E-Book, Englisch, Band 62, 806 Seiten, eBook
Reihe: IFMBE Proceedings
Badnjevic CMBEBIH 2017
1. Auflage 2017
ISBN: 978-981-10-4166-2
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
Proceedings of the International Conference on Medical and Biological Engineering 2017
E-Book, Englisch, Band 62, 806 Seiten, eBook
Reihe: IFMBE Proceedings
ISBN: 978-981-10-4166-2
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
1;Preface;6
2;Organization;8
3;Contents;12
4;Plenary Lectures I - Session I:BIOMEDICAL SIGNAL PROCESSING 1;21
5;1Using machine learning tool in classification of breast cancer;22
5.1;Abstract.;22
5.2;Keyword;22
5.3;1 Introduction;22
5.4;2 Methods;23
5.4.1;2.1 Wisconsin Breast Cancer Database (WBCD;23
5.4.2;2.2 Design of Artificial Neural Network for BreastCancer Classification;23
5.5;3 Results and discussion;24
5.5.1;3.1 Predictive ability of neural network architecturewith different number of hidden layers;24
5.6;4 conclusion;25
6;2MULTISAB project: a web platform based on specialized frameworks for heterogeneous biomedical time series analysis - an architectural overview;28
6.1;Abstract.;28
6.2;Keywords:;28
6.3;1 Introduction;28
6.4;2 Processing frameworks;29
6.4.1;2.1 Common signal features framework;29
6.4.2;2.2 Data handling framework;30
6.4.3;2.3 Specialized biomedical time series analysisframeworks;30
6.5;4 Signal visualization;31
6.6;3 Database;31
6.7;5 Discussion and conclusion;33
6.8;Conflict of Interest;33
6.9;Acknowledgements;33
6.10;References;34
7;3Short-term variations of parameters of heart rate variability in subjects with mild hypertension and normotensive subjects during preoperative period;35
7.1;Abstract;35
7.2;Introduction;35
7.3;Methods and subjects;35
7.4;Keywords;35
7.5;Results;36
7.6;Discussion;37
7.7;Conclusion;37
8;4Cardiac pulse waves modeling and analysis in laser Doppler perfusion signals of the skin microcirculation;39
8.1;Abstract.;39
8.2;1 Introduction;39
8.3;2 Materials and methods;39
8.3.1;2.1 Dataset;39
8.3.2;2.2 Experimental setting;39
8.3.3;2.3 Gaussian-based cardiac pulse waves modeling;40
8.4;3 Results;42
8.5;4 Conclusion;43
8.6;Acknowledgement;44
8.7;References;44
9;5Discrimination of Psychotic Symptoms from Controls Through Data Mining Methods Based on Emotional Principle Components;45
9.1;Abstract.;45
9.2;Keywords:;45
9.3;1 Introduction;45
9.4;2 Methods;46
9.4.1;2.1 Data Collection;46
9.4.2;2.2 Feature Extraction;46
9.4.3;2.3 Data Mining Methods for Emotion Recognition;46
9.4.4;2.4 Performance Criteria;47
9.5;3 Results;47
9.6;4 Discussion and Conclusion;48
9.7;Acknowledgements;48
9.8;References;48
10;6Differences in temporal gait parameters between multiple sclerosis and healthy people;50
10.1;Abstract.;50
10.2;Keywords:;50
10.3;1 Introduction;50
10.4;2 Methods;51
10.5;3 Results;52
10.6;4 Summary and conclusions;53
10.7;References;54
11;7An Adaptive Scheme for X-ray Medical Image Denoising using Artificial Neural Networks and Additive White Gaussian Noise Level Estimation in SVD Domain;55
11.1;Abstract.;55
11.2;Keywords:;55
11.3;1 Introduction;55
11.4;2 Singular r Value Dec composition n and NoiseLevel E stimation;56
11.5;3 O verview of M Multilayer P Perceptron;56
11.6;4 Adaptive Image Denoising;57
11.7;5 Results and Discussion;57
11.8;6 Conclusion;59
11.9;References;59
12;8Using Neural Networks and Ensemble Techniques based on Decision Trees for Skin Permeability Prediction;60
12.1;Abstract.;60
12.2;Keywords:;60
12.3;1 Introduction;60
12.4; 2 Methods;62
12.5;2.1 Identification of Parametes for Prediction of drug permeability;62
12.6;2.2 Artifical Neural Network for prediction of drug permeability;62
12.7;2.3 Ensemble techniques based on Decision Trees forprediction of skin permeability;63
12.8;2.4 Dataset Distribution;64
12.9;3 Result;64
12.10;3.1 Predictive ability of nerual network arhitecturs with different number of neurous in hidden layer;64
12.11;3.2 Predictive ability of nerual network arhitecturs with different Distribution of Dataset;65
12.12;3.3 Predictive ability of nerual network for prediction of drug permeability;65
12.13;3.4 Predictive ability of REPTree, Bagging, andRandom SubSpace;66
12.14;3.5 Comparison of ANN and Ensemble Techniques;66
12.15;4 Conclusion;66
12.16;References;67
13;Plenary Lectures I - Session II:BIOMEDICAL IMAGING AND IMAGE PROCESSING;70
14;9Fully Automated Brain Tumor Segmentation and Volume Estimation Based on Symmetry Analysis in MR Images;71
14.1;Abstract.;71
14.2;Keywords:;71
14.3;1 INTRODUCTION;71
14.4;2 MATERIAL AND METHODS;72
14.5;3 RESULTS;77
14.6;4 CONCLUSION;78
14.7;REFERENCES;78
15;10Multi-Regional Adaptive Image Compression (AIC) for Hip Fractures in Pelvis Radiography;79
15.1;Abstract.;79
15.2;Keywords:;79
15.3;INTRODUCTION;79
15.4;METHODOLOGY;80
15.5;1 Specifying Regions of Interest;80
15.6;2 Lossless Compression;81
15.7;3 Lossy Compression;82
15.8;EXPERIMENTAL RESULTS;83
15.9;CONCLUSION;84
15.10;REFERENCES;85
16;11Endovascular treatment of intracranial arteriovenous malformations using ONYX;86
16.1;1 Abstract;86
16.2;Keywords:;86
16.3;2 Materials and methods;87
16.4;3 Results;88
16.5;4 Discussion;88
16.6;5 Conclusions;90
16.7;References;91
17;12Evaluation of spatial distribution of skin blood flow using optical imaging;92
17.1;Abstract.;92
17.2;1 Introduction;92
17.3;2 Materials and methods;93
17.3.1;2.1 Data acquisition;93
17.3.2;2.2 Reference signal;94
17.3.3;2.3 Delay estimation;95
17.3.4;2.4 Image analysis;95
17.4;3 Results;96
17.5;4 Conclusions;97
17.6;References;97
18;13Computer-assisted diagnosis of osteoartrithis on hip radiographs;99
18.1;Abstract.;99
18.2;Keywords:;99
18.3;1 Introduction;99
18.4;2 Material a and Methods;100
18.4.1;1.1Data sets;100
18.4.1.1;1.2 Method1 (Traditional manual radiographical angle and ratio measurements to detect OA);100
18.4.1.2;1.3 Method2 (he Th proposed Computer assisted angle and ratio ment-measurement system;100
18.5;3 Experiments;103
18.6;4 Conclusion;104
18.7;References;104
