Lung Sound/Noise Separation for Anesthesia Respiratory Monitoring

Transcription

1 Lung Sound/Noise Separaion for Aneshesia Respiraory Monioring Hong Wang, Le Yi Wang 2, Han Zheng 3, Razmig Haladjian 4, Meghan Wallo 5 Deparmen of Aneshesia,4,5, Deparmen of Elecrical and Compuer Engineering 2,3, Wayne Sae Universiy 5050 Anhony Wayne Dr., MI Unied Saes of America, Absrac: - Noises are one of he key obsacles in applying coninuous monioring and compuer-assised analysis of respiraory sounds in operaing rooms. his paper inroduces a new mehodology for exracing auhenic lung sounds from a noisy environmen. his mehodology uilizes he unique feaure of ime-spli sages in breahing sounds, raher han frequency separaion or saisical independence. By employing a mulisensor sysem, he mehod performs ime-shared blind idenificaion and noise cancellaion wih recursion from cycle o cycle. Since no frequency separaion or signal/noise independence is required, his mehod can poenially provide a robus and reliable capabiliy of noise reducion, and complemen radiional filering and whiening echniques. Is uiliy is evaluaed by simulaion and demonsraed by an applicaion o monioring of endoracheal ube posiions using Human Paien Simulaors. Key-Words: -Blind idenificaion, aneshesia monioring, noise cancellaion, respiraory sounds. Inroducion his paper inroduces a new mehod for blind idenificaion of ransmission channels in lung sound exracion problems. he mehod is derived on he basis of he unique naure of breahing sounds and can be used effecively in aenuaing noise effecs in lung sounds. When applied o aneshesia respiraory monioring, he mehod can poenially enable compuerized sound analysis in real clinical environmen and enhance significanly accuracy and robusness in aneshesia diagnosis.. Moivaion Coninuous monioring of lung sounds is of essenial imporance in medical diagnosis for paiens wih lung diseases and deecion of criical condiions in operaing rooms. o obain quaniaive and reliable diagnosis and deecion, i is criically imporan ha respiraory ausculaion reains sounds of high clariy. Clinical acousic environmen imposes grea challenges for lung sound acquisiion. Unlike acousic labs in which noise levels can be arificially conrolled and reduced, and lung sounds can be processed offline, operaing rooms are very noisy due o surgical devices, venilaion machines, conversaions, alarms, ec. he unpredicable and broadband naures of such noises make operaing rooms a very difficul acousic environmen. his paper inroduces a new blind idenificaion mehod ha is unique o lung sound exracion from noisy environmens. he sysem will consis of several lung sound sensors (special microphones, elecronic sehoscopes, ec.) and a noise reference sensor. Our mehod conducs blind channel idenificaion during he pausing inervals in breahing cycles and performs noise cancellaion during inhale and exhale. Is applicaion in aneshesiology is sudied by some cases involving deecion of endoracheal ube misplacemens. Imporance of noise aenuaion in diagnosis accuracy in hese cases will be illusraed..2 Background Noises are well known o be a fundamenal challenge in developing auomaed lung sound analysis radiionally, sudies of hear and lung sounds have concenraed on filering echniques. o furher enhance he performance of he filering process, FF, power specrum densiy, bi-specrum analysis, wavele analysis, high-order saisics, and sochasic averaging have been invesigaed exensively for heir effeciveness in noise filering and sound separaion [,2,3,4,0]. Frequency-domain filering is applicable for reducing off-band noise (hose wih frequencies ouside he signal frequency band). Anoher class of

