A NOVEL FUZZY NEURAL NETWORK ESTIMATOR FOR PREDICTING HYPOGLYCAEMIA IN INSULIN-INDUCED SUBJECTS

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1 Proceeding 3rd Annual Conference IEEE/EBS Oct.5-8, 1, Itanbul, TURKEY A NOVEL FUZZY NEURAL NETWORK ESTIATOR FOR PREDICTING HYPOGLYCAEIA IN INSULIN-INDUCED SUBJECTS N. Ghevondian 1, H. T. Nguyen 1, S. Colagiuri 1 Key Univerity Reearch Strength in Health Technologie, Univerity of Technology, Sydney, NSW, AUSTRALIA Department of Endocrinology, Diabete and etabolim, Prince of Wale Hopital, Randwic, NSW, AUSTRALIA Abtr- Predicting the onet of hypoglycaemia can avoid maor health complication in Type 1 inulin-dependent-diabetemellitu (IDD) patient. Thi paper decribe the deign of a novel fuzzy neural networ imator algorithm (FNNE) for predicting the glycaemia profile and onet of hypoglycaemia in inulin-induced ubect, by modelling the change in heart rate and in impedance parameter. Hypoglycaemia wa induced briefly in 1 volunteer (group A: 6 non-diabetic ubect and group B: 6 Type 1 IDD patient) uing inulin infuion. Their in impedance, heart rate and ual blood glucoe level () were monitored at regular interval. The FNNE algorithm wa trained uing all ubect from group A and validated/ted on the remaining ubect from group B. The mean error of imation of profile for the training data et (group A) wa.17 (ρ <.5) and for the validation/t data et (group B) wa.139 (ρ <.5). Furthermore, the FNNE algorithm wa able to predict the onet of hypoglycaemia epiode in group A and group B with a mean error of.71 (ρ <.3) and.176 (ρ <.5) repectively. Keyword - Hypoglycaemia, heart rate, in impedance I. INTRODUCTION For patient with Type 1 inulin-dependent-diabetemellitu (IDD), hypoglycaemia i a frequent and evere complication [1]. Introduction of intenive therapy with continuou ubcutaneou inulin infuion pump and frequent daily inection ha led to an increae in hypoglycaemia epiode []. Pat tudie have hown that intenive therapy can lower the glucoe tehold for neurogenic warning ymptom, which epoe IDD patient to further ri aociated with evere hypoglycaemia [3]. The glycaemic level within the human body reflect onto the ymptomatic change of certain phyiological parameter [-5]. The mot profound phyiological diturbance are caued by the ivation of the ympathetic nervou ytem, which reflect tough to the parameter uch a weating and cardiovacular ytem repone. Sweating i primarily due to cholinergic ympathetic ivity [6] and cardiovacular ytem repone i due to the increae in heart rate and troe volume [7]. A a reult certain eperiment have been undertaen to analye the correlation between the meaured parameter and blood glucoe level () within the body [8-9]. However, no attempt have been made to imate profile or the everity of hypoglycaemia baed on the change in the meaured parameter. Thi paper decribe the deign of a fuzzy neural networ imator (FNNE) algorithm which i ued for predicting glycaemic profile and hypoglycaemia epiode in inulininduced ubect. Thi FNNE algorithm only ue heart rate and in impedance a ytem input. It comprie of a parallel fuzzy inference engine and a multi-layered neural networ ytem with trainable weight matri for tranforming thee meaured phyiological parameter into imated blood glucoe level. II. ETHODOLOGY Twelve ubect, 6 non-diabetic (group A) and 6 Type 1 IDD (group B) ubect aged 6 ± 3 year, volunteered for thi tudy after giving informed conent. The ample population conited of 6 male and 6 female. The tudy wa approved by the local Ethic Committee. All ubect were non-obee a aeed by their height: 1.69 ±.8 m, weight: 68 ± 1 g, and body urface area of 1.7 ±.3 m. The eperiment were conducted in morning period at the Diabete Centre, Prince of Wale Hopital clinical reearch laboratory. All ubect were required to fat from midnight. Two et of in urface electrode, which interfaced directly to the meauring intrument, were attached to the ubect to meaure the phyiological parameter, heart rate and in impedance [1]. Intravenou Teflon cannulae were inerted into a vein in each forearm and ept patent uing a aline drip. The cannula on the right forearm wa ued for intravenou infuion of inulin (Actrapid, Novo Nordi) et at a rate of mu/g/hour. Thirty minute before commencing inulin infuion, baeline meaurement of were collected at 15-min. interval. At the end of thi 3-min. baeline phae, the inulin infuion commenced at a flow rate of 1 ml/, maring