19;14DETERMINATION OF SEX BY DISCRIMINANT FUNCTION ANALYSIS OF LINEAR DIAMETERS IN BOSNIAN HUMAN SKULLS;106
19.1;Abstract.;106
19.2;Keywords:;106
19.3;1 INTRODUCTION;106
19.4;2 METHODS AND MATERIAL;106
19.4.1;2.1 Statistic methods;108
19.5;3 RESEARCH RESULTS;108
19.6;4 DISCUSSION;111
19.7;5 CONCLUSION;112
19.8;REFERENCES;112
20;Plenary Lectures I - Session III:BIOSENSORS AND BIOINSTRUMENTATION;113
21;15Eustachian Tube Dysfunction Assessment Through Tympanic Cavity Air Exchange Sensor;114
21.1;Abstract.;114
21.2;Keywords:;114
21.3;1 Introduction;114
21.4;2 Principles of ET air exchange sensor and measurment method;115
21.4.1;2.1 Using Boyle’s Law for Finding Exchange Volume;117
21.5;3 Classification of ETD cases;119
21.5.1;3.1 Observations about ET muscular signals;119
21.6;4 CONCLUSIONS;119
21.7;REFERENCES;119
22;16Design, Simulation and Implementation of a Selective Recording System from Peripheral nervous system;121
22.1;Abstract.;121
22.2;Keywords:;121
22.3;1 Introduction;121
22.4;2 Materials and methodes;122
22.4.1;2.1 Simulations;122
22.4.2;2.2 Implementation;123
22.5;3 RESULTS;123
22.6;4 DISCUSSION;125
22.7;References;125
23;17Fabrication and testing of a multi-electrode spiral nerve cuff;127
23.1;Abstract.;127
23.2;Keywords:;127
23.3;1 Introduction;127
23.4;2 Materials and methods;127
23.5;3 Results;128
23.6;4 Discussion;129
23.7;References;129
24;18Personal electromyographic biofeedback system „MyMyo“;131
24.1;Abstract.;131
24.2;Keywords:;131
24.3;1 Introduction;131
24.4;2 Existing solutions to the problem;131
24.5;3 Concept and requirements for the device;132
24.6;4 Device development;132
24.6.1;4.1 Power supply;133
24.6.2;4.2 Analog processing;133
24.6.3;4.3 Microcontroller and BLE;134
24.6.4;4.4 Data coding;134
24.6.5;4.5 Mobile phone application;135
24.6.6;4.6 Device design;135
24.7;5 Device testing;136
24.7.1;5.1 Transfer function;136
24.7.2;5.2 Data throughput problem;137
24.8;6 Conclusion;137
24.9;Acknowledgement;137
24.10;Conflicts of Interest;137
24.11;Literature;137
25;19A NOVEL ACTIVE DEVICE FABRICATION METHOD FOR INTERVENTIONAL MRI PROCEDURES;139
25.1;Abstract.;139
25.2;Keywords:;139
25.3;Introduction;139
25.4;Conclusion;144
25.5;References;144
26;20Experimental verification of EOG signal measurement using the modified digital stochastic measurement method;146
26.1;Abstract.;146
26.2;Keywords:;146
26.3;1 Introduction;146
26.4;2 Proposed method and simulation model;146
26.5;3 Experimental hardware solution;148
26.6;4 Results and discusion;150
26.7;5 Conclusion;151
26.8;Acknowledgment;151
26.9;References;151
27;Plenary Lectures I - Session IV:BIO-MICRO/NANO TECHNOLOGIES;152
28;21Application of Artificial Neural Network in modelling of photo-degradation suspension of manganese doped zinc oxide nanoparticles under visible-light irradiation;153
28.1;Abstract.;153
28.2;Keywords:;153
28.3;1 Introduction;153
28.4;2 Methods;154
28.5;3 Results and discussion;155
28.6;5. Conclusion;156
28.7;References;157
29;22Quantification of protein concentration adsorbed on gold nanoparticles using Artificial Neural Network;158
29.1;Abstract.;158
29.2;Keywords:;158
29.3;1 Introduction;158
29.4;2 Methods;158
29.4.1;2.1 Sample collection;158
29.4.2;2.2 Development of Artificial Neural Network for quantification of protein concentration bind on surface of gold nanoparticles NPs;159
29.4.3;2.3 Testing of ANN;159
29.5;3 Results;159
29.5.1;3.1 ANN training results;159
29.6;4 Conclusion;161
29.7;Conflict of interest;162
29.8;References;162
30;23 Design and Fabrication of a PDMS Microfluidic Devicefor Titration of Biological Solutions;163
30.1;Keywords:;163
30.2;1. Introduction;163
30.3;2. Chip design;163
30.4;3. Material and Methods;164
30.5;3. Results and discussion;165
30.6;4. Conclusion and future work;167
30.7;Acknowledgments;167
30.8;References;167
31;24BEAUTY OF FINE DOTS;169
31.1;Abstract.;169
31.2;Keywords:;169
31.3;1 Introduction;169
31.4;2 Why are quantum dots so unique?;170
31.5;3 Application of DHLA-coated CdSe/ZnSquantum dots;170
31.6;4 Nanotoxicology;171
31.7;5 Conclusion;172
31.8;References;172
32;25EFFECT OF CHEMICALLY-SYNTHESIZED SILVER NANOPARTICLES (AG-NP) ON GLYCEMIC AND LIPIDEMIC STATUS IN RAT MODEL;174
32.1;Abstract.;174
32.2;Keywords:;174
32.3;Introduction;174
32.4;RESULTS;176
32.5;CONCLUSION;178
32.6;REFERENCES;178
33;26Towards green nanotechnology: maximizing benefits and minimizing harm;180
33.1;Abstract.;180
33.2;Keywords:;180
33.3;1 Introduction;180
33.4;2 Nanotechnology and its potential negative impact on human health and environment;181
33.5;3 Green nanotechnology – a way to changethe future;182
33.6;4 Conclusion;185
33.7;References;185
34;27Development of the method for quantification of amino acid adsorbed on nanoparticle surface;187
34.1;Abstract.;187
34.2;Keywords:;187
34.3;1 Introduction;187
34.3.1;1.1 Nanoparticles;187
34.3.2;1.2 Gold nanoparticles (Au NPs);187
34.3.3;1.3 Silicon nanoparticles (Si NPs).;188
34.3.4;1.4 Interaction bewtween nanoparticles-biologicalsystems;188
34.4;2 Materials and Methods;188
34.4.1;2.1 Materials;188
34.4.2;2.2 Methods;188
34.5;3 Results and Discussion;189
34.5.1;3.1 Adsorption of amino acids on gold nanoparticles;189
34.5.2;3.2 Adsorption of amino acids on SiO2 nanoparticles;190
34.6;4 Conclusion;191
34.7;5 References;191
35;28Application of biological surface adsorption index approach (BSAI) in characterization of interactions between gold nanoparticles and biomolecules;192
35.1;Abstract.;192
35.2;Keywords:;192
35.3;1. Introduction;192
35.4;2. Materials and Methods;193
35.4.1;2.1 Materials and reagents;193
35.4.2;2.2. Synthesis of AuNPs;194
35.4.3;2.2. Preparation of probe compounds solutions;194
35.4.4;2.3. Sample preparation;194
35.4.5;2.4. SPME/GC-MS instrumentation and analyticalparameters;194
35.4.6;2.5. Quantification of the adsorption of probe compoundsby NPs;195
35.4.7;2.6. Adsorption coefficient calculation;195
35.5;3. Results;195
35.6;3. Conclusion;197
35.7;References;197
36;Plenary Lectures I - Session V: BIOMECHANICS, ROBOTICS AND MINIMALLYINVASIVE SURGERY;199
