PREDICTION OF BODY MASS INDEX (BMI) USING SPEECH SIGNALS WITH WAVELET PACKET BASED FEATURES

Transcription

1 VOL., NO., JANUARY 6 ISSN Asia Research Publishig Network (ARPN). All rights reserved. PREDICTION OF BODY MASS INDEX (BMI) USING SPEECH SIGNALS WITH WAVELET PACKET BASED FEATURES Chawki Berkai, M. Harihara, Sazali Yaacob ad Mohd Iqbal Omar School of Mechatroic Egieerig, Uiversiti Malaysia Perlis (UiMAP), Campus Pauh Putra, Perlis, Malaysia Uiversiti Kuala Lumpur Malaysia Spaish Istitute, Kulim Hi-Tech Park, Kulim, Kedah, Malaysia chawki.berkai@gmail.com ABSTRACT Adiposity owadays is oe of the most importat causes which are resposible for the chroic maladies such as hypertesio ad diabetes ad i the comig years, it is predestied to be resposible for may deaths. The body mass idex (BMI) is a useful tool which is commo i the field of medicie ad experts of the wellbeig to determie whether the idividual is uderweight, ormal, overweight or obese. BMI has some drawbacks i the assessmet i the degree of obesity as it does ot take ito accout the distributio of body fat, makig it a skewed metric. I the recet years, researchers have started to pay attetio o ivestigatig the relatioship betwee speech sigals ad BMI status. Thus, the paper reports the predictio/estimatio of BMI status (ormal, overweight, ad obese) via speech sigals usig Wavelet Packet Trasform (WPT) ad Probabilistic Neural Network (PNN) with the omissio of weight ad height values. For this study, Daubechies orders of "db4", "db6", "db" ad "db" are selected radomly. The speech sigals (/ah/ souds) are decomposed ito five levels by WPT. Eergy ad Etropy features are calculated at various sub-bads. -fold cross validatio techique has bee implemeted to examie the cosistece of the classifier results. The experimetal results reveal that the suggested features ad classificatio algorithm give classificatio accuracy of > 87%. Keywords: adiposity, BMI, wavelet packet trasform, probabilistic eural etwork. INTRODUCTION The obesity owadays is the major health issue which may coutries aroud the world are ecouterig. Besides, the problem of overweightig has become the focus of the health experts rather tha the mal utritio roud the world. The official statistics reveal that the umber of people who are obese is about 475 millio compared with the people who are cosidered to be overweight which attai.5 billio amog the adults (Kii, 5). The problem of overweightig has become a rampat pheomeo eve amog the childre ad the kids who have ot reached puberty ad it is estimated that there are more tha millio childre who are overweight. This has impacted egatively their health ad has had some cosequeces o them such as shorteig their logevity compared with the previous geeratios (Deckelbaum ad Williams, ; Reilly et al., 5). Obesity ca be described as a medical pheomeo whe the weight of the body icreases dramatically which icreases the amout of fat. The excess of the amout of fat i the body has a otorious effect o the health. Wheever there is istability of calories i the body which meas that the amout we take as a form of food ad utritio fat exceeds the amout we sped the this defiitely result to a extra weight which is fat. A myriad of elemets are resposible for causig the problem of obesity ad the imbalace of eergy itake ad eergy spet for istace: the shift from the jobs which require braw ad physical stregth to jobs that require brai ad cogitio, the umber of people who ow cars has augmeted therefore they rarely walk ad sped eergy, electroic appliaces ad devices which eable people s lives ad make it hassle free therefore ot too much eergy is cosumed, idleess which lead to abadomet of workig out ad less activity, over cosumptio of food; high calories ad fat i the igrediets (Kii, 5; Newburgh, 93). There are other factors which cotribute to the