A Neural Network System for Diagnosis and Assessment of Tremor in Parkinson Disease Patients

Transcription

1 A Neural Network System for Dagnoss and Assessment of Tremor n Parknson Dsease Patents Omd Bazgr*, Javad Frounch Department of Electrcty and Computer Engneerng Unversty of Tabrz Tabrz, Iran Omdbazgr92@ms.tabrzu.ac.r Seyed Amr Hassan Habb Department of Neurology of Rasool Akram Hosptal Iran Unversty of Medcal Scence Tehran, Iran habb.sah@ums.ac.r Lorenzo Palma, Paola Perleon Department of Informaton Engneerng Marche Polytechnc Unversty Ancona, Italy l.palma@unvpm.t Abstract Tremor s one of the most mportant symptom n Parknson s dsease, whch has been assessed clncally by neurologsts as part of UPDRS scale. In ths paper, we have mplemented a supervsed learnng pattern recognton system to assess UPDRS of each Parknson patent tremor to fll the absence of a relable dagnoss and montorng system for Parknson patents. In our system a smple nonnvasve method based on the recorded acceleraton through the smartphone have been used for data acquston. The results show hgh accuracy n the classfer block and neural network. A tght correlaton between UPDRS scale and acceleraton values reveals 91 percent accuracy by neural network wth two hdden layers. Index Terms Accelerometer, Tremor, UPDRS Scale, Neural Network, Pattern Recognton, Supervsed Learnng, Parknson s Dsease. I. INTRODUCTION Parknson s dsease (PD) s a neurodegeneratve chronc dsorder causng progressve loss of dopamne-producng bran cells, whch s the most wdespread llness after Alzhemer s dsease [1], [2]. The loss of dopamne n the mdbran often nduces characterstc motor symptoms: rgdty (ncrease of muscle tone that causes resstance to passve movement throughout the whole range of moton), tremor (nvoluntary rhythmc oscllatons of one or more body parts), bradyknesa (slowness of moton) and hypoknesa (decreased ampltude of moton) [3]. The predomnant method for evaluatng the status of PD patents, such as ther tremor, s the Unfed Parknson s Dsease Ratng Scale (UPDRS) [2]. By ths method neurologsts classfy PD patent s tremor from 0(absence of tremor) up to 4(Marked; nterferes wth most actvtes), clncally [3]. There are dfferent forms of tremulous movement n Parknson's dsease, the restng tremor, RT, and postural tremor, PT. RT can be dstngushed from other forms of tremor based on ts occurrence when the tremorng body part s completely supported aganst gravty wthout voluntary muscle contracton, n contrast to acton tremor, whch occurs wth voluntary muscle contracton. Postural tremor occur whle mantanng a posture aganst gravty [4]. The characterstc tremor of PD s undoubtedly the RT but t s extremely mportant to analyze the possble presence of other type of tremor as may be the PT [5]. The evaluaton and the dscrmnaton of severty of tremor related to UPDRS scale s an error prone task n actually clncal evaluaton and the determnaton of tremor severty requre the presence of at least one neurologst. The desgn of a system or an equpment able to assess tremor severty for PD patents s crucal. Advantages of usng ths equpment not only belongs to PD patents suffer tremor, whom do not have adequate access to neurologst through ther medcaton, but also for neurologsts wants to have remote montorng on ther patents, or make a comparson between dfferent methods of tremor medcaton n ther researches. The necessty of fndng better dagnoss method and extractng features through nonnvasve hand acceleraton or EMG sgnals procedure has been felt for recent years. In recent years pattern recognton structures has been gotten popular among researchers and a few results has been obtaned, such as, at el Lngme [6] whch he has desgned classfcaton algorthm based on hand acceleraton sgnal for Parknson and Essental tremor patents by usng EMD and DWT to extract features, and SVM n sequence of classfcaton block. Nowostawsk [7] has developed a smartphone-based applcaton that uses dscrete wavelet transforms and support vector machnes to dscrmnate Parknson s and Essental postural tremors wth over 96% of accuracy. Palmes [8], has developed an Instrumental Method (IM), machne learnng system, on the bass of tme doman features extracted from EMG sgnal, and SVM classfer by approprate kernel, to replace Clncal Method (CM) of dagnosng presence of dfferent knd of tremor. At el Palma [5], a real tme system by accelerometer devce for data acquston from PD patents wth ther related UPDRS score, STFT for sgnal processng block, frequency doman feature extracton, has been desgned to classfy Parknson patent s UPDRS level from 0 to 4 by defnng a severty parameter, K. At el Jakubowsk [9], on the bass of acceleraton values a neural network for tremor recognton among patents who suffer from Parknson, essental and physologcal tremor has been developed. Jakubowsk extracted features, manly on the bass of PSD, from STFT to separate each knd of tremor.