37;29MECHANICAL TESTING STRATEGIES FOR DENTAL IMPLANTS;200
37.1;Abstract.;200
37.2;Keywords:;200
37.3;1 INTRODUCTION;200
37.4;2 STRESS AND STRAINMEASUREMENTS;201
37.5;3 RESONANCE FREQUENCYANALYSIS;204
37.6;4 FRACTURE RESISTANCE;205
37.7;REFERENCES;205
38;30CONTACT FORCE PROBLEM IN THE REHABILITATION ROBOT CONTROL DESIGN;208
38.1;Abstract.;208
38.2;Keyword:;208
38.3;1 INTRODUCTION;208
38.4;2 REVIEW OF CURRENT REHABILITATIONROBOTS;209
38.4.1;2.1 Target population and rehabilitation robots;209
38.4.2;2.2 Trends of rehabilitation robots;209
38.5;3 THE ROBOT - ENVIRONMENTINTERACTION PROBLEM;210
38.5.1;3.1 Control of contact tasks;211
38.5.2;3.2 Cooperative manipulation;212
38.6;4 MANIPULATOR AND CONTACTDYNAMICS MODEL;214
38.6.1;4.1 Manipulator kinematics and dynamics;214
38.6.2;4.2 Environment model;215
38.6.3;4.3 Impedance controller for cooperative manipulation;215
38.7;5 DISCUSSION AND CONCLUSION;216
38.8;6 FUTURE RECOMMENDATIONS;216
38.9;REFERENCES;217
39;31 Implementation and Validation of Human Kinematics measured using IMUs for Musculoskeletal Simulations by the Evaluation of Joint Reaction Forces;220
39.1;Abstract:;220
39.2;Keywords:;220
39.3;I. INTRODUCTION;220
39.4;II. MATERIALS AND METHODS;221
39.5;III. RESULTS;222
39.6;IV. DISCUSSION A ND LIMITATIONS;225
39.7;V. CONCLUSION;226
39.8;REFERENCES;226
40;32FEA of the transiliacal internal fixator as an osteosynthesis of pelvic ring fractures;227
40.1;Abstract:;227
40.2;Keywords:;227
40.3;I. INTRODUCTION;227
40.4;II. MATERIALS AND METHODS;228
40.5;III. RESULTS;229
40.6;IV. DISCUSSION AND LIMITATIONS;230
40.7;V. CONCLUSION;231
40.8;VI. CONFLICT OF INTEREST DECLARATION;231
40.9;VII. REFERENCES;232
41;33Overview of the Development of Hydraulic Above Knee Prosthesis;233
41.1;Abstract.;233
41.2;Keywords:;233
41.3;1 INTRODUCTION;233
41.4;2 CURRENT ACCOMPLISHMENTS;233
41.5;3 DEVELOPMENT OF HAKP;233
41.6;4 CURRENT R&D OF HAKP;235
41.6.1;4.1 Prosthetic foot development;235
41.6.2;4.2 Design of HAKP prototype with knee andankle actuators;235
41.6.3;4.3 Development of motion recognition equipment for HAKP;236
41.7;5 CONCLUSION AND FUTURE WORK;237
41.8;REFERENCES;237
42;34Non-invasive estimation of respiratory depression profiles during robot-assisted laparoscopic surgery using a model-based approach;238
42.1;Abstract.;238
42.2;Keywords:;238
42.3;1 Introduction;238
42.4;2 Methods;239
42.5;3 Results;241
42.6;4 Discussion;244
42.7;References;245
43;Plenary Lectures I - Session VI: CARDIOVASCULAR, RESPIRATORY AND ENDOCRINESYSTEMS ENGINEERING;247
44;35Subclinical inflammation: The link between increased cardiovascular risk and subclinical hypothyroidism in postmenopausal women;248
44.1;ABSTRACT.;248
44.2;KEYWORDS:;248
44.3;INTRODUCTION;248
44.4;MATERIALS AND METHODS;248
44.5;RESULTS;249
44.6;DISCUSSION;251
44.7;CONCLUSION;252
44.8;REFERENCES;252
45;36Computational Vascular Surgery Planning and Predicting for Abdominal Aortic Aneurysm;254
45.1;Abstract.;254
45.2;1 Introduction;254
45.3;2 Methods;255
45.3.1;2.1 Experimental determination of tissue deformation;255
45.3.2;2.2 Computer models;256
45.3.3;2.3 Drag force calculation;256
45.4;3 Results;256
45.5;4 Discussion and conclusion;258
45.6;Acknowledgments;258
45.7;References;258
46;37Determination of Probabilistic Neural Network's Accuracy in Context of Cardiac Stress Test;259
46.1;Abstract.;259
46.2;Keywords:;259
46.3;1 Introduction;259
46.4;2 Cardiac Stress Test Basics;260
46.5;3 PNN Basics;262
46.6;4 Experiment Setup and Results;262
46.7;5 Conclusion;264
46.8;References;264
47;38Effects of electrical stimulation as a new method of treating diabetes on animal models: Review;266
47.1;Abstract.;266
47.2;Keywords:;266
47.3;1 Introduction;266
47.4;2 Effects of electrical stimulation of dorsalmotornucleus of vagus nerve;267
47.5;3 Electrical field stimulation (EFS) on thesecretion of insulin and glucagon;267
47.6;4 Effects of non-invasive peripheralstimulation in normal and insulinresistant rats;267
47.7;5 Effect of hepatic electrical stimulation ondiabatetic rats;268
47.8;6 Effects of mild electrical stimulation andheat shock on different models of mice;269
47.9;7 Conclusion;270
47.10;Conflict of interest declaration;270
47.11;References;270
48;39Numerical Simulation of Blood Flow Through the Aortic Arch;272
48.1;Abstract.;272
48.2;Keywords:;272
48.3;1 Background;272
48.4;2 Methodology;272
48.4.1;2.1. Mathematical model;272
48.4.2;2.2 Discretization;273
48.5;3 Physiology of blood flow;273
48.5.1;3.1 Basic presumptions of fluid mechanics of bloodflow through the aorta;274
48.5.2;3.2 Entrance region into aorta;274
48.6;4 Problem description;274
48.6.1;4.1 Geometry and mesh;276
48.6.2;4.2 Initial and boundary conditions;276
48.7;5 Result and discussion;277
48.8;6. Conclusion;280
48.9;References;281
49;40Computational modeling of plaque development in the coronary arteries;282
49.1;Abstract.;282
49.2;1 Introduction;282
49.3;2 Methods;283
49.4;3 Results;284
49.5;4 Discussion and conclusion;286
49.6;Acknowledgments;286
49.7;References;286
50;Plenary Lectures II: Poster Session;288
51;41People identification using Kinect sensor;289
51.1;Abstract.;289
51.2;Keywords:;289
51.3;1 Introduction;289
51.4;2 Proposed solution;290
51.4.1;2.1 Sensors used, preprocessing performed;290
51.4.2;2.2 Features used;290
51.4.3;2.3 Training and testing phase;290
51.4.4;2.4 Experimental results;292
51.5;3 Conclusion and future work;294
51.6;References;294
52;42Artificial Neural Network: Gas recognition;295
52.1;Abstract.;295
52.2;Keywords:;295
52.3;1 Introduction;295
52.4;2 Methods;295
52.5;3 Results and discussion;297
52.6;4 Conclusion;298
52.7;References;299
53;43A Fuzzy Model to Predict Risk of Urinary Tract Infection;301
53.1;Abstract.;301
53.2;Keywords:;301
53.3;1. Introduction;301
53.4;2. Methods;302
53.4.1;2.1 Fuzzification and Membership Functions;302
53.4.2;2.2 Fuzzy Inference System;303
53.4.3;2.3 Set of Rules;303
53.4.4;2.4 Defuzzification;304
53.5;3. Results;304