issue of obesity such as social, ecoomic ad cultural oes where the impact vary from oe coutry to aother ad the discrepacies ca be easily perceived (Ng et al., 4). It is highly recommeded the perso who has started to become obese ad gai extra weight to cosult a medical treatmet sice this issue will lead to other health complicatios ad umerous studies have bee carried out to ivestigate the relatio betwee the body mass idex ad the diseases caused as a aftermath of this ad the results were positively edorsig this theory (Pasco, Nicholso, Brea, ad Kotowicz, ). Quetelet idex (BMI) is determied as a proxy which helps to determie the ratio of the perso s weight accordig to his height (Muralidhara, 7). The BMI is a useful tool which is commo i the field of medicie ad experts of the wellbeig to determie whether the idividual is uderweight, overweight or obese. BMI is a proxy ad is computed by the dividig the mass of the body (Kg) by the height (i meters) power of two (Rothma, 8). The World Health Orgaizatio has labeled overweight whe the perso s BMI falls withi a rage of 5. ad 9.9 ad if it exceeds 3 the the perso is obese. Whe the BMI is withi the iterval of 8.5 ad 4.9 so the perso is i a state of equilibrium which meas either fat or 559

2 VOL., NO., JANUARY 6 ISSN Asia Research Publishig Network (ARPN). All rights reserved. uderweight but if the BMI falls less tha 8.5 the the perso is cosidered uderweight (Disdale, Ridler, ad Ells, ). These threshold values were agreed upo based o empirical studies ad medical research which cocluded that BMI is liked to maladies ad early death. BMI has bee prove ad it has show that it is the most useful tool to prove whether the idividual is sufferig from ay kid of obesity (Rothma, 8). BMI is very commo i use ad has may advatages such as accuracy, swiftess ad efficiecy; furthermore it ca be used for both geders ad all age categories. BMI is a vital meas that eables us to evaluate the mass of the body ad help to categorize its weight. It is a importat tool to calculate the amout of fatess rather tha just the weight ad aother privilege is that to be classified withi the ormal category, your weight does ot have to be of precisely measured. BMI has some drawbacks i the assessmet i the degree of obesity, for istace if a perso has braw ad is physically well built the he is cosidered fat sice he belogs to the group of the overweight but i fact he is ot (Celli et al., 4; Pope, Sowers, Welch, ad Albrecht, ). Moreover, BMI may be somehow misleadig whe it comes to categorizig people who have some muscular problems ad they classified withi the people whose weights is good. Besides, BMI is ot a suitable measure ad caot be applied for people who are small i size ad durig the time of pregacy. Fat i the body ca cause serious implicatios o the health especially the hips ad the thigh s fat. As a ew area of iterest, scietists have begu to focus ad study the lik betwee the sigals that are geerated by the speech ad BMI status. A myriad of ways have bee itroduced ad suggested i order to forecast the differet categories of weight (ormal, overweight ad obese) which relies o a mixture of the characteristics of voice that are related to the BMI status. Besides, i the latest time, ucomplicated methods have bee implemeted by researchers for predictio of body mass idex through shimmer, jitter, pitch, rate of speech, format, HNR, PLCC ad Mel frequecy cepstral iformatio (Arsikere, Leug, Lulich, ad Alwa, 3; Lee, Kim, Ku, Jag, ad Kim, 3; Lee, Ku, Jag, ad Kim, 3; Sedaaghi, 9). From the previous review, it have bee showed that may extractio features method were used to characteristics deductio ad classificatio algorithms were also suggested (Arsikere et al., 3; Lee, Kim, et al., 3; Lee, Ku, et al., 3; Sedaaghi, 9). The utilizatios of Wavelet ad Wavelet Packet Trasform (WPT) have