2 Fgure 1.Supervsed learnng pattern recognton structure whch have been used to assess tremor of Parknson patents II. METHODOLOGY A. Acquston System We have used a Sony Xpera SP smart phone, whch has a tr-axal accelerometer and gyroscope sensor wth 100 Hz samplng frequency for data acquston. Obvously, the devce could transfer the acqured data wth Wf and Bluetooth to PC as well as Internet network, the second faclty can be counted as an advantage for remote control of patent, and also data storage can be carry out locally va a mn SD card. By neurologst advce, n sequence of makng the devce wearable t nserted nto a bracelet ftted wth a strap that allows an easy use. The bracelet s made from elastc materal to be able to adapt to the dfferent patent body and to enable t to reman perfectly ntegral wth the lmb on whch t s placed avodng unwanted vbratons. The end result shown n Fg 2. To evaluate RT and PT we have appled two dfferent tests by neurologst confrmaton. For RT, patents seated on a char and putted ther restng arms wthout any contracton on ther thghs and the cellphone started to record the acceleraton values for one mnute. For postural tremor evaluaton, we asked patents to straght ther arm horzontally, parallel to ther thghs when they were seated on the char, durng ths stuaton the cellphone recorded the acceleraton values by 10 ml-second tme dfferences. All the tests n most of the cases done on both hands, n Rasoul Akram hosptal of Tehran, and neurologsts labeled each patent s UPDRS score for all states ndvdually. Fgure 2. Patent under Test, left sde rest tremor, rght sde postural tremor B. Algorthm Descrpton In order to desgn superor classfcaton algorthm, we propose general supervsed learnng pattern recognton structure n Fg.1. We found that by means of ths structure, feature extracton and classfcaton, precse result comes through for better dagnoss. Based on Fg 1 structure and prevous works, we have desgned an algorthm to classfy Parknson patent s tremor by UPDRS scores. The effcency and accuracy of our algorthm depends on selectng nner elements of each block, hence, testng dfferent elements of each block ndvdually to obtan best performance s nevtable. Therefore the algorthm has been desgned by followng structure. 1) Sgnal Flterng Block: In order to elmnate noses and other undesred frequency bands, determned based on earler works [5, 8] on Parknson tremor, we appled two band pass FIR equrpple flter, n three frequency bands, 3-6 Hz for RT and 6-9 Hz n case of PT. 2) Processng Block: Tremor sgnals contan numerous non-statonary or transtory characterstcs such as drft, trends, and abrupt changes [3]. These characterstcs are often the most mportant part of a sgnal lke tremor, as a result we have chosen a STFT block for wndowng the acceleraton sgnal durng the process and separatng tremor wndows and non-tremor wndows. The Hammng wndow of STFT block has 4 seconds length wth 50% overlap, these features of STFT block are selected based on [5] and further attempts to obtan best result n separatng tremor and non-tremor wndows. 3) Feature Extracton Block: Two major features whch s mentoned n most of earler works for not only objectve classfcaton of Parknson tremor but also other types of tremor, are frequency and ntensty of tremor. The hand tremor s a perodc oscllaton [5], hence the frequency analyss s performed by means the extracton of the fundamental frequency through whch classfy the type of tremor. The extracton of the fundamental frequency s essental for tremor classfcaton. Dependng on the detected frequency n frst block, we can dstngush fundamental tremor types. The basc types of tremor can be classfed on the bass of the prevous subdvson, whch has been done thanks to flterng block: RT (3-6 Hz), PT (6-9 Hz). The ampltude of hand tremor oscllaton s the second fundamental dstngushable characterstc, objectve causes of dagnoss tremor for neurologsts, whch s determned for essental tremor patents through logarthmc relatonshp by consderng TRS method n Rodger s work [10]. On the bass of earler works whch s mentoned above, the dea of tremor scale ampltude as a classfcaton factor, s to calculate the power spectral densty functon whch ndcates the sgnal power at dfferent frequences across the spectrum. The domnant frequency of tremor s evdent from a vsble peak n the power spectral densty, whle the average tremor ampltude can be determned from the area under the peak [5], [11]. In order to obtan a scale of PSD, an exact number n consequence of process, for each wndow of patent s accelerometer sgnal, we have calculated PSD weghted average mean absolute value n each frequency band based on (1):