53.6;4. Discussion;305
53.7;Acknowledgements;305
53.8;References;305
54;44Conceptual image of heart – path to patient benefit;306
54.1;Abstract:;306
54.2;Keywords:;306
54.3;1. Introduction;306
54.4;2. Conceptual design;307
54.5;3. Aim;308
54.6;4. Material and methods;308
54.7;5. Results;308
54.8;6. The use and future directions;310
54.9;7. Conclusion;310
54.10;References:;311
55;45A Novel Gait Detection Algorithm Based on Wireless Inertial Sensors;312
55.1;Abstract.;312
55.2;Keywords:;312
55.3;1 Introduction;312
55.4;2 Method;313
55.4.1;2.1 Hardware;313
55.4.2;2.2 Algorithm design.;313
55.4.3;2.3 Experiment;314
55.4.4;2.4 The accuracy and latency;315
55.5;3 Results;315
55.6;4 Conclusion;316
55.7;Acknowledgement;316
55.8;References;316
56;46Single-Chip Intrabody Communication Node;317
56.1;Abstract.;317
56.2;Keywords:;317
56.3;1 Introduction;317
56.4;2 The IBC system based on PSoC;318
56.4.1;2.1 Transmitter;318
56.4.2;2.2 Recevier;319
56.5;3 Measurement results;321
56.6;4 Conclusion;321
56.7;Acknowledgement;322
56.8;Conflicts of Interest;322
56.9;References;322
57;47Microneedle-assisted delivery of NSAIDs;323
57.1;Abstract;323
57.2;Keyword;323
57.3;I Introduction;323
57.4;II MICRONEEDLES-MODERN SYSTEM OF DRUG DELIVERY;323
57.5;III MICRONEEDLE-ASSISTED DELIVERY OF NSAIDS;325
57.6;IV CONCLUSION;326
57.7;REFERENCE;327
58;48PREPARATION OF NANOEMULSIONS BY HIGH-ENERGY AND LOW-ENERGY EMULSIFICATION METHODS;329
58.1;Abstract.;329
58.2;Keywords:;329
58.3;1 Introduction;329
58.4;2 Methods of preparation of nanoemulsions;330
58.4.1;2.1. High-energy methods for preparation of nanoemulsions;330
58.4.2;2.2. Low-energy methods for preparation of nanoemulsions;331
58.5;3 Conclusions;333
58.6;References;333
59;49Characterisation of NiTi orthodontic archwires characteristic functional properties;335
59.1;Abstract;335
59.2;Key word;335
59.3;Abbreviations;335
59.4;1 Introduction;335
59.5;2 Materials and Methods;337
59.6;3 Results and Discussion;338
59.7;4 Conclusions;344
59.8;References;344
60;50LONG-LATENCY INTRACORTICAL INHIBITION DURING UNILATERAL MUSCLE ACTIVITY;345
60.1;Abstract.;345
60.2;Keywords:;345
60.3;Introduction;345
60.4;Methods;346
60.5;Results;347
60.6;Discussion;349
60.7;Conclusions;349
60.8;Acknowledgement;349
60.9;References;349
61;51Simulation of kinematic behaviour of prosthetic devices;351
61.1;Abstract.;351
61.2;Keywords:;351
61.3;1 Introduction;351
61.4;2 Methods;352
61.5;3 Results;353
61.6;4 Conclusion;355
61.7;References;355
62;52Thyroid pathology and platelet functional activity;356
62.1;Abstract.;356
62.2;Keywords:;356
62.3;INTRODUCTION.;356
62.4;MATERIALS AND METHODS.;357
62.5;OWN RESEARCH.;358
62.6;CONCLUSION.;360
62.7;LITERATURE;361
63;53TRENDS AMONG NEONATOLOGISTS IN DECISION TO VENTILATE PRETERM INFANTS WITH PERMISSIVE HYPERCAPNIA;362
63.1;ABSTRACT:;362
63.2;Aim;362
63.3;Methods;362
63.4;Results;362
63.5;Conclusion;362
63.6;Keywords;362
63.7;INTRODUCTION:;362
63.8;METHODS;363
63.9;RESULTS;363
63.10;DISCUSSION;364
63.11;Literature;365
64;54A mathematical model of the effect of metabolic control on joint mobility in young type 1 diabetic subjects;367
64.1;Abstract.;367
64.2;1 Introduction;367
64.3;2 Materials and methods;368
64.3.1;2.1 Determination of ankle joint mobility;368
64.3.2;2.2 Mathematical model;368
64.3.3;2.3 Fitting;369
64.4;3 Results;369
64.5;4 Discussion;370
64.6;References;370
65;55Basics of mathematical modeling of pulmonary ventilation mechanics and gas exchange;372
65.1;Abstract.;372
65.2;Keywords:;372
65.3;1 Introduction;372
65.4;2 Breathing process;373
65.4.1;2.1 Air movement into and out of the lungs, and processes which cause it;373
65.5;3 Mathematical modeling of human respiratorysystem;375
65.5.1;3.1 Generalized system properties;375
65.5.2;3.2 A simplified linear model of pulmonary ventilation mechanics;375
65.5.3;3.3 Pulmonary gases exchange;376
65.6;4 Conclusions;378
65.7;References;378
66;56TESTING OF THE INFLUENCE OF MEDIA’S pH VALUE ON THE SOLUBILITY AND PARTITION COEFFICIENT OF THE ACETYLSALICYLIC ACID;380
66.1;Abstract.;380
66.2;Keywords:;380
66.3;Introduction*;380
66.4;Material and methods;380
66.5;Results and discussion;381
66.6;Conclusion;383
66.7;References;383
67;57The use of ELM and MnM servers for the prediction of RANK function in osteoclast formation;384
67.1;Abstract.;384
67.2;Keywords:;384
67.3;1 Introduction;384
67.4;1.1. ELM server;385
67.5;1.2. MiniMotif Miner;386
67.6;2. Aim and research goals;386
67.7;3. Material and methods;387
67.7.1;3.2.The results of RANK analysis with the MnM web-tool;387
67.8;4.Results;387
67.8.1;4.1. The results of RANK analysis with the ELM server;387
67.9;4.Discussion;387
67.10;5.Conclusion;389
67.11;References;390
68;58QSAR modeling and structure based virtual screening of new PI3K/mTOR inhibitors as potential anticancer agents;391
68.1;Abstract.;391
68.2;Keywords:;391
68.3;Introduction;391
68.4;Materials and methods;392
68.5;Results and discussion;394
68.6;Conclusion;395
68.7;Conflict of interest;395
68.8;Acknowledgement;395
68.9;References;395
69;59Career development in Green Biotechnology in B&H: roadblocks and prospects;396
69.1;Abstract.;396
69.2;Keywords:;396
69.3;1 Introduction;396
69.4;2 Green biotechnology/Plant biotechnology;397
69.5;3 Career development;398
69.6;Conflict of interest;399
69.7;References;399
70;60E-health in Bosnia and Herzegovina: exploring the challenges of widespread adoption;400
70.1;Abstract.;400
70.2;Keywords:;400
70.3;1 Introduction;400
70.4;2 Benefits and challenges of e-health;400
70.5;3 Analysis of the challenges affecting e-health adoption in Bosnia and Herzegovina;402
70.5.1;3.1 Political issues;402
70.5.2;3.2 Economic concerns and factors;402
70.5.3;3.3 Technological challenges;404
70.6;4 E-health implementation in Bosnia and Herzegovina: SWOT analysis;405
70.7;5 Conclusion;406
70.8;References;406
71;615-HIAA and HVA in the Coma Cerebri, Hydrocephalus and Tumor Cerebri;408
71.1;Abstract.;408
71.2;Keywords:;408
71.3;1 INTRODUCTION;408
71.4;2 MATERIALS AND METHODS;410