ot bee the focus of the categorizatio of BMI status. I fact few studies have tried to shed light o the issue usig wavelet ad wavelet packet trasform. The use of wavelet ad WPT aalysis is broad, vast ad varyig ad it is commo i use whe it comes to image ad sigal processig applicatios (Avci ad Avci, 8). This research which uses wavelet packet trasform with eergy ad etropy characteristics ad Probabilistic Neural Network is desiged to foresee ad have a early overview o the idividual s BMI status whe the calculatios of weight ad height are omitted. The /ah/ soud sigals are costituted of five compoets. The elemets of etropy ad eergy are calculated from the wavelet packet coefficiets ad are utilized to illustrate /ah/ soud sigals. A probabilistic eural etwork has bee implemeted to examie the efficiecy of the characteristics of wavelet packet (WP) eergy ad etropy. The results reveal ad coclude that the characteristics of wavelet packet ad PNN classifier ca be of high importace help to assist the people who are i the field of medicie to forecast ad foresee the BMI status. METHODOLOGY I this work, classificatio of BMI status cotais of two importat phases as showed i Figure-, acoustic feature extractio ad classificatio. By usig wavelet packet trasform, the speech sigals (/ah/ souds) are decomposed ito five levels. Eergy ad etropy features are extracted at each level to describe the /ah/ soud. PNN classifier is implemeted to estimate the efficiecy of the etropy ad eergy characteristics for classifyig the three BMI statuses (ormal, overweight ad obese). However, the researchers have used differet databases for that it is difficult to like our fidigs with earlier research. Figure-. Scheme blocks of the feature extractio ad classificatio stage. DATABASE A total of 75 people without ay voice-associated problems participated i this study with ages ragig from to 4 years. The cliical ifo of all the subjects was recorded (Table-). Usig /ah/ soud, the microphoe was positioed at a distace of about cm from the mouth of the subjects at the momet of recordig the voice. 56

3 VOL., NO., JANUARY 6 ISSN Asia Research Publishig Network (ARPN). All rights reserved. Table-. Stadard deviatio, mea of age ad umber of subjects. Geder Class Number of subjects Mea age (Std) BMI Normal (.3).9±.6 sigal recordig Sample rate Female Male Overweight (6.35) 9.46±.5 Obese (4.9) 35.7±3.49 Normal (3.5).±.7 Overweight (4.94) 7.36±.33 Obese 8.77 (4.94) 34.86±4.68 Philips LFH35 SpeechMike Premium microphoe 44 KHz FEATURE EXTRACTION Durig (DWT) discrete wavelet trasform dissolutio is i process, sigal is costituted ito two frequecy bads such as higher ad lower frequecy (Paiva ad Galvão, ; Zhag ad Li, ). Low frequecy bad is utilized for more process which is related to decompositio. Thus discrete wavelet trasform gives a left recursive biary tree structure. Through wavelet packet decompositio procedure, higher ad lower frequecy bads are decomposed ito two sub-bads. Therefore WP gives a stable biary tree structure. I the tree, by the umber of subspaces p ad its depth i (followig forms) (Harihara, Yaacob, ad Awag, ; Paiva ad Galvão, ) every subspace is idexed: p ( ) i k l[] is a low-pass (scalig) filter p ( ) i k l p i k...() l i p i k...() i Where h[] is the high pass (wavelet) filter. WP decompositio assistaces to divider the high frequecy side ito smaller bads which caot be achieved by applyig the geeral DWT (Kumbasar ad Kucur, ; Zhag ad Li, ). The speech sigals (/ah/ souds) are split i to five levels i this study. The umber of features at the fifth level of decompositio was 3. The samplig frequecy fs is 6, Hz. The /ah/ souds are filters ad four differet orders of Daubechies wavelets db4, db6, db ad db are used by usig the WP filters. The speech sigals are tested at 6 khz givig a 8 khz badwidth sigal. Due to the