Advances in Biomedical Engineering and Technology: Select Proceedings of ICBEST 2018

491 Pages·2021·19.838 MB·English

by  Albert A. Rizvanov, Bikesh Kumar Singh, Padma Ganasala

Lecture Notes in Bioengineering
Albert A. Rizvanov
Bikesh Kumar Singh
Padma Ganasala   Editors
Advances in
Biomedical
Engineering
and Technology
Select Proceedings of ICBEST 2018
Lecture Notes in Bioengineering
Advisory Editors
Nigel H. Lovell, Graduate School of Biomedical Engineering, University of New
South Wales, Kensington, NSW, Australia
Luca Oneto, DIBRIS, Università di Genova, Genova, Italy
Stefano Piotto, Department of Pharmacy, University of Salerno, Fisciano, Italy
Federico Rossi, Department of Earth, University of Salerno, Fisciano, Siena, Italy
Alexei V. Samsonovich, Krasnow Institute for Advanced Study, George Mason
University, Fairfax, VA, USA
FabioBabiloni,DepartmentofMolecularMedicine,UniversityofRomeSapienza,
Rome, Italy
Adam Liwo, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
RatkoMagjarevic,FacultyofElectricalEngineeringandComputing,Universityof
Zagreb, Zagreb, Croatia
Lecture Notes in Bioengineering (LNBE) publishes the latest developments in
bioengineering. It covers a wide range of topics, including (but not limited to):
(cid:129) Bio-inspired Technology & Biomimetics
(cid:129) Biosensors
(cid:129) Bionanomaterials
(cid:129) Biomedical Instrumentation
(cid:129) Biological Signal Processing
(cid:129) Medical Robotics and Assistive Technology
(cid:129) Computational Medicine, Computational Pharmacology and Computational
Biology
(cid:129) Personalized Medicine
(cid:129) Data Analysis in Bioengineering
(cid:129) Neuroengineering
(cid:129) Bioengineering Ethics
OriginalresearchreportedinproceedingsandeditedbooksareatthecoreofLNBE.
Monographs presenting cutting-edge findings, new perspectives on classical fields
or reviewing the state-of-the art in a certain subfield of bioengineering may
exceptionally be considered for publication. Alternatively, they may be redirected
to more specific book series. The series’ target audience includes advanced level
students, researchers, and industry professionals working at the forefront of their
fields.
Indexed by SCOPUS and Springerlink. The books of the series are submitted for
indexing to Web of Science.
More information about this series at http://www.springer.com/series/11564
Albert A. Rizvanov Bikesh Kumar Singh
(cid:129) (cid:129)
Padma Ganasala
Editors
Advances in Biomedical
Engineering and Technology
Select Proceedings of ICBEST 2018
123
Editors
Albert A.Rizvanov BikeshKumar Singh
Kazan FederalUniversity Department ofBiomedical Engineering
Kazan,Tatarstan Republic, Russia National Institute ofTechnology Raipur
Raipur, India
PadmaGanasala
GayatriVidya ParishadCollege
of Engineering
Visakhapatnam, Andhra Pradesh, India
ISSN 2195-271X ISSN 2195-2728 (electronic)
Lecture Notesin Bioengineering
ISBN978-981-15-6328-7 ISBN978-981-15-6329-4 (eBook)
https://doi.org/10.1007/978-981-15-6329-4
©SpringerNatureSingaporePteLtd.2021
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Organising Committee of ICBEST-2018
Chief Patron
Prof. A. M. Rawani, Director, NIT Raipur
Patron
Prof. S. Sanyal, TEQIP III Coordinator, NIT Raipur
Internal Adivsory Committee
Dr. Shrish Verma, Dean Academics, NIT Raipur
Dr. A. P. Rajimwale, Dean Faculty Welfare, NIT Raipur
Dr. P. Diwan, Dean, Student Welfare, NIT Raipur
Dr. (Mrs.) Shubhrata Gupta, Dean, R & C, NIT Raipur
Dr. G. D. Ramtekkar, Dean, P & D, NIT Raipur
Dr. P. Y. Dhekne, Registrar, NIT Raipur
Chairman
Dr. Bikesh Kumar Singh, Department of Biomedical Engineering, NIT Raipur
v
vi OrganisingCommitteeofICBEST-2018
Conference Secretaries
Dr. Neelamshobha Nirala, Department of Biomedical Engineering, NIT Raipur
Dr. Arindam Bit, Department of Biomedical Engineering, NIT Raipur
Dr. Saurabh Gupta, Department of Biomedical Engineering, NIT Raipur
Contents
ExtractionandPhytochemicalAnalysisofCocciniaindicaFruitUsing
UV-VIS and FTIR Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Alok Sharma, Bidyut Mazumdar, and Amit Keshav
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
3 Result and Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Numerical Simulation Method to Predict Air Flow and Contaminant
Control in a Multiple Bed Intensive Care Unit of Hospital . . . . . . . . . . 9
Arvind Kumar Sahu, Shobha Lata Sinha, and Tikendra Nath Verma
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Governing Equations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 Result and Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
A Simple Robust Image Processing Algorithm for Analysis
of Static Foot Pressure Intensity Image to Detect Foot Risk
