the frequency bins (Fig. 7).Apparently, there is a connection between the distribution of snore

in time domain and in the frequency domain.

Frequencies of background noise are assumed to be Gaussiandistributed. The result of the signal modeling through Laplacian

260 C. Doukas et al. / Biomedical Signal Proces

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ig. 7. Snore amplitude distribution of frequencies. Histograms have been normal-zed to their maximum value per frequency bin.

nd Gaussian distributions is a set of possibilities per sound sam-le for snore events (see Fig. 8, lower part). Preliminary resultsased on snore signals collected at “Evagelismos Sleep Clinic”, Med-

cal School, University of Athens have shown that the system canchieve a detection performance of 90–93% with very low rate ofalse detections.

The whole process results in the automated annotation of snorevents. This way, silent periods between two sequential snoresi.e., the time patient does not breath or exhales) are quantifiednd depending on their duration, an apneic event can be detected.he major advantage of this method is that it can be fully imple-ented on the DSP board and executed on the mobile device. Thisay, recording of snore sounds can be initiated only when snores

re detected, and a preliminary assessment can be provided to thexperts. However, this method has proved to be sensitive on back-round noise similar to speech (e.g., patient not sleeping alone oralking during sleep time). Therefore a more explicit voice activ-ty detection method with classification has been applied in ordero minimize such artifacts. The following subsection presents this

ethod in details.

.2. Application of voice activity detection for detection of breath

isorders

Voice activity detection, also known as speech activity detectionr speech detection, is a technique used in speech processing in

ig. 8. Up: Snoring sample. Detected periods of snoring are marked with line. Down:

istributions of the snore signal.

sing and Control 7 (2012) 256– 264

which the presence or absence of human speech is detected. A VADalgorithm is usually able to distinguish between speech (usuallydistorted by noise) and noise only. The output from a VAD is a signalthat possesses the information whether the input signal containsspeech (e.g., output value 1) or noise only (e.g., output value 0).In speech detection problems, it is often assumed that the speechand the noise signal are stationary within a certain time interval,allowing us this way to apply conventional techniques of signalprocessing. The most common features used by VAD algorithms arerelated to signal energy. A signal-to-noise ratio (SNR) is introducedthat uses an estimation of the noise energy. Further improvementis achieved if the SNR computation is done separately for everyspectral component and if probability densities are introduced forthe spectral energy values. Classification and pattern recognitionfollows using the derived features.

Within the context of breath detection during sleep, the incom-ing audio signal is sampled, quantized and divided into overlappingframes, then each frame is classified as either speech or non-speech.A frame length of 32 ms and a frame shift of 16 ms, which results inan overlap of 50%, have been used. The time samples of the observedsignal are input to a feature extraction module, whose output arefeature vectors which are classified as either speech or non-speechin the classification module.

The following sections present in more details the methodologyfor detecting a snore event using VAD techniques.

4.2.1. Snore hypothesis modelingSnore detection can be achieved by evaluating the ratio of two

distinct hypotheses, snore presence, and snore absence, denotedby H1 and H0 respectively. This approach is analogous to the eval-uation of voice activity detection presented in [33,34]:

H0 : snore absence : X(t) = N(t) (1)

H1 : snore presence : X(t) = S(t) + N(t) (2)

where X(t) = [X0(t), X1(t), . . . , XM−1(t)]T , S(t) = [S0(t), S1(t), . . . ,SM−1(t)]T , N(t) = [N0(t), N1(t), . . . , NM−1(t)]T are the cap-tured snore signal, source snore signal, and noise frequencycomponents.

4.2.2. Probability distribution of noise and the snore signalIn audio processing, it is often assumed that both the real and

the imaginary parts of noise frequency components are zero mean

Likelihood of snore presence on given samples based on Laplacian and Gaussian

C. Doukas et al. / Biomedical Signal Processing and Control 7 (2012) 256– 264 261

Fig. 9. System response emerged for a sequence of snore events. Estimated geometric mean and speech absence decision for the specific input.

ft

f

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fv

ie

H

H�n,k(t + 1) = n�n,k(t) + (1 − n)E

⌊∣∣Nk(t)∣∣2|Xk(t)

⌋

ollowing GD. The pdf of Nk(t) for the case of noise with k denotinghe frequency bin is given by

Gn (Nk(t)) = 1√

2��2n,k

e−(Nk(t)2)/(2�2

n,k)

(3)

here �2n,k

is slowly varying with time variance factor of the Gaus-ian assumed distributed noise for the kth frequency component.he imaginary part follows a similar distribution.