2 noise cancellaion mehodologies relies on sochasic independence [5,6,7,6]. Consider a respiraory sound measuremen sysem in Figure. he main ideas of sochasic whiening are: () Firs generae from y and y2 he signals w and w2 ha are saisically as independen as possible. (2) hen, w is viewed as an esimae of he sound y. his mehod is effecive only for independence noises and canno capure any gains or nonlinear saic mapping in he ransmission media. Fig.: Sound Channels We will develop an innovaive noise reducion mehodology ha is uniquely designed o overcome he above key difficulies. Since our mehod does no rely on frequency band separaion or saisical independence beween lung sounds and noises, i can provide a more robus noise cancellaion in his applicaion. We should emphasize ha applicaion of our mehod does no exclude filering or whiening mehods. In oher words, one may elec o apply arge filering by using boh frequency filers and whie noise separaion afer employing our mehod. 2 Mehod For lung sound exracion problems, he noise ransmission channels mus be idenified. Since noises canno be measured a source, i is a blind idenificaion problem. 2. ime-spli Blind Channel Idenificaion Our idea is o inroduce a virual noise framework and o use a ime-spli mehod o idenify he sysem and o achieve noise cancellaion. he noise reference sensor, which is placed in viciniy o he lung sensors, receives noises from all sources jus like he lung sensors, bu does no receive lung sounds. If we view y2 as a virual noise source, we may replace disribued noise sources in a lumped noise source y2, as shown in Fig. 2. Noise cancellaion is now reduced o idenificaion of he virual noise channel G (in Fig., G is he inverse of C3 followed by C2). Indeed, given esimaed G, he noise-free lung sound y can be approximaely exraced as y = y G y2. Idenificaion of G remains a difficul problem since y and y2 may be correlaed. Wihou a direc oupu measuremen of he channel G, all exising idenificaion mehods fail o apply. Our approach in resolving his cenral issue is based on a simple observaion: Venilaion or breahing cycles undergo he sages of inhale, exhale, and ransiional pause. In beween exhale and inhale, here is a pausing inerval in which lung sounds are very small. Consequenly, he measured y is acually he oupu of he noise channel G in ha inerval. As a resul, we can use inpu/oupu pair (y2 and y) o idenify G in he inerval. his will no require any assumpion on independence or frequency separaion. his idea leads o he following lung sound/noise separaion algorihms. Fig.2: Virual Noise Formulaion 2.2 Lung Sound/Noise Separaion For signal processing, a venilaion or breahing cycle is divided ino hree sages: Inhale (i), exhale (e), and ransiional pause (-i-e). hey are idenified () by venilaor variables, e.g., airway pressure cycles (posiive-negaive-neural) in venilaed paiens; or (2) by smoohed breahing wave profiles in naural breah. For he virual noise configuraion shown in Fig. 2, le G be paramerized by a parameer vecor θ, denoed by G(θ). Mos commonly used paramerizaion is he ARMAX model (auoregression, moving average, exernal inpu). he parameer vecor θ is o be idenified. he algorihm can be described as follows. Iniial Channel Idenificaion: During a pause sage, he measured y2 (virual inpu) and y (oupu) are used o idenify he noise channel G(θ), using a recursive algorihm, ha will be deailed laer. he esimaed model will be denoed by G(θ 0 ). Sep : Inhale and Exhale Sages A he k-h breahing cycle (k=0,,2, ), during he i (inhale) and e (exhale) sages, he esimaed noise channel model G(θ k ) is used o exrac he original lung sound via y = y G(θ k ) y2. Sep 2: ransiional Pause Sage During he pause sage of he k-h breahing cycle, he esimaed noise channel model is updaed by