3 Mean _ PSD Fsp Fst Fsp P f f Fst (1) In (1) Fst, s a symbol Start frequency of each band, for nstance start frequency of RT band s 3 Hz so the Fst equals to 3, smlarly, Fsp s stop frequency and for RT patents t s 6 Hz., s symbol of frequency steps between Fst and Fsp based on samplng frequency. P and f are power and frequency correspondent to value. Each case s dfferent from the other, hence montorng all patents whch belong to same UPDRS class s necessary to fnd the smlartes and dfferences. Result of our observaton showed us, for patents who suffer tremor wth less severty, UPDRS0, up to hgher level of severty, UPDRS4 the wdth of frequency spectrum whch s contaned fundamental frequency and peak value of PSD s gettng narrower. As a result, same as [5], usng SF50 and F50 for better comparson and correlaton s nevtable. F50 s the frequency at whch half power frequency lyng on the left of ths whle the other 50% s on the rght. It provdes an ndcaton of the power dstrbuton nto the consdered band. SF50 represents the frequency band wthn whch the 68% of the total power of the sgnal s contaned. Because SF50 s centered on F50, t s far to say that SF50 represents the dsperson of the frequency around the central frequency [5]. In some of the patents the value of F0, fundamental frequency, and F50 was not same, hence we calculated dfference of them as a feature. 4) Feature Selecton Block: Our am of mplementng ths block s usng best technque for the selecton of a subset of features from a larger avalable features that contaned chef dscrmnatve nformaton adhere to the classfcaton problem at hand. Another vtal pont n feature selecton s choosng the number of features l to be used out of an orgnal m l. Reducng ths number s n a queue wth our am of evadng over fttng to the specfc tranng data set and of desgnng classfers that result n good generalzaton performance that s, classfers that perform well when encountered wth test data set totally outsde the tranng set. Accordng to exhaustve search technque, all possble combnatons wll be exhaustvely formed and for each combnaton ts class separablty wll be computed [12]. In exhaustve technque type of classfer has to be defned n consequence of comparng the results, obvously usng ths technque ncrease the accuracy of classfer but also ncrease the computatonal cost, because of the feedback lnk between ths block and classfer block. The computatonal cost does not matter n ths problem, because the mean of mplementng of ths block s reducng dmenson by ncreasng the accuracy. 5) Classfcaton Block: The last block and bottle neck of the classfcaton systems s ther classfer block, whch proper chose of t has major role n obtanng defensble accuracy of system. Due to the presence of feedback connectons n neural network from the output to the context nodes as well as consderng nput dmenson of classfer, the robust changes n values are reflected and a hgh accuracy of classfcaton can be possbly acheved, we have chosen an approprate structure of ANN, Artfcal Neural Network. The goal of tranng a multlayer perceptron s to estmate the weghts, as well as the threshold values, of all neurons nvolved n the network. To ths end, an error functon s chosen (2). In (2) ŷ denotes the output of the network when ts nput s fed wth x, the goal s to compute the unknown weghts so that J N 1 ( y yˆ ) (2) We have made J mnmum. The algorthmc scheme for performng the prevous mnmzaton s back propagaton (BP) algorthm, the algorthm runs several tmes, startng from dfferent ntal values; the weghts correspondng to the best soluton are chosen. In back propagaton algorthm, the prevous and current value of weghts, w(old ) and w (new), wll estmate n each teraton step, through the relatonshp whch s defned n (3): w( new) w( old ) w (3) The correcton term s related to the gradent of the cost, computed at w (old ) : J (4) w And w refers to the