71.5;3 RESULTS;411
71.6;4 DISCUSSION;412
71.7;5 CONCLUSION;412
71.8;REFERENCES;412
72;62Estimation of lipophilicity data for derivatives of alkandiamine-N,N’-di-2-(3-cyclohexyl) propanoic acid with potential antineoplastic activity, by UHPLC-MS method;414
72.1;ABSTRACT:;414
72.2;Keywords:;414
72.3;1 INTRODUCTION;414
72.4;2 EXPERIMENTAL;415
72.4.1;2.1 Chemicals;415
72.4.2;2.3 Lipophilicity determination by UHPLC/MS method;418
72.5;3 RESULTS AND DISCUSSION;420
72.6;CONCLUSION;421
72.7;REFERENCES;421
73;63Detremination of kinetic effect of Metoprolol and Ranitidine on HRP- modified GC electrode biosensor;422
73.1;Abstract.;422
73.2;Introduction;422
73.3;Materials and methods;423
73.4;Results and discussion;423
73.5;Conclusion;426
73.6;Reference:;426
74;64Monitoring of bisoprolol fumarate stability under different stress conditions;427
74.1;Abstract.;427
74.2;Keywords:;427
74.3;Introduction;427
74.4;Materials and Methods;427
74.5;Results and Discussion;428
74.6;Conclusion;435
74.7;REFERENCES:;436
75;65Measuring the feeling: correlations of sensorial to instrumental analyses of cosmetic products;437
75.1;Abstract.;437
75.2;Keywords:;437
75.3;1 Introduction;437
75.4;2 Sensorial analysis;437
75.5;3 Instrumental analyses;438
75.6;4 Making connections and correlations;438
75.7;5 Conclusion;440
75.8;References;440
76;66Hydrophilic antioxidant scores against hydroxyl and peroxyl radicals in honey samples from Bosnia and Herzegovina;441
76.1;Abstract.;441
76.2;Keywords:;441
76.3;1 Introduction;441
76.4;2 Methods;442
76.4.1;2.1 Samples;442
76.4.2;2.2 Procedure;442
76.4.3;2.3 Instrumentation;443
76.4.4;2.4 Statistics;443
76.5;3 Results and discussion;443
76.6;4 Conclusion;444
76.7;Acknowledgements;444
76.8;Abbreviations;444
76.9;References;444
77;67Polymorphisms of 1691G>A and 4070A>G FV in Bosnian women with pregnancy loss;447
77.1;Abstract.;447
77.2;Keywords:;447
77.3;Background;447
77.4;Aim;448
77.5;Statistical analysis;448
77.6;Results;448
77.7;Discussion;449
77.8;Conclusion;450
77.9;Declaration of interest;450
77.10;References;450
78;68 Lack of association between I/D ACE and -675 ID 4G / 5G PAI-1 polymorphisms and predicting risk of pregnancy loss (PROPALO) in Bosnian women;452
78.1;Abstract.;452
78.2;Keywords:;452
78.3;Introduction;452
78.4;Material and Methods;453
78.5;Statistical analysis;454
78.6;Results;454
78.7;Disscusion;454
78.8;Conclusion;455
78.9;References;455
79;69Gene clustering using Gene expression data and Self-Organizing Map (SOM);457
79.1;Abstract.;457
79.2;Keywords:;457
79.3;1 Introduction;457
79.4;2 Methods;458
79.4.1;2.1 Principle of Self-organizing maps (SOM);458
79.4.2;2.2 The architecture of the SOM network;458
79.4.3;2.3 The learning algorithm of SOM;458
79.4.4;2.4 Establishment of a SOM;459
79.5;3 Results;460
79.6;4 Conclusion;461
79.7;References;462
80;70Public opinion toward GMOs and biotechnology in Bosnia and Herzegovina;464
80.1;Abstract.;464
80.2;Keywords:;464
80.3;1 Introduction;464
80.4;2 Methods;465
80.5;3 Results;465
80.6;4 Discussion;467
80.7;5 Conclusion;468
80.8;References;469
81;71Future trends and possibilities of using induced pluripotent stem cells (iPSC) in regenerative medicine;471
81.1;Abstract:;471
81.2;Keywords.;471
81.3;I. INTRODUCTION;471
81.4;II. ROLE, CLASSIFICATION AND FUNCTION OF STEM CELLS IN THE BODY;471
81.5;III. CONCLUSION;475
81.6;REFERENCES;476
82;Plenary Lectures II - Session VII:BIOMEDICAL SIGNAL PROCESSING 2;477
83;72ECG S Signal Cla n assification Using Ar rtificial N eural Netw works: Co omparison n f oft Different Feature T Types;478
83.1;Abstract.;478
83.2;Keywords:;478
83.3;1 Introduction;478
83.4;1.1 Related work;479
83.5;2 Artificial Neural Networks;479
83.5.1;2.1 Proposed method;479
83.6;3 Feature extraction;480
83.6.1;3.1 Time-domain features;480
83.6.2;3.2 Morphological features;481
83.6.3;3.3 Statistical features;482
83.7;4 Results;482
83.8;5 Conclusion;484
83.9;References;484
84;73Surface EMG Signal Classification by Using WPD and Ensemble Tree Classifiers;486
84.1;Abstract.;486
84.2;Keywords:;486
84.3;1 Introduction;486
84.4;2 Material and Methods;487
84.4.1;2.1 EMG Signal Data;487
84.4.2;2.2 Multi-Scale Principal Component Analysis(MSPCA) for De-Noising;487
84.4.3;2.3 Feature Extraction and Dimension Reduction;487
84.4.4;2.4 Decision Tree Classifiers;488
84.4.5;2.5 Ensemble Tree Classifiers;488
84.5;3 Results and Discussion;488
84.5.1;3.1 Performance Evaluation Criteria;488
84.5.2;3.2 Experimental Results;488
84.5.3;3.3 Discussion;490
84.6;4 Conclusion;490
84.7;References;491
85;74Tool for Comparative Case Studies of Heart Rate and Heart Rate Variability;493
85.1;Abstract.;493
85.2;Keywords:;493
85.3;1 Introduction;493
85.4;2 Heart Rate and Heart Rate VariabilityAnalyses;493
85.5;3 HR-HRV Analysis Tool;494
85.6;4 Case Study: Stress level;495
85.7;5 Conclusion;496
85.8;References;496
86;75 A n novel appr roach for p parameter r estimatio on of Fricke -Morse model using D al Differential Impedance Analysis;498
86.1;Abstract .;498
86.2;Keywords;498
86.3;1 Introduction;498
86.4;2 A new approach for parameter estimation of Fricke-Morse model using Differential Impedance Analysis;499
86.5;3 Evaluation of the proposed method;500
86.6;4 Conclusion;505
86.7;Acknowledgement;505
86.8;References;505
87;76Wavelet and Teager Energy Operator (TEO) for Heart Sound Processing and Identification;506
87.1;Abstract;506
87.2;Keywords:;506
87.3;1 Introduction;506
87.4;2 Materials and Methods;507
87.4.1;2.1 Teager Energy Operator;508
87.4.2;2.2 Wavelet Transform;508
87.5;3 Results;508
87.5.1;3.1 Graphical User Interface (GUI);509
87.5.2;3.2 Examples of TEO without/with Wavelets;510
87.6;4 Discussion;511
87.7;5 Conclusion;513
87.8;Conflict of interest;513
87.9;References;513
88;77Ovary Cancer Detection using Decision Tree Classifiers based on Historical Data of Ovary Cancer Patients;514
88.1;Abstract.;514
88.2;Keywords:;514
88.3;1 Introduction;514
88.4;2 Methodology;515
88.4.1;2.1 Dataset;515
88.4.2;2.2 Algorithms;516
