Full Description
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.
Contents
1. Classification of Alertness and Drowsiness States using the Complex Wavelet Transform based Approach for EEG Records. 2. Stochastic Event Synchrony based on a Modified Sparse Bump Modeling: Application to PTSD EEG Signals. 3. HealFavor: A Chatbot Application in Healthcare. 4. Diagnosis of Neuromuscular Disorders using Machine Learning Techniques. 5. Prosthesis control using undersampled surface electromyographic signals. 6. Title of chapter:Assessment and Diagnostic Methods for Coronavirus Disease 2019 (COVID-19). 7. Predictive Analysis of Breast Cancer using Infrared Images with Machine Learning Algorithms. 8. Histopathological Image Analysis and Classification Techniques for Breast Cancer Detection. 9. Study of Emotional Intelligence & Neuro-Fuzzy System. 10. Essential Statistical Tools for Analysis of Brain Computer Interface. 11. Brain Computer Interfaces: The basics, state of the art and future. 12. Oriented Approaches for Brain Computing and Human Behavior Computing Using Machine Learning. 13. An Automated Diagnosis System for Cardiac Arrhythmia Classification.