weght parameters of a network neuron. The behavor of the algorthm largely depends on the value of the learnng rate μ. Ths should be small enough to guarantee convergence of the algorthm, but not too small, snce such a choce may lead to very slow convergence rates [12]. In ths paper, we used the back propagaton algorthm wth momentum term, because ths term can controls the correcton better, by ncreasng the learnng rate through (5) [15]: J w( new) w( old ) (5) w As has been proved n [16], the network should possess as low number of weghts as needed to reduce the error functon to the approprate low level and at the same tme the number of learnng samples should be as hgh as possble. III. RESULTS The set of extracted features at the realzatons of the Parknson tremor assessment on the bass of UPDRS scale

4 processes, at many dfferent patents has been used as the nput vector to the neural network classfer. The ntal dmenson of the nput vector s equal to the number of extracted features. The number of output neurons was set to fve,.e., the number of under recognton classes. Each class s represented by the unty value sgnal of the output neuron. The Parknson tremor wth dfferent type of UPDRS scales was assocated wth the destnaton vector as mentoned n Table 1. Two hdden layer of sgmodal neurons has been appled n the network. The number of hdden neurons has been tuned heurstcally to obtan the hghest effcency n the retreval mode. Too hgh number of hdden neurons causes bad generalzaton of the network, whle too low number creates neffectve learnng process and brngs t on too hgh level of error. We have adjusted ths number heurstcally by learnng dfferent networks and choosng the smallest number of hdden neurons, suffcent to reduce error functon to the satsfactorly small level. All data characterzng the Parknson tremors have been splt nto learnng and testng parts. 787 expermental patterns whch s extracted from 43 Parknson patents, obtaned at the nvestgaton of the patents of the dsablty movement neurologcal center of Hazrat Rasoul Hosptal, have been used n tranng, and further 201 patterns of 9 totally dfferent Parknson patents only n testng mode. To fnd out the best network structure dfferent numbers of hdden neurons have been employed for tranng the algorthm. All traned networks have been tested by test data, whch s not takng part n tran data. The network of the smallest possble number of hdden neurons and combnaton of extracted features, provdng the smallest value of the testng error has been selected as the optmal one. The reducton of the number Table 1.The assgned destnaton vector of ANN output to each UPDRS Scale UPDRS Scale Destnaton vector/ ANN output UPDRS UPDRS UPDRS UPDRS UPDRS of nputs means the smplfcaton of the Neural Network structure and the ncrease of ts dscrmnaton ablty. The features whch have very good ndvdual dscrmnatve ablty are not necessarly best n the set together wth other features [9]. Consequently, the resultng neural network of reduced complexty has been once agan traned and tested. The best combnaton set of features whch have most dscrmnatory characterstc, and the effcency parameter of the classfcaton system s mentoned n the Table2. The fnal structure of neural network as a classfer has 4 nput, 2 hdden layer wth 5 neuron whch s fed nto sgmod functon. In earler works, researchers has desgned ther classfcaton systems to dscrmnate dfferent types of tremor, but The am of mplementaton of our system s provdng hgh accuracy assessment specfcally among Parknson patent s tremor whch could be consdered as a complement of prevous works for classfcaton of tremor. I. CONCLUSION The developed system s very approprate for dagnoss and remote control of neurodegeneratve movement dsorder dseases, especally n ntal phase of the dsease, such as Parknson s both ambulatory and home montorng. Our work s based on set of algorthms for obtanng an objectve classfcaton of tremor and a quantfcaton of ts severty accordng to UPDRS Scale. The proved correlaton wth the UPDRS allows us to have a report objectvely and unversally recognzed for the evaluaton of patents wth PD. In foremost related studes lke [6,8 and 9], researchers focus was on classfcaton of dfferent type of tremor such as Parknson, essental and