3 using he new daa from measured y2 (virual inpu) and y (oupu). he channel model G(θ k ) is used as he iniial condiion and he model is updaed by a recursive algorihm, leading o an updaed model G(θ k+ ). Recursive Seps In he (k+)-h breahing cycle, go o Sep wih he newly updaed channel model G(θ k+ ). hese seps are hen repeaed from cycle o cycle. his cycle-o-cycle recursion will be compuaionally very efficien since models are updaed by using only new measuremens and no pas daa need o be remembered. 2.3 Recursive Idenificaion Algorihms We now provide some deails on recursive algorihms. here are many possible choices. he following four are mos commonly used in applicaions and have been esed by he auhors in aneshesia applicaions. Consider an ARMAX model for G y = he value of y a ime index u = he value of y2 a ime index φ = [ u, u,, u ] n+ θ = he n-dimenional parameer vecor Regression model of G( θ ): () y = φθ + w he following recursive algorihms can be used o updae he parameer vecor θ. Adapive Filering: c θ = θ + + φ( y φ θ), 0 r r < Adapive Filering wih Averaging: ˆ c θ = ˆ θ + + φ( y φ θ), 0 r r < θ θ ˆ θ θ + = One-Sep Opimal Projecion: φ θ = θ + + r ( y φ θ ) a φφ + Recursive Leas-Squares: Pφ K = ; P ( I K + = φ ) P + φ Pφ + = + K y θ θ ( φ θ ) heoreical properies of hese algorihms have been exensively sudied, especially heir convergence and convergence raes. We refer he reader o [9] for furher heoreical deails. 3 Illusraive Examples his secion presens some illusraed examples ha demonsrae he uiliy of he mehod inroduced in his paper. o provide flexibiliy in evaluaing our mehod, exensive simulaion has been performed. he sysem includes several noise sources wih differen characerizaions (such as waveforms, frequency ceners, and bandwidh), waveforms colleced from real environmen, as well as compuer generaed random noises. Noise sources pass hrough several differen ransmission channels o influence he lung-sound sensor and reference sensor. he srucure and parameer values of hese channels are no known o he idenificaion algorihms.he sysem idenificaion akes a black-box approach in which a discreized channel model wih he regression represenaion () is used. hese simulaion sudies include variaions in noise ypes, frequency shifing, waveforms, and magniudes. 3. Noise Impac on Sound Characerisics We shall sar wih an illusraion of noise impac on lung sound paerns. Fig. 3(a) illusraes a ypical normal breahing sound and an expiraional wheeze (hese are from he public respiraory sound daabase of Music Deparmen of McGill Universiy). For his example, he wheeze can be clearly characerized by a subsanial narrowing of specrum, shifing of cener frequency (owards low pich in his example), and power imbalance of inspiraion and expiraion. For his example, sounds are obviously very clean wih minimum noise corrupion. Sound paerns are significanly alered when noise arifacs are considered. Fig. 3(b) shows he corruped wheeze, boh in is ime-domain waveforms and frequencydomain specrum. I is clear ha in a noisy environmen, he ime-domain waveforms of a wheeze are disored o he poin ha i is no longer possible o recognize sound paerns. 3.2 Channel Idenificaion Here, we consider naural breahing paiens in which he phases of inhale, exhale, and pause are mainly refleced in he sound power or averaged magniude profiles. Hence, swiching beween idenificaion and noise cancellaion will be derived from lung sound signals.

4 During he noise-cancellaion phase, he esimaed regression model is used o derive he noise esimae which is hen subraced from he signal measured by he lung sensor. he process is hen repeaed in he nex breahing cycle. he boom plo is he esimaed lung sound. We jus wan o commen ha here are some sudies on ime-domain sound paerns [8,,2]. he noise corrupion in his simulaion alers he sound waveforms significanly so ha he ime-domain wave paerns of he original lung sound are no longer apparen. (a) Uncorruped Lung Sounds (a) ime-domain Feaures (b) Noise Impac on he Wheeze Figure 3 A Normal Sound and a Wheeze, and Noise Impac on Sound Paerns Figure 4 presens a ypical simulaion resul. he original lung sound is a ypical normal breahing waveform (he op plo in (a)). he environmen noise is, afer passing hrough an unknown ransmission channel, measured by he reference sensor (he 2 nd plo in (a)). he lung sound sensor is corruped by environmen noises