1st Edition
Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare
In modern healthcare, various medical modalities play an important role in improving the diagnostic performance in healthcare systems for various applications, such as prosthesis design, surgical implant design, diagnosis and prognosis, and detection of abnormalities in the treatment of various diseases. Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare discusses the uses of analysis, modeling, and manipulation of modalities, such as EEG, ECG, EMG, PCG, EOG, MRI, and FMRI, for an automatic identification, classification, and diagnosis of different types of disorders and physiological states. The analysis and applications for post-processing and diagnosis are much-needed topics for researchers and faculty members all across the world in the field of automated and efficient diagnosis using medical modalities. To meet this need, this book emphasizes real-time challenges in medical modalities for a variety of applications for analysis, classification, identification, and diagnostic processes of healthcare systems. Each chapter starts with the introduction, need and motivation of the medical modality, and a number of applications for the identification and improvement of healthcare systems. The chapters can be read independently or consecutively by research scholars, graduate students, faculty members, and practicing scientists who wish to explore various disciplines of healthcare systems, such as computer sciences, medical sciences, and biomedical engineering.
This book aims to improve the direction of future research and strengthen research efforts of healthcare systems through analysis of behavior, concepts, principles, and case studies. This book also aims to overcome the gap between usage of medical modalities and healthcare systems. Several novel applications of medical modalities have been unlocked in recent years, therefore new applications, challenges, and solutions for healthcare systems are the focus of this book.
1. Classification of Alertness and Drowsiness States using the Complex Wavelet Transform based Approach for EEG Records 1.1 Introduction
1.2 Methodology
1.3 Results and Discussion
1.4 Conclusion
2. Stochastic Event Synchrony based on a Modified Sparse Bump Modeling: Application to PTSD EEG Signals
2.1 Introduction
2.2 Sparse Bump Modeling
2.3 Stochastic Event Synchrony
2.4 Synchro-squeezed wavelet transform
2.5 Stochastic event synchrony based on a modified bump modelling
2.6 Data description and preprocessing
2.7 Results
2.8 Conclusion
3. HealFavor: A Chatbot Application in Healthcare
3.1 Introduction
3.2 Theoretical Background
3.3 Data Preparation Methodology
3.4 Prototype System Architecture
3.5 Machine Translation
3.6 Evaluation
3.7 Conclusion
3.8 Future Works
4. Diagnosis of Neuromuscular Disorders using Machine Learning Techniques
4.1 Introduction
4.2 Literature Review
4.3 EMG Signal Classification Framework
4.4 Results and Discussion
4.5 Conclusion
5. Prosthesis control using undersampled surface electromyographic signals
5.1 Introduction
5.2 Myoelectric controlled prosthesis
5.3 sEMG signal recording in myoelectric prosthesis control
5.4 Conclusion and future scope
6. Title of chapter:Assessment and Diagnostic Methods for Coronavirus Disease 2019 (COVID-19)
6.1 Introduction
6.2 Clinical findings of COVID-19
6.3 Existing diagnostic tools
6.4 Current Screening tools for COVID-19
6.5 Capnogram features
6.6 "Proposed tools for early screening of COVID-19 using respired CO2 features"
6.7 Conclusion
7. Predictive Analysis of Breast Cancer using Infrared Images with Machine Learning Algorithms
7.1 Introduction
7.2 Methods & Materials
7.3 Classification using Machine Learning Models
7.4 Performance Evaluation Parameters
7.5 Classification Results & Analysis
7.6 Conclusion & Future Work
8. Histopathological Image Analysis and Classification Techniques for Breast Cancer Detection
8.1 Introduction
8.2 Methodology
8.3 Image Database
8.4 Performance Evaluation of CAD system
8.5 Results and Discussion
8.6 Conclusion
9. STUDY OF EMOTIONAL INTELLIGENCE & NEURO-FUZZY SYSTEM
9.1 Emotional Intelligence
9.2 Emotions and Cognitive Intelligent Systems (CIS)
9.3 Methods for Implementation of Emotional Intelligence
9.4 Artificial Neural Network and Fuzzy Inference System
9.5 The Integrated Neuro Fuzzy Approach
9.6 SUMMARY
10. ESSENTIAL STATISTICAL TOOLS FOR ANALYSIS OF BRAIN COMPUTER INTERFACE
10.1 Introduction to testing of hypothesis
10.2 Design of experiment
10.3 Completely Randomized Design
10.4 Randomized Block Design
10.5 Latin Square Design
10.6 Factorial Experiment
11. Brain Computer Interfaces: The basics, state of the art and future
11.1 Introduction
11.2 Signal acquisition techniques for BCI
11.3 BCI types and brain signal patterns
11.4 Signal processing
11.5 Software tools for BCI
11.6 Conclusion and future perspectives
12. Oriented Approaches for Brain Computing and Human Behavior Computing Using Machine Learning
12.1 Overview of Machine Learning (Definition Approaches)
12.2 Machine Learning Algorithm for Brain Computing
12.3 Machine Learning Algorithms for Human Behavior Computing
13. An Automated Diagnosis System for Cardiac Arrhythmia Classification
13.1 Introduction
13.2 The Human Heart
13.3 Different Segments of ECG
13.4 Database
13.5 proposed Methodology
13.6 Experimental Results
13.7 Conclusion
Biography
Varun Bajaj is working as a faculty in the discipline of Electronics and Communication Engineering, at Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Jabalpur, India.
G R Sinha is an Adjunct Professor at International Institute of Information Technology Bangalore (IIITB) and currently deputed as Professor at Myanmar Institute of Information Technology (MIIT) Mandalay Myanmar.
