Computational Intelligence in Healthcare

✍ Scribed by Amit Kumar Manocha, Shruti Jain, Mandeep Singh, Sudip Paul

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 418
• Series: Health Information Science
• Edition: 1
• Category: Library

✦ Synopsis

Artificial intelligent systems, which offer great improvement in healthcare sector assisted by machine learning, wireless communications, data analytics, cognitive computing, and mobile computing provide more intelligent and convenient solutions and services. With the help of the advanced techniques, now a days it is possible to understand human body and to handle & process the health data anytime and anywhere. It is a smart healthcare system which includes patient, hospital management, doctors, monitoring, diagnosis, decision making modules, disease prevention to meet the challenges and problems arises in healthcare industry. Furthermore, the advanced healthcare systems need to upgrade with new capabilities to provide human with more intelligent and professional healthcare services to further improve the quality of service and user experience. 

To explore recent advances and disseminate state-of-the-art techniques related to intelligent healthcare services and applications. This edited book involved in designing systems that will permit the societal acceptance of ambient intelligence including signal processing, imaging, computing, instrumentation, artificial intelligence, internet of health things, data analytics, disease detection, telemedicine, and their applications. As the book includes recent trends in research issues and applications, the contents will be beneficial to Professors, researchers, and engineers. 

This book will provide support and aid to the researchers involved in designing latest advancements in communication and intelligent systems that will permit the societal acceptance of ambient intelligence. This book presents the latest research being conducted on diverse topics in intelligence technologies with the goal of advancing knowledge and applications healthcare sector and to present the latest snapshot of the ongoing research as well as to shed further light on future directions in this space. The aim of publishing the book is to serve for educators, researchers, and developers working in recent advances and upcoming technologies utilizing computational sciences.