88.5;3 Experiments and results;517
88.5.1;3.1 Feature Selection;519
88.6;4 Discussion and Conclusion;519
88.7;Conflict of interest;520
88.8;References;520
89;78Mental workload vs. stress differentiation using single-channel EEG;522
89.1;Abstract.;522
89.2;1 Introduction;522
89.3;2 Methods;522
89.3.1;2.1 Experimental procedure;522
89.3.2;2.2 Feature extraction;523
89.3.3;2.3 ECG and EDA features;524
89.3.4;2.4 Feature selection;524
89.4;3 Results;525
89.5;4 Discussion;525
89.6;5 Conclusion;526
89.7;References;526
90;79Human-machine interface via EMG signals derived from EEG measurement device;527
90.1;Abstract.;527
90.2;Keywords:;527
90.3;1 Introduction;527
90.4;2 Measurement method;529
90.5;2 Experiment Results;530
90.5.1;2.1 Discussion;530
90.5.2;2.2 Conclusion;530
90.6;References;531
91;Plenary Lectures II - Session VIII:BIOMEDICAL IMAGING AND IMAGE PROCESSING 2;532
92;80A Novel Feature Extraction Approach with VBM 3D ROI Masks on MRI;533
92.1;Abstract.;533
92.2;Keywords:;533
92.3;1 Introduction;533
92.4;2 Material;534
92.4.1;2.1 OASIS Database;534
92.4.2;2.2 Preprocessing;534
92.5;3 Methods;534
92.5.1;3.1 Voxel Based Morphometry;534
92.5.2;3.2 Masking of 3D ROI;535
92.5.3;3.3 Histogram Based First Order Statistics;536
92.6;4 Experimentals;538
92.6.1;4.1 Statistical Analysis of FOS;538
92.7;5 Conclusion;538
92.8;Acknowledgement;539
92.9;References;539
93;81A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images;541
93.1;Abstract.;541
93.2;Keywords:;541
93.3;1 Introduction;541
93.4;2 Methods;542
93.4.1;2.1 Bias-field estimation;542
93.4.2;2.2 Data Generation;544
93.4.3;2.3 Evaluation;544
93.5;3 Results;544
93.5.1;3.1 Application on craniofacial CBCT images;544
93.6;4 Conclusion;546
93.7;References;547
94;82Detection and Segmentation of Nodules in Chest Radiographs Based on Lifetime Approach;549
94.1;Abstract.;549
94.2;Keywords:;549
94.3;1 INTRODUCTION;549
94.4;2 PROPOSED METHOD;550
94.5;1.1 The Image Database;551
94.6;1.2 Preprocessing;551
94.7;1.3 Detection of Candidate Nodules;551
94.8;1.4 Segmentation of Candidate Nodules;554
94.9;1.5 Texture Based Feature Extraction;554
94.10;3 EXPER?MENTAL RESULTS;555
94.11;4 DISCUSSION and CONCLUSIONS;556
94.12;REFERENCES;556
95;83Automated Colony Counting Based on Histogram Modeling Using Gaussian Mixture Models;558
95.1;Abstract.;558
95.2;Keywords:;558
95.3;1 Introduction;558
95.4;2 Image Processing and Colony Counting;558
95.4.1;2.1 Image Processing;559
95.4.2;2.2 Rating and Feature Extraction;561
95.5;3 Results;561
95.6;4 Conclusions;562
95.7;Bibliography;562
96;Plenary Lectures II - Session IX: CLINICAL ENGINEERING AND HEALTHTECHNOLOGY ASSESSMENT;564
97;84The electrode setup for vibratory evoked potentials;565
97.1;Abstract.;565
97.2;Keywords:;565
97.3;1 Introduction;565
97.4;2 Materials and methods;566
97.5;3 Results and discussion;567
97.6;4 Conclusion;570
97.7;References;570
98;85Quality control of angular tube current modulation;571
98.1;Abstract:;571
98.2;Keywords:;571
98.3;I. INTRODUCTION;571
98.4;II. MATERIALS AND METHODS;571
98.5;III. RESULTS;573
98.6;IV. DISCUSSION;573
98.7;V. CONCLUSION;574
98.8;ACKNOWLEDGEMENTS;574
98.9;DISCLOSURE OF POTENTIAL CONFLICTS OFINTEREST;574
98.10;REFERENCES;574
99;86A testbed evaluation of MAC layer protocols for smart home remote monitoring of the elderly mobility pattern;576
99.1;Abstract.;576
99.2;Keywords:;576
99.3;1 Introduction;576
99.4;2 Related work;577
99.5;3 Smart Home Testbed Design;578
99.6;4 Simulation and testbed analysis;578
99.6.1;4.1 Observed Event Evaluations;579
99.6.2;4.2 MAC layer evaluation using our tested;579
99.6.3;4.3 MAC Layer Evaluation Comparison of CASAS and Conducted Simulation;580
99.7;5 Conclusion and future works;581
99.8;Acknowledgment;582
99.9;References;582
100;87Proposal of integrated software system for simulation and GIS visualization of accidents caused by emission of hazardous gases;584
100.1;Abstract.;584
100.2;Keywords:;584
100.3;1 Introduction;584
100.4;2 Methods;585
100.5;3 Results and discussion;586
100.5.1;3.1 Integrated software system;586
100.5.2;3.2 “XY plume” model;588
100.5.3;3.3 “XYZ plume” model;588
100.6;4 Conclusion;589
100.7;References;590
101;88LEGAL METROLOGY: MEDICAL DEVICES;591
101.1;Abstract.;591
101.2;Keywords:;591
101.3;1 Introduction;591
101.4;2 Directives for conformity assessment of medical devices;592
101.5;3 Medical devices with measuring function in legal metrology in Bosin and Herzegovina;593
101.6;4 Conclusion;595
101.7;References;595
102;89Diagnosis of Chronic Kidney Disease by Using Random Forest;597
102.1;Abstract.;597
102.2;Keywords:;597
102.3;Introduction;597
102.4;Materials and Methods;598
102.5;2.1 Chronic kidney disease dataset;598
102.6;2.2 Artificial Neural Network (ANN);598
102.7;2.3 Support Vector Machine (SVM);599
102.8;2.4 C4.5 Decision Tree;599
102.9;2.5 Random Forest;600
102.10;2.6 Experimental Setup and Performance Evaluation;600
102.11;Results and Discussion;601
102.12;Conclusions;601
102.13;Acknowledgments;601
102.14;References;601
103;90Global Survey on Biomedical Engineering Professionals in Health Technology Assessment;603
103.1;Abstract.;603
103.2;Keywords:;603
103.3;1 Introduction;603
103.4;2 Materials and Methods;604
103.5;3 Results and Discussion;604
103.6;4 Conclusions;605
103.7;References;605
104;Plenary Lectures II - Session X:BIOINFORMATICS AND COMPUTATIONAL BIOLOGY;607
105;91CLASSIFICATION OF METABOLIC SYNDROME PATIENTS USING IMPLEMENTED EXPERT SYSTEM;608
105.1;Abstract.;608
105.2;Keywords:;608
105.3;Introduction;608
105.4;Methods;609
105.5;Results;610
105.6;Conclusion;612
105.7;Conflict of interest declaration;612
105.8;References;612
106;92Pre-classification process symptom questionnaire based on fuzzy logic for pulmonary function test cost reduction;615
106.1;Abstract.;615
106.2;Keywords:;615
106.3;1 Introduction;615
106.4;2 The probability of the presence of Asthma or COPD in a patient;616
106.4.1;2.1 Fuzzy logic algorithm for pre-classification of COPD and asthma;616