physologcal. In ths work we have desgned our classfcaton system based on Parknson patent s tremor whch s rarely has been done wth a vald and acceptable result. Aora et al [13], has presented a statstcal method utlzng only 10 Parknson s patents to assess ther tremor based on UPDRS method. They recorded ther tremor wth tappng on the screen of a smartphone. They acheved to a hgh accuracy, usng randomly 90% percent of acqured tremor sgnal as a tran data set and rest of t as test data set. The 96.2% senstvty and 96.9% specfcty percentage n Aora s paper reported n ths paper, we recruted tremor sgnal of 43 Parknson s patents as a tran data, and 9 other Parknson s patents for test data, whch ndcate more valdty. We also recorded patent s tremor by accelerometer sensor of a smartphone whch s more acceptable among neurologsts n comparson wth screen sensor. Set of features PSD,SF50, F50,F0 Table 2. The output result of desgned ANN Relatve Error Accuracy Senstvty Specfcty 2.5% 91 % 89.6% 90.64% Our next am s mplementaton of the system on an embedded electronc board such as ARM or FPGA, whch s able to read the recorded sgnal on smart phone SD card, n order to evaluate feasblty of producng portable devce for patents and neurologst. Ths user frendly and practcal system can be convenent for both patents and neurologsts, remote control could be appled on Parknson patents. Further mprovements for the presented work wll be the complaton of more UPDRS factors and the ncluson of other

5 symptoms of Parknson, such as rgdty or dysknesa wll be pursued. The desgned system wll make home montorng possble for patents wth Parknson s dsease, authorzng neurologsts to calbrate drug therapy based on a long-term observaton rather than a smple outpatent vst. II. REFERENCES [1] Dorsey ER1, Constantnescu R, Thompson JP, Bglan KM, Holloway RG, Keburtz K, Marshall FJ, Ravna BM, Schftto G, Sderowf A, Tanner CM, "Projected number of people wth parknson dsease n the most populous natons," Neurology, pp , [2] Schwarz J1, Odn P, Buhmann C, Csot I, Jost W, Wüllner U, Storch A., "Depresson n Parknson's dsease," J Neurol, vol. 2, no. 258, pp , [3] K. Nazmand, K. Tonn, A. Kalaras, U. M. Fetzek, J.-H. Mehrkens, and T. C. Lueth, "Quanttatve evaluaton of parknson s dsease usng sensor based smart glove," Computer-Based Medcal Systems (CBMS) 24th Internatonal Symposum on. IEEE, p. 1 8, [4] Chrstopher W. Hess & Seth L. Pullman, Tremor: Clncal Phenomenology and Assessment Technques, New York, Unted States of Amerca: Clncal Motor Physology Laboratory, Department of Neurology, [5] Paola Perleon, Lorenzo Palma, Alberto Bell and Luca Pernn, "A real-tme system to ad clncal classfcaton and quantfcaton of tremor n Parknson s Dsease," n Bomedcal and Health Informatcs(BHI),IEEE, Valenca, [6] Lngme A,JueWang, RuoxaYao, "Classfcaton of parknsonan and essental tremor usng emprcal mode decomposton and support vector machne," Dgtal Sgnal Processng, Elsever, vol. 21, no. 4, p , [7] Alan Mchael Woods, Marusz Nowostawsk, Elzabeth A. Franz, Martn Purvs, "Parknson s dsease and essental tremor classfcaton on moble devce," Pervasve and Moble Computng,Elsever, vol. 13, pp. 1-12, [8] Paulto Palmes, We Tech Ang, Ferdnan Wdjaja, Lous CS Tan, and Wng Lok Au, "Pattern Mnng of Multchannel semg for Tremor Classfcaton," IEEE Transactons on Bomedcal Engneerng, vol. 57, no. 12, pp , [9] Jacek Jakubowsk, Krzystof Kwatos, Augustyn Chwaleba, and Stanslaw Osowsk, "Hgher Order Statstcs and Neural Network for Tremor Recognton," IEEE Transactons on Bomedcal Engneerng, vol. 49, no. 2, pp , [10] Rodger J. Elble, Seth L. Pullman, Joseph Y. Matsumoto, Jan Raethjen, Gunther Deuschl, Ron Tntner, "Tremor ampltude s logarthmcally related to 4- and 5-pont tremor ratng scales," Bran, vol. 29, pp , [11] R.J. Elble, R. Snha, and C. Hggns, "Quantfcaton of tremor wth dgtzng tablet," J. Neuroscence Methods, vol. 32, pp , [12] Theodords, Sergos, Aggelos Pkraks, Konstantnos Koutroumbas, and Donss Cavouras, Introducton to Pattern Recognton: A Matlab Approach, Burlngton, USA: Elsever, [13] S. Aora, V. Venkataraman, S. Donohue, K.M. Bglan, E.R. Dorsey, M.A. Lttle, "Detectng and Montorng the symptoms of Parknson's dsease usng smart phones : a plot study," Parknsosms & Related Dsorders, vol. 21, no. 6, p , 2015.