the beginning of the data-logging phae for a duration of 9 min. During thi phae, wa collected at 5-min. interval while the intrument logged at 1min interval. A. Initial data proceing III. ALGORITH DESIGN The meaured phyiological parameter (heart rate and in impedance) were firt normalied to enhance the crocorrelation of the algorithm and to counter varying environmental condition which could affect the meaurement proce. The proceing unit then collated the meaured heart rate and in impedance parameter (meaured at 1 ample per min.) with ual meaured data (meaured at 1 ample per 5 min.), uing a weighted average method. A a reult, a generalied data matri et Y which conit of heart rate wa formed: in impedance i and meaured /1$1. 1 IEEE

2 Report Documentation Page Report Date 5 Oct 1 Report Type N/A Date Covered (from... to) - Title and Subtitle A Novel Fuzzy Neural Networ Etimator for Predicting Hypoglycaemia in Inulin-Induced Subect Contr Number Grant Number Program Element Number Author() Proect Number Ta Number Wor Unit Number Performing Organization Name() and Addre(e) Key Univerity Reearch Stength in Health Technologie Univerity of Technology Sydney, NSW, Autralia Sponoring/onitoring Agency Name() and Addre(e) US Army Reearch, Development & Standardization Group (UK) PSC 8 Bo 15 FPO AE Performing Organization Report Number Sponor/onitor Acronym() Sponor/onitor Report Number() Ditribution/Availability Statement Approved for public releae, ditribution unlimited Supplementary Note Paper from 3rd Annual International Conference of the IEEE Engineering in edicine and Biology Society, October 5-8, 1, held in Itanbul, Turey. See alo AD1351 for entire conference cd-rom., The original document contain color image. Abtr Subect Term Report Claification unclaified Claification of Abtr unclaified Claification of thi page unclaified Limitation of Abtr UU Number of Page

3 Proceeding 3rd Annual Conference IEEE/EBS Oct.5-8, 1, Itanbul, TURKEY Y = [ ] i = n1 1 n n3 ( n3) where n correpond to the population ize i.e. for the th ubect ( = 1, 1). B. Fuzzy neural networ imator (FNNE) The FNNE algorithm i baed on a parallel combination of fuzzy inference mechanim (FI) and a multi-layered neural networ (NN) architecture with trainable weight matri to tranform the meaured phyiological parameter, (1) and i into an imated profile, for each ubect ( = 1, 1). Thi FNNE algorithm benefit from the linguitic rule modelling behaviour of the FI and the trainable chareritic of the multi-layered NN algorithm. It architecture i hown in Figure 1. The core principle of the FNNE algorithm i baed on the firt-order Sugeno fuzzy model [11-1] where the FI i compiled on a et of N fuzzy if-then rule. Thee rule are mainly derived uing a determinitic approach with medical advice by epert in the field of diabete tudy. The th rule ha the following format: Rule : if 1 i VH and i BN then 3 = f ( 1, ) () where VH (Very High) and BN (Below Normal) are the linguitic variable aociated with the fuzzy ubet of 1,, and f ( 1, ) = i the reulting firt-order conequent polynomial function for calculating the th imated value, 3 (from data et). The reulting FNNE algorithm produce (tough the training proce of the firt and econd phae) a olution for all N polynomial function coefficient (, 1,, for = 1,..,N), collated into a coefficient matri Β, repreented a: Β coeff = (3) 1 N N1 N ( N 3) where the th row [ ] = 1 i the coefficient data et for the th polynomial function f ( 1, ). Layer 1 conit of the fuzzification layer which map the th (= 1, n) normalied meaured heart rate value 1, and in impedance value within, i correponding new fuzzy et [13]. th ubect th input parameter 1, 3 th ual value ( = 1,, 1) into (layer 1) (layer ) (layer 3) Fuzzification AND operator Normaliation (product) (layer ) Neural networ layer 3 Fig. 1: FNNE bloc diagram (layer 5) Neuro-fuzzy conequence (layer 6) Output layer 3 th ubect th imated Layer perform the AND operation on all correponding linguitic variable (derived by fuzzification) aociated with each if-then rule. Layer 3 normalie each firing trength node from layer, baed on the um of all rule (N rule). The neural networ layer (layer ) perform the feedforward networ proce of the th value 1 and, together with the ual th value 3 a feedbac error. Uing thi neural networ tructure, layer ue all N imated polynomial function coefficient (, 1,, for = 1,..,N) to produce N output neuron which will be