✦ Table of Contents

Preface
Acknowledgment
Contents
About the Editors
Measurement of Human Bioelectricity and Pranic Energy of Different Organs: A Sensor and CPS-Based Approach
1 Introduction
1.1 Human Bioelectricity
1.2 Global Understanding on Consciousness
1.3 Kirlian Photography
1.4 Energy Measurement
1.5 Bioelectricity
1.6 Pranik Urja
1.7 The Human Body and Seven Chakras
1.8 Aura of the Human Body
1.9 Yin and Yang Energy
1.10 Quantum Consciousness
1.11 Science of Meditation and Its Effect on the Human Body
1.12 Science of Mantra and Its Effect on the Human Body
1.13 Science of Yajna (Yagya) and Its Effect on the Human Body
1.14 Artificial Intelligence for Health Informatics
1.15 Health Sensor Data Management
1.16 Multimodal Data Fusion for Healthcare
1.17 Heterogeneous Data Fusion and Context-Aware Systems for the Internet of Things Health
2 Literature Survey
3 The Methodology and Protocols Followed
3.1 Experimental Setup of an Expert System
4 Results and Discussions
4.1 Results, Interpretation, and Analysis on Healthcare Experiments
4.2 Head System
4.3 Immune System
4.4 Musculoskeletal System
5 Applications of Yagya and Mantra Therapy in Kirlian Captures
5.1 Science of Mantra and Its Effect on the Human Body
6 Future Research Perspectives
7 Novelty of the Research
8 Recommendations
9 Conclusion
Key Terms and Definitions
Annexure
Agreement Letter
Consent for Research Purposes
The EORTC QLQ (C-30)
Data Readings
References
Additional Readings
Development of Compression Algorithms for Computed Tomography and Magnetic Resonance Imaging
1 Introduction
1.1 Image Compression
1.2 Coding Redundancy
1.3 Inter-pixel Redundancy
1.4 Psycho-visual Redundancy
1.5 Image Compression Model
1.6 Classification of Image Compression
1.7 Quality Measures for Image Compression
1.8 Lossless Compression
1.9 Lossy Compression
1.10 Medical Image Compression
2 Wavelet Transform
2.1 Wavelet Transform-Based Compression
2.2 Significance of Wavelet Analysis
2.3 Selection of Decomposition Level Based on Quality of Image Compression
3 Encoding
3.1 Encoding the Images
3.2 Types of Encoding
3.2.1 Embedded Wavelet
3.2.2 SPIHT
3.2.3 Spatial Orientation Tree Wavelet
3.2.4 Wavelet Difference Reduction
4 Lossless Compression
4.1 Huffman Coding
4.2 Arithmetic Coding
5 Conclusion
References
Realization of Carry-Free Adder Circuit Using FPGA
1 Introduction
2 Proposed Methodology
3 Results and Discussion
4 Conclusion
References
Telemedicine and Telehealth: The Current Update
1 Introduction to Telemedicine and Telehealth
2 Birth of Telemedicine
2.1 Rebirth of Telemedicine
2.2 Modern Telemedicine
3 General Architecture of Telemedicine
4 Methods and Modalities of Telemedicine [12, 13]
5 Categories of Telemedicine
6 Risks Involved with Telemedicine
7 Telemedicine in India
8 Telemedicine During the COVID-19 Pandemic
9 Conclusion
References
Advancements in Healthcare Using Wearable Technology
1 Introduction
2 Classification of Human Wearable Devices
3 Quality Parameters of Wearable Devices
4 Applications of Wearable Technology in Different Sectors
4.1 In the Mining Sector
4.2 In Sports and Fitness
4.3 Emergency Services: Firefighters, Police Officers, and Paramedics
4.4 In Wholesale and Retail
4.5 In Travel and Hospitality
4.6 In Logistics
4.7 In Healthcare and Medicine
4.8 In the Oil and Gas Industry
5 Wearable Devices with Internet of Things Technology for Healthcare Monitoring
5.1 IoT in Vital Signs Monitoring
5.1.1 Pulse Rate and Heart Rate
5.2 IoT in ECG Monitoring
5.3 IoT in EEG Monitoring
5.4 IoT in Clinical Trials
6 Challenges in Wearable Technology
References
Machine and Deep Learning Algorithms for Wearable Health Monitoring
1 Introduction
2 Key Technologies of WHM
2.1 Generic System Architecture of WHM
2.2 Device or System of WHM
2.3 Vital Signals for WHM
2.4 Data Analysis of Vital Signals Used for WHM
3 Traditional Machine Learning-Based Approaches
3.1 ANN
3.2 Kriging Model
3.3 SVM
3.4 PCA
3.5 k-NN
3.6 Other Traditional ML Algorithms
4 Advanced Methods Based on Deep Learning
4.1 RBM
4.2 Autoencoders
4.3 Sparse Coding
4.4 CNNs
4.5 RNN
4.6 Generative Adversary Network
5 Algorithms, Application, and Frameworks of Different ML Methods for WHM
5.1 Comparison of Different DL Algorithms for WHM
5.2 Different DL Applications in WHM Systems
5.3 Hardware and Software Frameworks for DL Implementation
6 Challenges and Open Directions in WHM Based on ML or DL Methods
6.1 Challenges of ML- or DL-Based WHM
6.2 Open Research Directions of ML-/DL-Based WHM
7 Conclusions
References
Characterization of Signals of Noncontact Respiration Sensor for Emotion Detection Using Intelligent Techniques
1 Introduction
2 Physiological Aspects of Emotion and Respiration
2.1 Brain and Emotion
2.2 Brain and Respiration
2.3 Relation Between Respiration and Emotion
3 Noncontact Respiration Rate Detector and Data Acquisition
4 Methodology
5 Results and Discussion
5.1 Wavelet Transform
5.2 Principal Component Analysis
5.3 Classification of Signals Based on the Signals
6 Conclusion
References
Benefits of E-Health Systems During COVID-19 Pandemic
1 Introduction
2 Literature Survey
3 E-Health Methods and System Study
3.1 Techniques that Are Used to Build E-Health Framework
3.1.1 Health Monitoring and Detection Using Statistics
3.1.2 Health Detection Using Machine Learning
3.1.3 Health Monitoring Using Deep Learning
3.2 Techniques Used for Online Monitoring
3.2.1 Continuous Patient Monitoring (CPM)
3.2.2 Remote Patient Monitoring (RPM)
3.2.3 Early Detection of Onset of Health Issues
3.2.4 Shared Data Models
3.2.5 DIY Medical Instrumentation
3.2.6 Machine Learning Modeling
4 E-Health Framework Designs and Architectures
4.1 E-Health System Design I