followig characteristics, Daubechies wavelet has bee chose i this study: Fast computatio, time ivariace, Sharp filter trasitio bads (Cohe, Daubechies, ad Feauveau, 99; Harihara et al., ). I this work, usig the extracted WP coefficiets, etropy ad eergy are computed. Through the followig Eq. (3) usig the extracted WP coefficiets, the sub-bad eergy ca be calculated: Eergy p C, k i (3 ) Where the umber of decompositio levels is =,,., N ad k=,,. N - ad p is the measure idex. Etropy ca be utilized as a joit measure to quatify ueve patter of the /ah/ soud sigals. Usig the extracted WP coefficiets, by the followig Equatio (4), the measure of etropy ca be calculated. Etropy i C p, k log C p, k (4) Where the umber of decompositio levels is =,,., N ad k=,,.n- ad p is the measure idex. Figures 3(a), 3(b) ad 3(c) show the two levels WP decompositio of the speech sigals (/ah/ souds) relatig to the BMI status (ormal, overweight ad obese). Accordig to the graphs, it is difficult for us to discrimiate amog three speech sigals (/ah/ souds) that was recorded from the ormal, overweight, ad obese" subjects. Therefore, to determie WP coefficiets, eergy ad etropy are used. A feature database is created, after the calculatio of etropy measures from each subbad wavelet packet coefficiets ad used for classificatio purpose. The block diagram of the feature extractio ad classificatio stage is show i Figure-. Figure-. Feature extractio ad classificatio stage. 56

4 VOL., NO., JANUARY 6 ISSN Asia Research Publishig Network (ARPN). All rights reserved. Normal sigal Node:[ ].4 Node:[ ] Node:[ ] Node:[ ]. Node:[ ].4 Node:[ 3] Figure-3(a). Two level WP decompositio of the /ah/ soud sigal for ormal class with db wavelet. Overweight sigal Node:[ ].4 Node:[ ] Node:[ ] Node:[ ]. Node:[ ].4 Node:[ 3] Figure-3(b). Two level WP decompositio of the /ah/ soud sigal for overweight class with db wavelet. 56

5 VOL., NO., JANUARY 6 ISSN Asia Research Publishig Network (ARPN). All rights reserved. Obese sigal Node:[ ]. Node:[ ] Node:[ ] Node:[ ].4 Node:[ ]. Node:[ 3] Figure-3(c). Two level WP decompositio of the /ah/ soud sigal for obese class with db wavelet. CLASSIFICATION Probabilistic eural etwork The artificial eural etwork is oe of the moder tools which are used to study the tred ad the behavior of some pheomeo ad to poit out the issues that are beig examied through the experimets carried out i differet fields. The aim of our curret study is to examie the impact of PNN structure o the BMI status classificatio which has to deal with some medical issues such as the weight gaiig. PNN ca be split ito four segmets such as the iputs, patter, summatio ad the output segmet (Specht, 99). The PNN ca compute oliear decisio boudaries by substitutig the sigmoid activatio fuctio used i eural etworks with a expoetial purpose ad a fast traiig process, orders of magitude faster tha back propagatio which will allow iheretly parallel structure, that guarateed to coverge to a optimal classifier as the size of the illustrative traiig set icreases, a local miima issues ca be avoided ad at the same traiig samples ca be supplemetary or removed without extesive retraiig. All these segmets are highly liked ad coected with each other. The patter segmet is stimulated through a expoetial fuctio rather tha sigmoidal oe. The segmet related to patter is resposible for the measuremet of the space which separates the iput vector ad the traiig iput vector. The summatio segmet gathers all the outputs which are dedicated to cotribute ad results i a output kow as a vector of probabilities. These probabilities use the biary priciple, the segmets resposible for outputs display for the cocered segmet ad otherwise by implemetig a full trasfer fuctio. The et is a efficiet meas that is utilized for the purpose of classificatio whe a patter of each