As shown before in previous figures, it can be assumed that bothhe real and the imaginary parts of the frequency distribution ofaptured snore signal are better modeled using a T�D

�D : f �s (Sk(t))

4√3

2√

��s,k4√2

|Sk(t)|−1/2e−(√

3|Sk(t)|/√

2�s,k) (4)

or the kth frequency component, where �2s,k

is the slowly varyingariance factor.

Using the predefined statistical model for snore and assum-ng Gaussian noise, the conditional pdfs of snore absence can bexpressed as

0 : fX|H0 (Xk(t)) = 1√2��2

n,k

e−(Xk(t)2/2�2

n,k)

(5)

The snore presence hypothesis is derived byH1 for T�D snore signal model:

1 : fX|H1 (Xk(t)) =∫ ∞ 4√3

∣∣Sk(t)∣∣−1/2

4√ √ × e−

√3|Sk(t)|√

2�s,k− (Xk(t)−Sk(t))2

2�2n,k dSk

−∞ 4� 2 �s,k�n,k

(6)

The likelihood ratio of those two conditional pdfs of snore pres-ence and absence as proposed in [33], is defined as

�k ≡ fXk |H1 (Xk)

fXk |H0 (Xk)=

∫ ∞−∞

4√34� 4√2

√�s,k�n,k|Sk(t)| e

−√

3|Sk(t)|√2�s,k

− (Xk(t)−Sk(t))2

2�2n,k dSk

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n,k

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2�2n,k

(7)

where fXk|H1|(Xk) is the hypothesis of snore presence H1 andfXk|H0

(Xk) is the hypothesis of snore absence H0 under the assump-tion of Gaussian distributed noise. The decision criteria is basedon evaluating the geometric mean of the likelihood ratio for theindividual frequencies and is given by

log �k = 1K

K−1∑k=0

log �k

H1><H0

� (8)

where � denotes the threshold of decision.

4.2.3. SNR estimationAn essential intermediate step towards the evaluation of the

likelihood ration testing (LRT) described previously, is the estima-tion of the a priori SNR. Thus, the values of snore and backgroundnoise power spectrum have to be continuously tracked. In thiscase the method of [36], namely predicted estimation (PD) hasbeen employed. According to PD method, the a priori SNR is esti-mated on the power spectrum of noise �n,k(t) = �n,k(t)2 and signal�s,k(t) = �s,k(t)2, which are given by

�s,k(t + 1) = s�s,k(t) + (1 − s)E⌊∣∣Sk(t)

∣∣2|Xk(t)⌋

(9)

262 C. Doukas et al. / Biomedical Signal Proces

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The first evaluation experiments conducted deal with the initialapproach that utilizes discrete Fourier transform and the modelingof signal coefficients, in order to identify breath events. In this case,both annotated and not annotated sound samples have been used.

Table 1Detection of discrete snore events, background noise events and speech events basedon the evaluation of the first method.

Fig. 10. State diagram of sleep breath disorder detection scheme.

here �s,k(t), �n,k(t) are estimates of �s,k(t), �n,k(t) and n, s aremoothing parameters both set to 0.99. The proposed algorithmakes into account both the real and the imaginary parts of thepectrum, by computing the geometrical mean in Eq. (8) using both

arts of the complex spectrum. Thus,∣∣Xk(t)

∣∣2depends on the com-

lex part that is evaluated at every iteration that is either∣∣XR

k(t)

∣∣2

r∣∣XI

k(t)

∣∣2. The estimation of the variance of noise �n,k(t) = �n,k(t)2

nd snore �s,k(t) = �s,k(t)2 is performed separately for real andmaginary frequency parts DFT based on Eq. (9).