Areas in Diabetic Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Hari S. Nair, Navya Thomas, and R. Periyasamy
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2 Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.1 Foot Image Data Acquisition. . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2 Image Processing Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
vii
viii Contents
Heavy Metal Ions Detection by Carbon Paste Electrode
as an Electrochemical Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Arti Mourya, Bidyut Mazumdar, and Sudip K. Sinha
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2 Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.1 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.2 Preparation of Electrode as a Sensor . . . . . . . . . . . . . . . . . . . . . . 30
3 Result and Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.1 Cyclic Voltammetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2 Effect of Operating Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.3 Electrochemical Impedance Spectroscopy (EIS) . . . . . . . . . . . . . . 33
4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Median Filtering Detection Using Markov Process
in Digital Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Saurabh Agarwal and Satish Chand
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2 Feature Construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3 Experimental Setup and Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.2 Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Differential of EMG Activity of Selected Calf Muscle During DLHR
Exercise in Relation to Performance Level. . . . . . . . . . . . . . . . . . . . . . . 43
Monika, L. M. Saini, and Saravjeet Singh
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2 Material and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.1 Data Acquisition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3 Result and Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Advanced Encryption Standard Algorithm in Multimodal
Biometric Image. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Sharmila S. More, Bhawna Narain, and B. T. Jadhav
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
2 Multimodal Biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
2.1 Role of Multimodal Biometrics in AES . . . . . . . . . . . . . . . . . . . . 56
3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.1 Proposed Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.2 Module Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Contents ix
4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
In-Silico Construction of Hybrid ORF Protein to Enhance Algal Oil
Content for Biofuel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Mohit Nigam, Ruchi Yadav, and Garima Awasthi
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
2 Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3 Result and Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.1 Identification of Algal Gene and Its Functional Protein. . . . . . . . . 69
3.2 Hybrid ORF Construction Using Selected Conserved Regions
of Superfamilies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.3 Hybrid ORF Clone Designed Using Vector NTI Tool Kit. . . . . . . 70
3.4 Multi-template Homology Modeling of Hybrid ORF Protein
Using Schrödinger Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
3.5 Protein Structure Prediction of the Constructed Hybrid ORF
by Phyre2 Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
ANFIS Detects the Changes in Stressful Patterns of Sleep EEG . . . . . . 91
Prabhat Kumar Upadhyay and Chetna Nagpal
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
2.1 Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
2.2 Neuro-fuzzy System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
2.3 Rules for Manual Scoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
2.4 Observations on Fuzziness in Stress. . . . . . . . . . . . . . . . . . . . . . . 94
3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
3.1 Data Recording. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
3.2 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
3.3 Sleep Stage Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
3.4 Stress Level Classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Recent Advances in Deep Learning Techniques and Its Applications:
An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Abhishek Hazra, Prakash Choudhary, and M. Sheetal Singh
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
2 Overview of Deep Learning Techniques. . . . . . . . . . . . . . . . . . . . . . . . 105
2.1 Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . 105