proceed by the neuro-fuzzy conequence layer (layer 5). Layer 5 i the central layer within the FNNE architecture. Thi layer perform the neuro-fuzzy conequence operation where each output i the product of the normalied firing trength (output from layer 3) by the correponding output neuron from the neural networ layer (layer ). Finally, the output layer (layer 6) provide the th imated output, 3, by umming the all neuro-fuzzy conequence output node generated by layer 5. Uing the general delta-learning rule [1], during each qth iteration, the coefficient within Β coeff are updated in uch a manner to minimie the overall th error E = 1/ ( 3 3 ). Uing the teep decent technique, thee coefficient are updated according to: q+ 1 q = η E ( ) () where η i a poitive learning contant, E ( ) i the th error gradient vector and = [ 1 1] i the th obervation within Y ( = 1,, 6) (training uing group A only). At the end of each qth iteration, the validation data et, i and ( = 7, 8) in group B are ued to determine the cycle error (imated 3, with repect to

4 Proceeding 3rd Annual Conference IEEE/EBS Oct.5-8, 1, Itanbul, TURKEY the meaured 3 ) and it rate of convergence. The trained and optimied FNNE algorithm i applied to the remaining ubect within group B ( = 9,1 1) with no further training to provide formal ting. IV. RESULTS A. Phyiological parameter repone The profile of both group A and B are hown in Figure. In the baeline phae (-3 to min interval), the mean and ± tandard deviation (SD) for group A and B were.3 ±. mmol/l and 1.8 ± 1. mmol/l repectively. The minimum value reached by group A wa 1.93 ±. mmol/l, while the minimum value reached by group B wa 1.5 ±.5 mmol/l within the time pan of 6 minute. Heart rate (bpm) Sin impedance (Ω) Actual (mmol/l) Inulin group A group B Time (min) Fig. : eaured phyiological parameter profile The ring heart rate at baeline phae for group A and group B had a mean of 6 ± 1 bpm (range 51 7 bpm) and 7 ± bpm (range 63 9 bpm) repectively. During the evere hypoglycaemia phae, the heart rate for group A increaed to a mean of 73 ± 7 bpm (range 63 8 bpm, mean increae of 11 bpm). Similarly, the heart rate for group B increaed to mean of 95 ± 15 bpm (range bpm, mean increae of 1 bpm). The in impedance at baeline phae for group A and group B had a mean of ± 96 ohm (range ohm) and 17 ± ohm (range ohm) repectively. During evere hypoglycaemia epiode, the in impedance for group A decreaed to a mean of 76 ± 135 ohm (range 1 9 ohm). Similarly, the in impedance for group B decreaed to a mean of 36 ± 176 ohm (range ohm). B. FNNE algorithm repone The FNNE algorithm wa trained uing the data et of all ubect from group A ( = 1, 6). The validation procedure wa determined uing the data obtained in the firt two group B ubect ( = 7,8) and ting wa conducted on the remaining four group B ubect ( = 9,1 1). Figure 3 repreent the combined correlated data et of all ubect in group A and B (ample ize n of ). The olid linear line repreent the correlation of imated veru ual data et, and the two dahed line repreent the ± 5% deviation from thi correlation. The correlation of group A and group B produced an overall R of (mmol/l) n = R = (mmol/l) Figure 3: Combined correlated plot All ubect from group A demontrated high correlation of imated (the mean multiple correlation coefficient, R =.986) with repect to ual profile, ρ <.5 (mean lielihood probability). The overall mean error of imation ε and a mean error of imation for hypoglycaemia ( <.5 mmol/l) ε hypo for group A were.17 (ρ <.5) and.71 (ρ <.3) repectively. The imation profile of all ubect from group B correlated within the tatitical confidence interval =.979 (ρ <.5). The overall ε and R ε hypo for group B were.139 (ρ <.5) and.176 (ρ <.5). Table 1 ummarie the overall performance of the FNNE algorithm for both, group A and group B, including the multiple correlation coefficient, the imated value at hypoglycaemia and it time lead/ lag. IV. DISCUSSION Thi paper ha introduced a novel fuzzy neural networ imator (FNNE) algorithm for modelling the non-linear correlation between meaured phyiological parameter and the ual profile. Thi algorithm i baed on a et of firtorder imation function for imating the profile and conequently, detecting the onet of hypoglycaemia epiode. Thee imation function were trained uing controlled data et of non-diabetic ubect (group A), and validated/ted uing data et obtained from Type 1 IDD ubect (group B).