.2.4. Threshold estimationThe LRT employed here introduces, by definition, a bias towards

nore detection H1 [35]. This is attributed to the fact that the modelf noise (GD) is present both at the numerator and the denom-nator of the ratio in Eq. (7) (likelihood). This bias introduces anffset, which tends to increase as SNR drops (higher noise). To dealith the dynamic nature of the snore signals captured by the far-eld microphones, an adaptive threshold is introduced that aims athe minimization of the misclassifications. The underlying conceptehind this threshold is to track continuously the mean likelihoodatio value for the snore absent intervals. In this direction a bufferbuf holding past values of log�k is employed. Initially, for the first–2 s of operation the system assumes snore absence. Given the first

ikelihood values the computation of the threshold is performed by

ˆ(t) ≡(

Nbuf + 3 · �Nbuf

)(10)

here �Nbufand Nbuf are the standard deviation and mean of the

alues in Nbuf . The buffer is updated with new values only withinnore absence intervals and if those are smaller than 4 · �Nbuf

. Formoothing the threshold estimate, a forgetting factor �� = 0.98 isntroduced (Fig. 9)

ˆ(t + 1) = �� · �(t) + (1 − ��)�(t + 1) (11)

The following section presents a method for identifying poten-ial apneic incidents based on snore event detection and the use of

hangover state machine.

.3. Apnea indication

Given the decision vectors a snore absence/presence hangovertate machine based on [29] that is able to track transitions fromnore to silence intervals is defined. Time information elapsedetween phenomena of snore presence and absence can be stored

nto buffer and give the ability of indicating apnea condition.The implementation of the hangover scheme as an apnea indica-

or is based on the idea that snores are highly correlated with time

s generated with the function of breath. The hangover schemes implemented as a state machine shown in Fig. 10. Parameters

1 and H0 indicate snore presence and absence respectively, beingriggered by the value of log�k. If the value of the geometric mean

sing and Control 7 (2012) 256– 264

log�k is greater than or equal to the threshold the snore event isdetected otherwise snore absence is assumed. This slightly biasesthe system towards snore detection. Thus the value of log�k is thenused to determine which state H1 or H0 the machine should bein. As mentioned, an apnea incident occurs when patient breathis interrupted for more than 6 s. Given that a new log�k emergesevery 20 ms, a number of 50 consecutive snore absence detectionsshould emerge by the system to indicate an ‘apnea’ incident. A setof 5 (corresponding to 100 ms) consecutive snore presence indica-tions is required to reset the state to ‘normal’ breathing. Followingthe transitions in the Fig. 10, let’s assume that initially the system isat the ‘normal’ breathing state due to past sequential snore detec-tion events. The value of log�k is employed to determine how thehangover scheme should proceed. If it gets below the threshold �,the state machine begins to progress through the transition statestoward the ‘apnea’ state. At this point the incident of ‘apnea’ is notdefinite as the lower value might be a false by the snore detectionalgorithm not being able to detect a snore event. After 50 consecu-tive indications of snore absence is the hangover scheme will enterthe ‘apnea’ state. The chain will remain in that state unless log�k

becomes greater that the threshold. When this event occurs, thehangover scheme will begin to progress through transition statestowards the ‘normal’ breath state. This is done due to the uncer-tainty of snore presence indication, which might be a false alarm.After five consecutive snore indications, the hangover scheme willreturn to the ‘normal’ state and wait till the value of log�k dropsbelow the threshold again. The following section presents someinitial results concerning the evaluation of the above-describedalgorithms.

5. Preliminary evaluation results

In order to evaluate the proposed algorithmic technique forsleep sound analysis, a number of 30 sound samples have beencollected at “Euagelismos Sleep Clinic”, Medical School in Univer-sity of Athens. Each sound sample corresponds to a complete sleepstudy (duration up to 6 h) of patients that either suffered from sleepapnea or were examined for symptoms of sleep breath disorders.Snore sound events have been manually annotated by the SleepClinic experts, in 10 sound samples with duration of 1 h each. Thespecific data have been used for training the algorithms and the restfor testing. Training refers to the hangover state machine presentedin section 4.3 and utilized for identification of apneas versus nor-mal sleep breaths. The table results correspond to the detection ofbreaths and snores by utilizing the two methods presented (voiceactivity detection and DFT signal processing).

In almost all sound samples, additional speech related noise(e.g., background talks, conversation between caregivers andpatient, etc.) was also identified. The annotation of sound signalshas been performed using the Audacity tool [30], since the spec-trum visualization feature it provides, makes annotation muchfaster and efficient. The processing and classification of sound sig-nals is done through custom code developed in Matlab developedby the authors.

Snore events Background noise Speech

Actual events 4232 918 234Detected events 3898 113 detected as snores –

C. Doukas et al. / Biomedical Signal Processing and Control 7 (2012) 256– 264 263

Table 2Detection of discrete snore events, background noise events and speech events based on the evaluation of the second method using real environment recordings.