4.2 E-Health System Design II
4.3 E-Health System Design III
4.4 E-Health System Design IV
4.5 E-Health System Design V
5 Proposed Architecture
5.1 E-Healthcare Service Interface
5.2 E-Healthcare Data Processing
5.3 E-Healthcare Business Logic and Rules
5.4 E-Healthcare Data Analytics Monitoring
5.5 Extended E-Health Services Platform
6 Comparative Study
7 Conclusion
References
Low-Cost Bone Mineral Densitometer
1 Introduction
2 Literature Survey
3 Methodology
3.1 Quantitative Ultrasound (QUS) and Its Basic Physics
3.2 System Description
3.3 Pulse Generator
3.4 Receiver
4 Results and Discussion
5 Conclusion
References
Smart Infusion Pump Control: The Control System Perspective
1 Introduction
1.1 Background and Literature
2 Infusion Pump, Definition, and Classification
2.1 Block Diagram of a Smart Infusion Pump
2.2 Infusion Pump Control Model for Drug Infusion
3 Methodology
3.1 PID Controller
3.1.1 Controller Tuning
PSO
3.2 Linear Quadratic Gaussian
4 Results and Discussion
5 Conclusion
References
Automated Detection of Normal and Cardiac Heart Disease Using Chaos Attributes and Online Sequential Extreme Learning Machine
1 Introduction
2 Methodology
2.1 Fundamentals of ELM
2.2 Analytical Concept of OSELM
3 Box Plot
4 Attributes’ Dimension Reduction by Generalized Discriminant Analysis
5 Chaos Attributes
5.1 Correlation Dimension
5.2 Detrended Fluctuation Analysis
5.3 Sample Entropy
5.4 Poincare Plot as SD1/SD2 Ratio
5.5 Hurst Exponent
5.6 Permutation Entropy
5.7 Improved Multiscale Permutation Entropy
5.8 Cumulative Bi-Correlation
6 Parameters Used for Simulation
7 Results
8 Discussion
9 Conclusion
References
Interference Reduction in ECG Signal Using IIR Digital Filter Based on GA and Its Simulation
1 Introduction
1.1 Electrocardiogram Signal
1.2 Digital Filters
1.3 Genetic Algorithm
2 Model Used to Generate ECG Signal
3 Design of IIR Digital Filters Based on GA
4 Simulation Results
5 Conclusion
Bibliography
Contactless Measurement of Heart Rate from Live Video and Comparison with Standard Method
1 Introduction
2 Experimental Method
2.1 Preprocessing
2.1.1 Face Detection
2.1.2 ROI Detection/Tracking
2.2 Signal Extraction
2.2.1 Applying Band-Pass Filtering
2.2.2 Averaging Pixel Intensity
2.3 Post-processing
2.3.1 Measuring the Heart Rate
3 Flow Chart Explanation
3.1 Importing Python Modules
3.2 Helper Methods (Defining Methods in Python)
3.3 Webcam Parameters
3.4 Output Video
3.5 Color Magnification Parameters
3.6 Output Display Parameters
3.7 Initialize Gaussian Pyramid
3.8 Band-pass Filter for Specified Frequencies
3.9 Grab a Pulse
3.10 Reconstruct Resulting Frame
4 Result and Discussion
5 Conclusion
References
Automatic Melanoma Diagnosis and Classification on Dermoscopic Images
1 Introduction
2 Related Works
3 Materials and Methods
3.1 Image Enhancement
3.2 Watershed Segmentation
3.3 Feature Extraction
3.4 Skin Lesion Classification
4 Experimental Analysis
4.1 Discussion
5 Conclusion
References
GI Cloud Design: Issues and Perspectives
1 Introduction
2 Problem Statement
3 Overview of the Cloud Environment
4 Services Architecture of the GI Cloud
4.1 Data as a Service (DaaS)
4.2 Software as a Service (SaaS)
4.3 Privacy and Security as a Service (P-SaaS)
4.4 Portability and Interoperability as a Service (P-IaaS)
4.5 Human Computing as a Service (HCaaS)
4.6 Generic Services
5 Discussions on the GI Cloud
6 Parametric Performance Evaluation of the GI Cloud
7 Summary and Conclusions
References
A Hybrid Method for Detection of Coronavirus Through X-rays Using Convolutional Neural Networks and Support Vector Machine
1 Introduction
2 Literature Work
3 Methodology
4 Environmental Setup
5 Evaluation Metrics and Result
6 Conclusion and Future Work
References
Feature Extraction Using GLSM for DDSM Mammogram Images
1 Introduction
2 Motivation
3 Problem Definition
4 Methodology
5 Results and Observations
6 Conclusion
References
Deep Learning-Based Techniques to Identify COVID-19 Patients Using Medical Image Segmentation
1 Introduction
2 Classification of Deep Learning Algorithms
3 Literature Survey
4 Motivation Behind the Work
5 Deep Learning-Based Proposed mMdel
6 Normal Encoder-Decoder Network (Fig. 8)
6.1 U-NET
6.2 U-NET Architecture
7 Attention Mechanism
7.1 Types of Attention
7.2 Transfer Learning
8 Problem
9 Solution Approach
9.1 Attention U-NET
9.2 ResNet-34
10 Conclusion
References
Emerging Trends of Bioinformatics in Health Informatics
1 Introduction
2 Defining Bioinformatics
2.1 Tools of Bioinformatics
2.2 Applications of Bioinformatics
2.2.1 Medicine
2.2.2 Research
2.2.3 Agriculture
3 Healthcare and Healthcare Informatics
4 Healthcare Informatics and Bioinformatics
5 Big Data Technologies
5.1 Impact of Big Data in Bioinformatics
6 Bioinformatics Resources for Metabolomics
7 Bioinformatics Resources Towards Personalized Medicine
7.1 Hardware and Software
7.2 Natural Language Processing
7.3 Imaging Informatics
7.4 Computer-Aided Drug Design
8 Commercial Platforms for Healthcare
9 Challenges and Future of Bioinformatics in Healthcare
References
Computational Methods for Health Informatics
1 Introduction
2 Computational Methods for Health Informatics
3 Machine Learning for Health Informatics
4 Deep Learning for Health Informatics
5 Bioinformatics for Health Informatics
6 Conclusions
References
Computational Model of a Pacinian Corpuscle for Hybrid-Stimuli: Spike-Rate and Threshold Characteristics
1 Introduction
2 Methods
2.1 Model Description
2.2 Model Parameters and Approximations
2.2.1 Model Assumptions
3 Simulation of the Hybrid-Stimuli PC Model
3.1 Spike Response
3.2 Spike-Rate Characteristics
3.3 Threshold Characteristics
4 Conclusion and Future Work
References
Index