5 Proceeding 3rd Annual Conference IEEE/EBS Oct.5-8, 1, Itanbul, TURKEY Group A Group B TABLE I FNNE ALGORITH PERFORANCE SUARY Subect R ε (hypo) mmol/l ε hypo t hypo (min) N/A Analying the tatic behaviour of the meaured phyiological parameter of all twelve ubect, a ignificant correlation wa oberved with repect to the meaured profile, ρ <.1. From the tranition of normal glycaemic level ( above 3.5 mmol/l) to hypoglycaemia phae ( below.5 mmol/l), the mean heart rate of group A and group B increaed by 11 and 1 bpm repectively. Similarly, the mean in impedance of group A and group B decreaed by 1 and 111ohm repectively. However, the variance of the meaured phyiological parameter and value for group B were ignificantly larger than group A. Thi effect i primarily due to the irregular glucoe counterregulatory preent in Type 1 IDD ubect ytem [1]. The FNNE wa trained within 3 iteration of the training data et baed on the convergence rate of the validation data et (ubect 7 and 8). The overall mean error of imation ε and mean error of imation of hypoglycaemia ε hypo for group A were.17 (ρ <.5) and.71 (ρ <.3) repectively. The trained firt-order imation function obtained from group A wa validated and ted uing group B data et. All group B ubect demontrated good correlation of imation ( R =.979), particularly below the 5-min. interval, ρ <.55. The overall ε and ε hypo for group B were.139 (ρ <.5) and.176 (ρ <.5) repectively. The prediction time of hypoglycaemia varied from ubect to ubect with a mean prediction period of 9.7 ± 6.1 minute of ual onet. V. CONCLUSION In order to improve the accuracy of the algorithm for imating the profile, further eperiment are recommended, epecially in the hypoglycaemia tage. Changing the firt-order imation function to higher order function (e.g. quadratic) may improve imation and provide better fitting equation. The ize of the coefficient matri will need to increae to accommodate higher-order function. Increaing the number of imation function can improve the ocillation effect oberved within group B. Other method for improving imation may include larger training and validating ampling data et, adaptive training of memberhip function and ue of Hopfield neural networ architecture to compenate for large time delay due to autonomic ytem repone. By addreing thee iue with on-going reearch, an improved imation procedure can be accomplihed and implemented for the non-invaive detection of evere hypoglycaemia for patient with Type 1 diabete. REFERENCES [1] P. E. Cryer, C. Binder and G. B. Bolli, Hypoglycaemia in IDD, Diabete, vol. 38, pp , [] Diabete Control and Complication Trial (DCCT) Reearch Group, The effect of intenive treatment of diabete on the development and progreion of long-term complication in IDD, N. Eng. J. ed., vol. 39, pp , [3] S. A. Amiel, W. V. Tamborlane, D. C. Simonon and R. S. Sherwin, Defective glucoe counterregulation after trict glycaemic control of IDD, N. Eng. J. ed., vol. 316, pp , [] E. A.. Gale, T. Bennett, I. A. acdonald, J. J. Holt and J. A. atthew, The phyiological effect of inulin-induced hypoglycaemia in man. Repone at differing level of blood glucoe, Clin. Sci., vol. 65, pp , [5] S. R. Heller and I. A. acdonald, Phyiological diturbance in hypoglycaemia: effect on ubective awarene, Clin. Sci., vol. 81, pp. 1-9, [6] H. Uno, Sympathetic innervation of the weat gland and piloarrector mucle of macaque and human being, J. Inv. Dermatol, vol. 69, pp. 11-1, [7] B. Arner, P. Hender, T. Karlefor and H. Wling, Haemodynamic change and adrenal function in man during induced hypo, Acta Endocrinal., vol., pp. 3-31, [8] R. J.. Corrall, B.. Frier, N. c.d. Davidon, W.. Hopin and E. B. French, Cholinergic manifation of the acute autonomic reion to hypogycaemia in man, Clin. Sci., vol. 6, pp. 9-53, [9] N. D. Harri, S. B. Bayouchev and J. L. arque, A portable ytem for monitoring phyiological repone to hypo. J. ed. Eng. & Tech., vol., pp. 196-, [1] N. Ghevondian and H. T. Nguyen, Low power portable monitoring ytem of parameter for hypoglycaemic patient, in Proc. 19th Ann. Int. Conf. IEEE Eng. in ed. Biol. Soc., pp , [11]. Sugeno and G. T. Kang, Structure identification of fuzzy model, Fuzzy Set and Sytem, vol. 8, pp , [1] T. Taagi and. Sugeno, Fuzzy identification of ytem and it application to modelling and control, IEEE Tran. On Sytem, an and Cybernetic, vol. 15, pp , [13] J. Yan,. Ryan and J. Power, Uing Fuzzy Logic. Prentice Hall, 199. [1] J.. Zurada, Introduction to Artificial Neural Sytem. W, 199.