wih is signal denoed by he 3 rd plo in (a). While he lung sound is significanly corruped by he noise, is envelope profile sill reains an indicaion of is inhale, exhale, and pause sages. his profile informaion is used o divide each breahing cycle ino he phases for idenificaion or noise cancellaion. In his simulaion, a 30-h order moving average regression model is used in idenificaion. During he idenificaion phase, a recursive leas-squares idenificaion algorihm is used o updae he parameers in he regression model. (b) Frequency-Domain Feaures Figure 4 Channel Idenificaion and Noise Cancellaion A beer undersanding of he effeciveness of our mehod is depiced in he frequency-domain comparison in Fig. 4(b). he noise specrum overlaps wih he lung sound specrum. he esimaed lung

5 sound resores he power specrum of he lung sound he boom plo of (b) shows effeciveness of noise reducion. In erms of power levels, he lung sound measuremens conain 5-25% noises, which are largely removed via his mehod. 4 Monioring of Endoracheal ube Posiions Unlike he class of naural breahing cases, if a paien is venilaed in aneshesia he breah paern swiching from inhale o exhale and hen o pause is conrolled by a venilaor. Consequenly, swiching beween idenificaion and noise cancellaion can be direcly derived from venilaion variables, such as airflow pressure changes. Lung sounds can be used o monior endoracheal ube posiions. A proper inubaion will have he ube posiioned in racheal. As a resul, oxygen will be venilaed ino boh lungs. his implies ha breahing sounds from boh lungs should be presen. When he ube is eiher misplaced or drifed ino one side of he lungs, bronchial inubaion occurs. In bronchial inubaion, only one side of he lungs is properly venilaed, while he oher receives no oxygen supply. Bronchial inubaion has dire clinical consequences and mus be promply deeced. Bronchial inubaion is refleced by imbalanced sound inensiies beween he lef and righ lungs. Fig. 5 shows a case sudy involving noise-corruped lung sounds in a paien (20-year-old healhy soldier simulaed on he Human Paien Simulaor from MEI, Inc.). In his sudy, arificially-creaed righ bronchial inubaion wih differen degrees of reduced lung sounds is used. he simulaion seing conains wo lung sensors, one for he lef lung and one for he righ lung, as well as a noise reference sensor. All sensors are elecronic sehoscopes in his sudy. Wihou noise corrupion, one may simply observe he sound powers from boh lung sounds and compare heir relaive inensiies, as shown in he boom-lef plo of Fig. 5. When power or inensiy imbalance reaches a pre-designed hreshold, a diagnosis of bronchial inubaion may be prescribed. However, noises will have a derimenal impac on his approach. When high noise levels are experienced, he difference in power specrum densiies diminish, as shown in he righ-boom plo of Fig. 5. able summarizes some ypical power levels of lung sounds for bronchial inubaion monioring in a paien simulaion, in which lef bronchial inubaion was inroduced. he power of he lef lung sound, ha always receives oxygen and is no affeced by he ube migraion, is normalized a 00%. hen he relaive power level of he righ lung sound reflecs he severiy of ube migraion. he lower he percenage shif, he lower he oxygen venilaion level o he righ lung. If he lung sounds are no corruped by noise, one can selec a deecion hreshold, say, 65% for deecing bronchial inubaion. he following able is one resul from he simulaions. he 2 nd and 3 rd columns indicae he power levels of lef and righ lung sounds under noise-free environmen. able. Power Levels of Lung Sounds Figure 5: Monioring of Bronchial Inubaion he las wo columns of he able show changes in power levels when noises are added. Due o severe noise corrupion, his deecion problem becomes much more difficul. For example, if he deecion hreshold is se a, say, 65%, hen noise arifacs will make he deecion algorihm miss even he complee righ-lung bronchial inubaion. I should be emphasized ha since his is a sochasic deecion problem, reliabiliy and accuracy of bronchial inubaion deecion can be assessed by he