106.4.2;2.2 COPD/Asthma Symptom questionnaire;617
106.4.3;2.3 Pre-classification system validation;618
106.5;3 Result and discussion;619
106.6;4 Conclusion;622
106.7;References;622
107;93Artificial Neural Network and Docking Study in Design and Synthesis of Xanthenes as Antimicrobial Agents;624
107.1;Abstract.;624
107.2;Keywords:;624
107.3;1 INTRODUCTION;624
107.4;2 EXPERIMENTAL;625
107.4.1;2.1. Instrumentation;625
107.4.2;2.2. General procedure for the synthesis of arylsubstituted xanthenes;625
107.4.3;2.3. Antimicrobial activity;625
107.4.4;2.4. Calculating lipophilicity parameters;625
107.4.5;2.5. Docking study and physicochemical properties calculations;625
107.4.6;2.6 Artificial Neural Network for prediction thecompounds activity against Escherichia coli and Candida albicans strains;626
107.5;3 RESULTS AND DISSCUSION;626
108;94Mathematical and Computational Models of Cell Cycle in Higher Eukaryotes;634
108.1;Abstract.;634
108.2;Keywords:;634
108.3;1 INTRODUCTION;634
108.4;2 MATHEMATICAL ANDCOMPUTATIONAL MODELING;634
108.4.1;2.1 MODELING WITH DIFFERENTIALEQUATIONS;635
108.4.2;2.2 MODELING WITH PETRI NETS;635
108.4.3;2.3 LAW OF MASS ACTION;635
108.4.4;2.4 FIRST-ORDER OR UNIMOLECULAR REACTIONS;636
108.4.5;2.5 SECOND-ORDER OR BIMOLECULARREACTIONS;636
108.4.6;2.6 REVERSIBILE MASS ACTION ORREVERSIBLE REACTION;636
108.4.7;2.7 STEADY STATES;637
108.4.8;2.8 STABILITY ANALYSIS;639
108.5;3 CONCLUSION;639
108.6;APPENDIX;640
108.7;REFERENCES;640
109;Plenary Lectures II - Session XI: HEALTH INFORMATICS, E-HEALTH AND TELEMEDICINE;642
110;95Using Information and Communications Technology as an Enabler for Designing an Efficient National Level Vaccination Planning and Dispensing System;643
110.1;Abstract:;643
110.2;Keywords:;643
110.3;I. INTRODUCTION AND MOTIVATION;643
110.4;II. LITERATURE REVIEW;644
110.5;III. PROPOSED METHODOLOGY;644
110.6;ACKNOWLEDGMENT;648
110.7;CONFLICT OF INTEREST;648
110.8;REFERENCES;648
110.9;V. CONCLUSION;648
111;96Stroke Center Heart Rate Data Acquisition;650
111.1;Abstract.;650
111.2;Keywords:;650
111.3;1 Introduction;650
111.4;2 Stroke Center Description;651
111.5;3 Heart Rate Monitor Implementation;651
111.6;3.1 Hardware Implementation;652
111.7;4 Discussion and Conclusion;653
111.8;References;653
112;97Health service quality measurement from patient reviews in Turkish by opinion mining;655
112.1;Abstract.;655
112.2;Keywords:;655
112.3;1 Introduction;655
112.4;2 Experiment Setup;656
112.4.1;2.1 Data Collection;656
112.4.2;2.2 Data Pre-Processing;656
112.4.3;2.3 Classification;658
112.4.4;2.4 Interpretation of Results;658
112.5;3 Conclusions;659
112.6;References;659
113;98e-Medical Test Recommendation System Based on the Analysis of Patients’ Symptoms and Anamneses;660
113.1;Abstract.;660
113.2;Keywords:;660
113.3;1 Introduction;660
113.4;2 Related Work;660
113.5;3 Methodology;661
113.5.1;3.1 Data;661
113.5.2;3.2 Methods;661
113.6;4 Results;663
113.7;5 Conclusion;663
113.8;References;664
114;99Antihypertensive therapy dosage calculator;666
114.1;Keywords:;666
114.2;1. Introduction;666
114.3;2. Aim;668
114.4;3. Material and methods;668
114.5;4. Software;670
114.6;5. Conclusion;671
114.7;References;671
115;100Wireless Body Area Network Studies for Telemedicine Applications Using IEEE 802.15.6 Standard;672
115.1;Abstract.;672
115.2;1 Introduction;672
115.3;1.1 Related Work;673
115.4;2 Design;674
115.4.1;2.1 Hardware;674
115.4.2;2.2 Firmware;674
115.5;2.3 Service Layer;674
115.6;3 Conclusions;676
115.7;4 Acknowledgments;676
115.8;References;676
116;101Real-Time Monitoring of ST Change for Telemedicine;677
116.1;Abstract.;677
116.2;Keywords:;677
116.3;1 Introduction;677
116.4;2 Related Studies;678
116.5;3 Proposed Method;678
116.5.1;3.1 Database;679
116.5.2;3.2 Feature Extraction;679
116.5.3;3.3 Classification;680
116.5.4;3.4 Results;680
116.6;4 Discussion and Conclusions;682
116.7;Acknowledgment;682
116.8;References;682
117;Plenary Lectures II - Session XII:BIOMEDICAL SIGNAL PROCESSING 3;684
118;102Micro cell culture analog Apparatus (uCCA) output prediction using microcontroller system based on a Artificial Neural Network;685
118.1;Abstract;685
118.2;Keywords;685
118.3;I Introduction;685
118.4;II Theoretical Considerations;686
118.5;III System Description;686
118.6;IV The Implementation;687
118.7;V Results and Discussion;687
118.8;VI Conclusion;688
118.9;References;688
119;103CLASSIFICATION OF PREDIABETES AND TYPE 2 DIABETES USING ARTIFICIAL NEURAL NETWORK;689
119.1;Abstract.;689
119.2;Keywords:;689
119.3;1 Introduction;689
119.4;2 Methods;690
119.5;2.1 Dataset for development of Artificial NeuralNetwork for classification of prediabetes anddiabetes type 2;690
119.6;2.2 Artificial Neural Network for classification of prediabetes and diabetes type 2;690
119.7;3 Results;691
119.7.1;3.1 Training performance of developed ANN;691
119.7.2;3.2 Testing performance of developed ANN;692
119.8;4 Conclusion;693
119.9;References;693
120;104Multi-biophysical event detection using blind source separated audio signals;694
120.1;Abstract.;694
120.2;1 Introduction;694
120.3;2 Background;694
120.4;3 Materials and Methods;695
120.5;4 Results;696
120.6;5 Discussion;697
120.7;6 Conclusion;700
120.8;References;700
121;105Diversity performance of microstrip patch antennas placed on human body at ISM and MBAN frequencies.;702
121.1;Abstract.;702
121.2;Keywords:;702
121.3;1 Introduction;702
121.4;2 Design and simulation of the microstripantenna placed on a body;702
121.5;3 Diversity measurements in the indoor environment for static and dynamic cases;703
121.6;4 Results and Conclusion;708
121.7;References;708
122;106Flexible system for HRV analysis using PPG signal;709
122.1;Abstract.;709
122.2;Keywords:;709
122.3;1 Introduction;709
122.4;2 Processing approach;710
122.5;3 Verification and comparison procedure;711
122.6;4 Experimental results;711
122.7;5 Conclusion and future work;714
122.8;6 Statements;714
122.9;References;716
122.10;Appendix;716
