U-Healthcare Monitoring Systems: Volume 1: Design and Applications

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U-Healthcare
Monitoring Systems,
Volume 1: Design and Applications
Copyright
Contributors
Preface
1
Wearable U-HRM device for rural applications
	Introduction
	U-Healthcare System in India
	Application
	Open Issues and Problems
	Requirements of a Healthcare System
	Requirement of Wearable Devices
	Implementation
	Measurement of Heart Rate and Body Temperature
	Discussion
	Conclusion and Future Trends
	Glossary
	References
2
A robust framework for optimum feature extraction and recognition of P300 from raw EEG
	Introduction
	Literature Survey
	The Framework
		Initialization
		Model Setup
			Preprocessors
			Custom epoch extractor (Cepex)
		Postprocessor
		Classification
	Results and Discussion
		The Dataset
		Framework Results
			Preprocessing
			Postprocessing
			Classification
			Performance comparison
			Open source implementation
	Conclusion and Future Work
	References
3
Medical image diagnosis for disease detection: A deep learning approach
	Introduction
		Related Work
	Requirement of Deep Learning Over Machine Learning
		Fundamental Deep Learning Architectures
			Multilayer Perceptron
			Deep Belief Networks
			Stacked Auto-Encoder
			Convolution Neural Networks
				Convolution architecture
					Convolution layers
					Stride and pooling layers
					Fully connected
			Recurrent Neural Network
				How does LSTM improve the RNN?
	Implementation Environment
		Toolkit Selection/Evaluation Criteria [13]
		Tools and Technology Available for Deep Learning [13]
		Deep Learning Framework Popularity Levels [14]
	Applicability of Deep Learning in Field of Medical Image Processing [15]
		Current Research Applications in the Field of Medical Image Processing
	Hybrid Architectures of Deep Learning in the Field of Medical Image Processing [17]
	Challenges of Deep Learning in the Fields of Medical Imagining [17]
	Conclusion
	References
	Further Reading
4
Reasoning methodologies in clinical decision support systems: A literature review
	Introduction
	Methods
		Research Questions
		Selection Criteria
		Search Strategy
	Literature Review and Results
		Paper Screening
		Selecting the Most Relevant Papers
		Extracting and Analyzing Concepts
			Rule-based reasoning
			Ontology reasoning
			Ontology-based fuzzy decision support system
			Case-based reasoning
		Current Challenges and Future Trends
	Conclusion
	References
5
Embedded healthcare system for day-to-day fitness, chronic kidney disease, and congestive heart failure
	Ubiquitous Healthcare and Present Chapter
	Introduction
	Frequency-Dependent Behavior of Body Composition
	Bioimpedance Analysis for Estimation of Day-to-Day Fitness and Chronic Diseases
	Measurement System for Body Composition Analysis Using Bioimpedance Principle
		Measurement Electrodes
		AFE4300 Body Composition Analyzer
		Statistical Analysis
		Validation of Developed Model
	Database Generation
	Predictive Regression Model for Day-to-Day Fitness
	Predictive Regression Model for CKD
	Predictive Regression Model for CHF
	Discussion
	Conclusion
	References
6
Comparison of multiclass and hierarchical CAC design for benign and malignant hepatic tumors
	Introduction
	Materials and Methods
		Dataset Collection
		Data Set Description
		Data Collection Protocol
		ROIs Selection
		ROI Size Selection
		Proposed CAC System Design
		Feature Extraction Module
		Classification Module
			SSVM classifier
	Results
		Experiment 1: To Evaluate the Potential of the Threeclass SSVM Classifier Design for the Characterization of Benign and Ma ...
		Experiment 2: To Evaluate the Potential of SSVM-Based Hierarchical Classifier Design for Characterization Between Benign a ...
		Experiment 3: Performance Comparison of SSVM-Based Three-Class Classifier Design and SSVM-Based Hierarchical Classifier De ...
	Discussion and Conclusion
	References
	Further Reading
7
Ontology enhanced fuzzy clinical decision support system
	Introduction
	Problem Description
	Related Work
	The Combining of Ontology and Fuzzy Logic Frameworks
	System Architecture and Research Methodology
		Knowledge Acquisition
		Semantic Modeling
		The Fuzzy Modeling
			Raw EHR data preprocessing
			Features definition and fuzzification
			Features selection and DT induction
		Knowledge Reasoning
			Initial fuzzy knowledge base construction
			Enhancement of the generated fuzzy knowledge
			The inference engine
			The defuzzification process
			Framework evaluation
	Conclusion
	References
	Further Reading
8
Improving the prediction accuracy of heart disease with ensemble learning and majority voting rule
	Introduction
	Review of Related Works
	Ensemble Learning Systems
		Diversity
		Training Ensemble Members
		Combining Ensemble Members
	Materials and Methods
		Logistic Regression
		Multilayer Perceptron
		Naïve Bayes
		Combining Classifiers Using Majority Vote Rule
		Performance Metrics
	Result and Discussion
	Conclusion and Future Directions
	References
	Further Reading
9
Machine learning for medical diagnosis: A neural network classifier optimized via the directed bee colony optimization alg ...