6 probabiliies of false alarm and missed deecion, boh mus be small. Consequenly, shifing hreshold for alarm will simply increase one probabiliy and reduce he oher. Hence, i will no enhance overall deecion accuracy. Also, unlike sandard saisics in which he sample size can be used in reducing impac from randomness, deecion accuracy in his applicaion canno be improved by using more breahing cycles. his is due o he persisen naure of noises. I seems ha he only remedy for deecion accuracy is o reduce noise effecs by signal processing mehods. 5 Concluding Remarks his paper inroduces a new noise cancellaion mehod for exracing auhenic lung sounds from noisy ausculaion environmens. he mehod is unique in is uiliy of he breahing pause period for sysem idenificaion and inhale/exhale phases for noise cancellaion. As such i resolves a dauning challenge in his blind idenificaion problem: noises may have similar frequency bands as he lung sounds and may no be saisically independen o he lung sounds. his approach opens he opporuniy of exending compuer-aided lung sound analysis from acousic lab seings o real clinical applicaions. here are many possible issues ha can be sudied in his direcion. hese include is effeciveness in nonlinear noise ransmission channels, sensor locaion selecions, sensor configuraion, impac of modeling disribued noises by lumped noises, ec. Also, combinaion of his mehod wih regular filering (for eliminaing off-band noise) and whiening (removing independen noises) can be sudied. However, he key foundaion of his mehod seems o be sound in his applicaion. References: [] Charleson, S.; Azimi-Sadjadi, M.R.; Gonzalez- Camarena, R., Inerference cancellaion in respiraory sounds via a muliresoluion join ime-delay and signal-esimaion scheme, Biomedical Engineering, IEEE ransacions, Oc. 997, pp [2] Gavriely N. Nissan M. Rubin AH. Cugell DW. Specral characerisics of ches wall breah sounds in normal subjecs. horax. 50(2), 995 Dec, pp [3] Hadjileoniadis, L.J.; Panas, S.M. Adapive reducion of hear sounds from lung sounds using fourh-order saisics. IEEE ransacions on Biomedical Engineering, 44 (7), 997, pp [4] Hadjileoniadis, L.J.; Panas, S.M. Adapive reducion of hear sounds from lung sounds using wavele-based filer. Sud. Healh echnol. Inform. 43 (Par B), 997, pp [5] S. Haykin, Adapive Filer heory, Prenice-Hall, Englewood Cliffs, NJ, 990. [6] S. Haykin, ed. Unsupervised Adapive Filering, Vol. I and II, John Wiley & Sons, Inc., [7] Kompis M. Russi E. Adapive hear-noise reducion of lung sounds recorded by a single microphone. Proc. Annual Inernaional Conference of he IEEE, Vol. 2, 992, pp [8] Lehrer S. Undersanding Lung Sounds, 3 rd ed., W.B. Sounders, [9] L. Ljung, Sysem Idenificaion: heory for he User, Prenice-Hall, Englewood Cliffs, NJ, 987. [0] Lu YS. Liu WH. Qin GX. Removal of he hear sound noise from he breah sound. Engineering in Medicine and Biology Sociey, Proceedings of he Annual Inernaional Conference of he IEEE, vol., Nov. 988, pp [] Paserkamp H. Kraman S. Wodicka G. Respiraory sounds. Am. J. Respir Cri Care Med. Vol. 56, 997, pp [2] Sovijarvi AR. Heliso P. e al A new versaile PC-based lung sound analyzer wih auomaic crackle analysis (HeLSA); repeaabiliy of specral parameers and sound ampliude in healhy subjecs. echnology & Healh Care. 6(), 998 Jun, pp [3] Wang H. and Wang LY., Coninuous Inro- Operaive Respiraory Ausculaion in Aneshesia, IEEE Sensors 2003, orono, Oc., [4] Wang H. and Wang LY., Muli-Sensor Adapive Hear and Lung Sound Exracion, IEEE Sensors 2003, orono, Oc., [5] Wang LY., Yin G., and Wang H., Nonlinear sysem idenificaion in medical applicaions, SYSID 2003, Roerdam, he Neherlands, Augus 27-29, [6] B.Widrow and S.D. Searns, Adapive Signal Processing. Prenice-Hall, Englewood Cliffs, NJ, 985.