123;107AN APPLICATION OF KINECT DEPTH SENSOR FOR SCOLIOSIS AND KYPHOSIS SCREENING;717
123.1;Abstract.;717
123.2;Keywords:;717
123.3;1 Introduction;717
123.4;2 Material and Method;717
123.5;3 RESULTS AND DISCUSSION;718
123.6;3.1 Scoliosis evaluation;718
123.7;3.2 Kyphosis evaluation;720
123.8;4 CONCLUSIONS;720
123.9;References;721
124;108Development of a muscle activated switch for the severelydebilitated;722
124.1;Abstract.;722
124.2;Keywords:;722
124.3;1 INTRODUCTION;722
124.4;2 METHODOLOGY;722
124.4.1;2.1 The Existing System and Setup;723
124.4.2;2.2 Investigating EMG Signal Sensitivity;723
124.4.3;2.3 Statistical Measures of Performance;724
124.4.4;2.4 Detecting EMG Signals of Differing Magnitudes;724
124.5;3 RESULTS & DISCUSSION;724
124.5.1;3.2 Findings for detecting EMG Signals with differingmagnitudes and velocity;725
124.6;4 Conclusion;725
124.7;References;726
125;109A Dynamic Stopping Algorithm for P300 Based Brain Computer Interface Systems;727
125.1;Abstract.;727
125.2;Keywords:;727
125.3;1 INTRODUCTION;727
125.4;2 Mater?al and Method;728
125.4.1;2.1 Dataset I: BCI Competition III Dataset II;728
125.4.2;2.2 Dataset II: Region Based Paradigm;728
125.4.3;2.3 Signal Processing and Classification;729
125.5;3 Dynamic Stopping Algorithm;729
125.6;4 Results and Discussion;730
125.7;5 Conclusion;732
125.8;CONFLICT OF INTEREST;732
125.9;REFERENCES;732
126;Plenary Lectures II - Session XIII:PHARMACEUTICAL ENGINEERING;733
127;110Effects of various metal and drug agents on excretion of enzyme aspartyl proteinasein Candida albicans and its role in human physiological processes;734
127.1;Abstract.;734
127.2;Keywords;734
127.3;1 Introduction;734
127.4;2 Materials and Methods;735
127.4.1;2.1 Reagents, strains and growth conditions;735
127.4.2;2.2 Metal and drug supplements;735
127.4.3;2.3 Sample preparation and aspartyl proteinase assay;735
127.4.4;2.4 Determination of total protein amount (aspartyl proteinase);735
127.5;3 Results and Discussion;735
127.5.1;3.1 Addition of iron supplements promotesC. albicans proliferation in vitro;735
127.5.2;3.2 Iron uptake by C. albicans induces itsaspartyl proteinase excretion;735
127.5.3;3.3 Analgesics are potential promoters of fungalvirulence and pathogenicity;736
127.5.4;3.4 Subinhibitory concentrations of antibiotics stimulate excretion of aspartyl proteinasein C. albicans;736
127.6;4 Conclusion;737
127.7;5 Declaration of Conflict Interests;737
127.8;References;737
128;111 practical Transport Optimization Method and Concept in Pharmaceutical Industry;739
128.1;Abstract.;739
128.2;Keyword:;739
128.3;1 Introduction;739
128.4;2 Case Study;740
128.5;3 Results Discussion;743
128.6;4 Conclusion and Future Research;745
128.7;References;745
129;112Antiproliferative Evaluation and Docking Study of Synthesized Biscoumarin Derivatives;747
129.1;Abstract.;747
129.2;Keywords:;747
129.3;1 INTRODUCTION;747
129.4;2 MATERIALS AND METHODS;747
129.5;Cell culturing;748
129.6;Proliferation assays;748
129.7;Docking study and physicochemical properties calculations;749
129.8;3 RESULTS AND DISCUSSION;749
129.9;4 CONCLUSIONS;757
129.10;REFERENCES;757
130;113Passive absorption prediction of transdermal drug application with Artificial Neural Network;759
130.1;Abstract;759
130.2;Keywords:;759
130.3;INTRODUCTION;759
130.4;Materials and methods;761
130.5;Results and discussion;762
130.6;CONCLUSION;763
130.7;References;763
131;114The role of population pharmacokinetic analysis in rational antibiotic therapy in neonates;765
131.1;ABSTRACT.;765
131.2;Keywords:;766
131.3;INTRODUCTION;766
131.4;METHODS;766
131.5;RESULTS;766
131.6;DISCUSSION AND CONCLUSION;769
131.7;CONFLICT OF INTEREST STATEMENT;770
131.8;REFERENCES;770
132;115The ratio of hematological parameters and markers of inflammation in patients with iron deficiency and pernicious anemia;772
132.1;ABSTRACT;772
132.2;Keywords;772
132.3;AIM:;772
132.4;MATERIALS AND METHODS:;772
132.5;RESULTS:;772
132.6;Introduction:;772
132.7;Metodology:;773
132.8;Equipment:;773
132.9;Results and discusion:;773
132.10;References:;776
133;116BLOOD GROUP, HYPERTENSION, AND OBESITY IN THE STUDENT POPULATION OF NORTHEAST BOSNIA AND HERZEGOVINA;777
133.1;Abstract;777
133.2;Keywords:;777
133.3;INTRODUCTION;777
133.4;MATERIALS AND METHODS;777
133.5;RESULTS;778
133.6;DISCUSSION;779
133.7;CONCLUSION;779
133.8;ACKNOWLEDGEMENTS;779
133.9;Conflict of interest;780
133.10;LITERATURE;780
134;Plenary Lectures II - Session XIV:GENETIC ENGINEERING;781
135;117Free fatty acid profile in Type 2 diabetic subjects with different control of glycemia;782
135.1;Abstract.;782
135.2;Keywords:;782
135.3;INTRODUCTION;782
135.4;MATERIALS AND METHODS;783
135.5;RESULTS;783
135.6;DISCUSSION;785
135.7;CONCLUSION;786
135.8;REFERENCES;786
136;118Clonal selection of autochthonous grape variety Vranac in Montenegro;788
136.1;Abstract.;788
136.2;Keywords;788
136.3;1 Introduction;788
136.4;2 Matherial and methods;789
136.5;3 Results;789
136.6;4 Discussion and conclusions;791
136.7;5 References;791
137;119A Dissimilar Approach to Associating Angiotensin Converting Enzyme Polymorphisms;792
137.1;Abstract.;792
137.2;Keywords:;792
137.3;1 Introduction;792
137.4;2 Methods;793
137.4.1;2.1 Genomic DNA isolation;793
137.4.2;2.2 Spectrophotometry;793
137.4.3;2.3 Quantification with Gel Electrophoresis;793
137.4.4;2.4 ACE Gene Amplification;793
137.4.5;2.5 Confirmatory Polymerase Chain Reaction;794
137.4.6;2.6 Polymerase Chain Reaction Product GelElectrophoresis;794
137.4.7;2.7 Genotyping;794
137.4.8;2.8 Artificial Neural Network Development;794
137.5;3 Results;794
137.6;4 Discussion;795
137.7;Conflict of Interest Declaration;795
137.8;Ethical Statement;795
137.9;References;795
138;120Successful collection of stem cells in one day in the process of autologous stem cell transplantation;797
138.1;Abstract.;797
138.2;Keywords:;797
138.3;1 Introduction;797
138.4;2 Material and methods;798
138.5;3 Results;798
138.6;4 Discussion:;800
138.7;Conflict of intrest;801
138.8;References;801
139;Author Index;803