	Introduction
	Neural Network Dynamics
	Directed Bee Colony Optimization Algorithm
	Experimental Setup
	Result and Discussion
	Conclusion
	References
	Further Reading
10
A genetic algorithm-based metaheuristic approach to customize a computeraided classification system for enhanced screen fi ...
	Introduction
	Methodology for Designing a CAD System for Diagnosis of Abnormal Mammograms
		Image Data Set Description
		Enhancement Methods
			Alpha trimmed mean filter
			Contrast adjustment
			Histogram equalization
			Contrast limited adaptive histogram equalization
			Recursive mean separated histogram equalization
			Contra harmonic mean filter
			Mean filter
			Median filter
			Hybrid median filter
			Morphological enhancement
			Morphological enhancement and contrast stretching
			Unsharp masking
			Unsharp masking and contrast stretching
			Wavelet based subband filtering
		Selection of ROIs
			Selection of ROI size
		Feature Extraction: Gabor Wavelet Transform Features
		SVM Classifier
	Experimental Results
		Obtaining the Accuracies of Classification of Abnormal Mammograms After Enhancement With Alpha Trimmed Mean Filter
		Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With Contrast Stretching
		Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With Histogram Equalization
		Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With CLAHE
		Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With RMSHE
		Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With Contra-Harmonic Mean
		Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With Mean Filter
		Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With Median Filter
		Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With Hybrid Median Filter
		Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Morphological Enhancement
		Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Morphological Enhancement, Followed B ...
		Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Unsharp Masking
		Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After UMCA
		Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Wavelet-Based Subband Filtering
	Comparison of Classification Performance of the Enhancement Methods
	Genetic Algorithm-Based Metaheuristic Approach to Customize a Computer-Aided Classification System for Enhanced Mammograms
	Conclusion
	Future Scope
	References
	Further Reading
11
Embedded healthcare system based on bioimpedance analysis for identification and classification of skin diseases in Indian ...
	Introduction
	Need of Bioimpedance Measurement for Identification and Classification of Skin Diseases
	System Developed for the Measurement of Human Skin Impedance
		Skin Electrode
		Impedance Converter IC AD5933
		Microcontroller IC CY7C68013A
		Personal Computer
	Generation of a Database of Indian Skin Diseases
	Impedance Indices for Identification and Classification of Skin Diseases
	Identification of Skin Diseases
		Wilcoxon Signed Rank Test
	Measures of Classification of Skin Diseases
		Box and Whisker Plot of Impedance Indices
		Mean and Standard Deviation of Impedance Indices
	Classification of Skin Diseases Using Modular Fuzzy Hypersphere Neural Network
	Conclusion
	References
12
A hybrid CAD system design for liver diseases using clinical and radiological data
	Introduction
	Methodology Adopted
		CAD System Design A
			Dataset description
			Feature extraction
			Feature classification
			Classification results
		CAD System Design B
			Dataset description
			Feature extraction
			Feature classification
			Classification results
		CAD System Design C: Hybrid CAD System
	Discussion
	Conclusion and Future Scope
	References
	Further Reading
13
Ontology-based electronic health record semantic interoperability: A survey
	Introduction
	EHR and Its Interoperability
		Introduction and Definitions
		The Interoperability Benefits
		The Different Interoperability Levels
		EHR Semantic Interoperability Requirements
	E-Health Standards and Interoperability
	Ontologies and Their Role in EHR
	Methods
		Research Questions
		Search Strategy
		Search Results
		Discussion
	The Challenges of EHR Semantic Interoperability
	Conclusion
	References
14
A unified fuzzy ontology for distributed electronic health record semantic interoperability
	Introduction
		EHR Clinical and Business Benefits and Outcomes
		EHR Semantic Interoperability Barriers and Obstacles
			The heterogeneity problem
			Dynamics and complexities of healthcare systems
			The challenges of standards
	Related Work
	Preliminaries
		Techniques and Approaches of EHR Semantic Interoperability
		EHR Standards
		Ontologies
		Terminologies
		Semantic Interoperability Frameworks
		Privacy and Security in EHR Systems
	Methodology
		The Proposed Framework
		A Prototype Problem Example
		A Comparison Study
	Conclusion
	References
	Further Reading
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	K
	L
	M
	N
	O
	P
	R
	S
	T
	U
	V
	W
	X
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