Noise condition SNR Detection rate False negative rate(type II error)

False positive rate(type I error)

Type I + II error rate

9%

4%

3%

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Quite sleep room 15.30 dB 98.31% 1.6Aircondition operating 14.41 dB 97.36% 2.6Open window 12.85 dB 92.07% 7.9

he collected dataset consisted of segments with duration of at least0 min. The developed model is fed with the latter sound segmentsnd the produces an estimation of snore occurrences with corre-ponding timestamp within the signal. The signal (if not manuallynnotated previously) is then verified (with the help of Audacityool) and the successful detection rate is identified. This method haschieved a successful detection performance of 90–93% with a ratef false detections between 10 and 15%. False detections occurred inases where background noise was pretty high (e.g., periods whereonitoring equipment was producing noise or patient was talking

o the medical personnel) (Table 1).The second evaluation phase was conducted in order to assess

he performance of the proposed VAD-based snore detectionlgorithm. The experimental set-up included the prototype imple-entation of the system, as described in Section 3 in three different

oom conditions/scenarios. A recording frequency of 22.05 kHz haseen used. Sounds have been encoded into MP3 format directly onhe DSP board at a 128 kbps rate and have been recorded into annternal memory card (SD) of 2 GB capacity.

The first scenario involved recordings in a typical home envi-onment during night with no apparent noise; the second waserformed with additional noise from operating air-conditioningystem and the third with additional urban noise from an openindow. The experiments involved the use of the device in record-

ng the sleep of 4 different individuals (2 male and 2 female) with aotal recording duration of 6 h. Again, a manual annotation by thexperts participating in this research of 1 h for each sample waserformed, in order to evaluate later the proper snore detectionroduced by the system. The noise intensity in the three record-

ng scenarios has been restricted close to 15 dB, since the recordingnvironment had to be comfort for the patient.

The following table presents the evaluation results from theifferent recordings:

As it may be seen in Table 2, the introduction of the proposedetection method performs much better than the initial approachdetection rate around 92%). The following section described the

ethodology adopted for developing the apnea indicator module.

. Conclusion

Despite the fact that obstructive sleep apnea is not widelynown, it is a very common disease with high potential implica-ions and effects on patient’s health. The most common assessment

ethod involves the overnight physiological sign monitoring of theatient in Sleep Clinics, and requires specific equipment and spe-ialized personnel. Most widely used diagnosis technique of sleepreath disorder events rely completely on the manual scoring ofhysiological data by specialists, which is time consuming, costlynd not readily available as well. This paper presents a non-invasiveystem for automated Sleep Apnea detection utilizing snore soundnalysis. Snore signals are recorded in the device and snore events,long with apneic events, can be detected by a single mobile device.he major benefit of the system is the ability to monitor patients at

ome improving this way the prognosis and treatment procedurend offering the maximum comfort to patients at same time. Theystem can only be utilized as a preliminary remote assessmentethod in case patients present both OSA and snoring. However,

[

[

6.18% 7.87%3.79% 6.43%4.81% 12.74%

since snoring events are highly related to OSA [37] the system canact as an indication for further assessment of the patient using thestandard PSG techniques. The cost of the prototype system wasabout 500 euros. It is estimated that this cost can be considerablylowered when producing the system massively, and by any meansit is considered of very low cost compared to the cost of the PSGequipment.

The innovative elements of the proposed system are summarizeto the following issues:

• Fast and reliable snore detection algorithms based on SDFT andVAD techniques even in noisy environments.

• Development of an apnea indicator module.• Integration into a single mobile device that may be utilized at

patient’s home.

Conducted experiments using the system in various conditionshave indicated great accuracy in detecting snores against back-ground noise. According to clinical protocol the identification oflong pauses in breaths or snores can indicate an apneic event.

Future work includes the assessment of additional sound prop-erties of the acquired snore signal and the detection of sounds likewhistles, talks and breathing sounds level, which may be also usedfor apnea and hypopnea detection. In addition, the full deploymentof the system in several homes enabling the additional evaluationand collection of an apnea-related sound repository is also targeted.

Acknowledgments

Authors would like to thank Dr. Vayakis and Dr. Koutsoure-lakis from “Euagelismos Sleep Clinic”, Medical School, Universityof Athens, for the collaboration and the provision of snore soundsamples.

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