Badnjevic | CMBEBIH 2017 | E-Book | sack.de
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E-Book, Englisch, Band 62, 806 Seiten, eBook

Reihe: IFMBE Proceedings

Badnjevic CMBEBIH 2017

Proceedings of the International Conference on Medical and Biological Engineering 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



This volume presents the proceedings of the International Conference on Medical and Biological Engineering held from 16 to 18 March 2017 in Sarajevo, Bosnia and Herzegovina. Focusing on the theme of ‘Pursuing innovation. Shaping the future’, it highlights the latest advancements in Biomedical Engineering and also presents the latest findings, innovative solutions and emerging challenges in this field. Topics include: -    Biomedical Signal Processing -    Biomedical Imaging and Image Processing -    Biosensors and Bioinstrumentation -    Bio-Micro/Nano Technologies -    Biomaterials -    Biomechanics, Robotics and Minimally Invasive Surgery -    Cardiovascular, Respiratory and Endocrine Systems Engineering -    Neural and Rehabilitation Engineering -    Molecular, Cellular and Tissue Engineering -    Bioinformatics and Computational Biology -    Clinical Engineering and Health Technology Assessment -    Health Informatics, E-Health and Telemedicine -    Biomedical Engineering Education -    Pharmaceutical Engineering
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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


Almir Badnjevic has a PhD in Electrical Engineering and experiences in both the fields of Biomedical Engineering and Medical Devices. He is currently the Director of Verlab Ltd, a verification laboratory for medical devices. Almir is also an Assistant Professor at International Burch University, an industry expert in the faculty of Electrical Engineering and a lecturer in the faculty of Medicine in the University of Sarajevo. On top of that, he is also Executive Director of the Bosnia and Herzegovina Medical and Biological Engineering Society.



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