of the two segmets whe it is itroduced to it. O the other had, PNN ca be of great help wheever it is put through a myriad of examples. Varyig smoothig parameter (SP) has a power o the oliearity of the assessmet s iterval of the et. The otio of decisio boudary attai a hyper plae for bigger SP ad for the small SP it roud up the decisio surface of the classifier. RESULTS AND DISCUSSIONS I The /ah/ soud sigals are exposed to acoustic feature extractio utilizig WPT with eergy ad etropy features. With the database preseted above, -fold- cross validatio schemes are utilized to show the cosistecy of the classificatio results of BMI status. I the -fold cross validatio scheme, the traiig is repeated times ad the suggested feature vectors are split haphazardly ito sets. The PNN is applied for classifyig the BMI status. To get maximum accuracy, Smoothig parameter (SP) value was varyig i PNN. Classificatio results of PNN for differet wavelet families are showed i Table-. Average ad stadard deviatio of the categorizatio accuracies of differet types of BMI status are displayed i a Table. The stadard deviatio of the classificatio obviously illustrates the reliability of the classifier results. 563

6 VOL., NO., JANUARY 6 ISSN Asia Research Publishig Network (ARPN). All rights reserved. Table- shows the results of PNN classifier for Daubechies orders db4, db6, db, ad db with etropy ad eergy features. From Table-, the maximum classificatio accuracy was gotte at the fifth level of WPD. The best average classificatio accuracy usig the PNN classifier for ormal class is foud to be 87.55% with less stadard deviatio of.33 for db ad db. The lowest classificatio accuracy is 73.7% with the stadard deviatio of.33 for db usig the PNN classifier for overweight class. The best overall accuracy was 8.83% for eergy feature ad 84.73% for etropy feature. The umber of features at the fifth level of decompositio was 3. All the features were utilized to provide better represetatio of /ah/ soud sigal. For Daubechies orders db4, db6, db, db etropy features implemet better tha eergy features. At the fifth level of decompositio, both the features equally perform well for db4, db6, db, ad db. I this paper, it ca be observed from the above discussio that the highest of classificatio accuracy ca be gotte irrespective of the differet order of Daubechies wavelets. At the fifth level of wavelet decompositio, the maximum classificatio accuracy of 87.55% for db ad db was gotte ad it displays that the Suggested features ad classificatio algorithm offers a good classificatio compared with previous research (73.8% (Lee, Ku, et al., 3)). Table-. BMI status classificatio results usig PNN at every level of WP decompositio for diffidet Daubechies orders db4, db6, db, db. Daubechies orders db4 db6 db db Classes Eergy features Etropy features Eergy + Etropy features Normal 8.35± ±. 84±.75 overweight 73.97± ± ±.9 obese 78.5±.3 8.±.4 84.±.35 Overall accuracy 78.9±. 8.55±.7 8.8±.6 Normal 8.3±.9 8.7± ±.99 overweight 74.9± ± ±. obese 8.±.6 8.9± ±. Overall accuracy 79.35± ± ±.67 Normal 84.97± ± ±.33 overweight 74.76± ± ±.48 obese 8.76±. 83.8± ±.93 Overall accuracy 8.83± ± ±.73 Normal 8.64±. 84.8± ±. overweight 73.7± ± ±.63 obese 83.58± ±. 84.6±. Overall accuracy 79.64± ± ±.8 CONCLUSIONS This paper presets the aalysis of speech sigals (/ah/ souds) based o the Wavelet Packet Trasform with eergy ad etropy features ad PNN classifier. The /ah/ soud sigals were decomposed ito five levels. By usig wavelet packet coefficiets at every level, etropy ad eergy features were extracted. The impact of the proposed features was examied at each level of decompositio of /ah/ soud sigals. At the fifth level of decompositio, maximum classificatio accuracy of 87% was acquired. The classificatio results idicate that the proposed approach has abilities to evaluatio the BMI Status with accuracy of > 87%. To evaluate our method more studies will be implemeted with larger databases. ACKNOWLEDGEMENT Authors would like to ackowledge the Joural Icetive Research Grat received from Uiversiti Malaysia Perlis (UiMAP). [Grat No: 97-97]. REFERENCES Arsikere H., Leug G. K., Lulich S. M. ad Alwa A. 3. Automatic estimatio of the first three subglottal 564

7 VOL., NO., JANUARY 6 ISSN Asia Research Publishig Network (ARPN). All rights reserved. resoaces from adults speech sigals with applicatio to speaker height estimatio. Speech Commuicatio. 55(): 5-7. Avci E. ad Avci D. 8. A ovel approach for digital radio sigal classificatio: Wavelet packet eergymulticlass support vector machie (WPE MSVM). Expert Systems with Applicatios. 34(3): Celli B., MacNee W., Agusti A., Azueto A., Berg B., Buist A.,... Fahy B. 4. Stadards for the diagosis ad treatmet of patiets with COPD: a summary of the ATS/ERS positio paper. Europea Respiratory Joural. 3(6): Cohe A., Daubechies I. ad Feauveau J.-C. 99. Biorthogoal bases of compactly supported wavelets. Commuicatios o pure ad applied mathematics. 45(5): Deckelbaum, R. J., & Williams, C. L. (). Childhood obesity: the health issue. Obesity research, 9(S), 39S- 43S. Disdale H., Ridler C. ad Ells L.. A simple guide to classifyig body mass idex i childre. Natioal Obesity Observatory, Oxford. Harihara M., Yaacob S. ad Awag S. A.. Pathological ifat cry aalysis usig wavelet packet trasform ad probabilistic eural etwork. Expert Systems with Applicatios. 38(): Kii S. 5. Associatio of obesity with physical activity, televisio viewig, video/computer gamig amog school childre i Magalore. Idia Joural of Commuity Health, 7(Supp ). Kumbasar V. ad Kucur O.. Performace compariso of wavelet based ad covetioal OFDM systems i multipath Rayleigh fadig chaels. Digital Sigal Processig. (5): Lee B. J., Kim K. H., Ku B., Jag J.-S. ad Kim J. Y. 3. Predictio of body mass idex status from voice sigals based o machie learig for automated medical applicatios. Artificial itelligece i medicie. 58(): 5-6. Muralidhara D. 7. Body mass idex ad its adequacy i capturig body fat. Thai J Physiol Sci., 97-. Newburgh L. 93. The cause of obesity. Joural of the America Medical Associatio. 97(3): Ng M., Flemig T., Robiso M., Thomso B., Graetz, N., Margoo C., Abera S. F. 4. Global, regioal, ad atioal prevalece of overweight ad obesity i childre ad adults durig 98-3: a systematic aalysis for the Global Burde of Disease Study 3. The Lacet. 384(9945): Paiva H. M. ad Galvão R. K. H.. Optimized orthoormal wavelet filters with improved frequecy separatio. Digital Sigal Processig. (4): Pasco J. A., Nicholso G. C., Brea S. L. ad Kotowicz M. A.. Prevalece of obesity ad the relatioship betwee the body mass idex ad body fat: crosssectioal, populatio-based data. PloS oe. 7(): e958. Pope S. K., Sowers M., Welch G. W. ad Albrecht G.. Fuctioal limitatios i wome at midlife: the role of health coditios, behavioral ad evirometal factors. Wome's Health Issues, (6): Reilly J. J., Armstrog J., Dorosty A. R., Emmett P. M., Ness A., Rogers I.,... Sherriff A. 5. Early life risk factors for obesity i childhood: cohort study. Bmj. Rothma K. J. 8. BMI-related errors i the measuremet of obesity. Iteratioal joural of obesity. 3, S56-S59. Sedaaghi M. 9. A comparative study of geder ad age classificatio i speech sigals. Iraia Joural of Electrical ad Electroic Egieerig. 5(): -. Specht D. F. 99. Probabilistic eural etworks. Neural etworks. 3(): 9-8. Zhag Z. ad Li Y.. Recovery of the optimal approximatio from samples i wavelet subspace. Digital Sigal Processig. (5): Lee B. J., Ku B., Jag J.-S. ad Kim J. Y. 3. A ovel method for classifyig body mass idex o the basis of speech sigals for future cliical applicatios: a pilot study. Evidece-Based Complemetary ad Alterative Medicie. 565