This book examines the use of biomedical signal processing—EEG, EMG, and ECG—in analyzing and diagnosing various medical conditions, particularly diseases related to the heart and brain. In combination with machine learning tools and other optimization methods, the analysis of biomedical signals greatly benefits the healthcare sector by improving patient outcomes through early, reliable detection. The discussion of these modalities promotes better understanding, analysis, and application of biomedical signal processing for specific diseases.
The major highlights of Biomedical Signal Processing for Healthcare Applications include biomedical signals, acquisition of signals, pre-processing and analysis, post-processing and classification of the signals, and application of analysis and classification for the diagnosis of brain- and heart-related diseases. Emphasis is given to brain and heart signals because incomplete interpretations are made by physicians of these aspects in several situations, and these partial interpretations lead to major complications.
- Examines modeling and acquisition of biomedical signals of different disorders
- Discusses CAD-based analysis of diagnosis useful for healthcare
- Includes all important modalities of biomedical signals, such as EEG, EMG, MEG, ECG, and PCG
- Includes case studies and research directions, including novel approaches used in advanced healthcare systems
This book can be used by a wide range of users, including students, research scholars, faculty, and practitioners in the field of biomedical engineering and medical image analysis and diagnosis.
Table of Contents
1. Automatic Sleep EEG Classification with Ensemble Learning Using Graph Modularity
Kamakhya Narain Singh, Sudhansu Shekhar Patra, Swati Samantaray, Sudarson Jena, Jibendu Kumar Mantri, and Chinmaya Misra
2. Recognition of Distress Phase Situation in Human Emotion EEG Physiological Signals
Abdultaofeek Abayomi, Oludayo O. Olugbara, and Delene Heukelman
3. Analysis and Classification of Heart Abnormalities
4. Diagnosis of Parkinson’s Disease Using Deep Learning Approaches: A Review
Priyanka Khanna, Mridu Sahu, and Bikesh Kumar Singh
5. Classifying Phonological Categories and Imagined Words from EEG Signal
Ashwin Kamble, Pradnya H Ghare, and Vinay Kumar
6. Blood Pressure Monitoring Using Photoplethysmogram and Electrocardiogram Signals
Jamal Esmaelpoor and Zahra Momayez Sanat
7. Investigation of the Efficacy of Acupuncture Using Electromyographic Signals
Kim Ho Yeap, Wey Long Ng, Humaira Nisar, and Veerendra Dakulagi
8. Appliance Control System for Physically Challenged and Elderly Persons through Hand Gesture-Based Sign Language
G. Boopathi Raja
9. Computer-Aided Drug Designing – Modality of Diagnostic System
Shalini Ramesh, Sugumari Vallinayagam, Karthikeyan Rajendran, Sasireka Rajendran, Vinoth Rathinam, and Sneka Ramesh
10. Diagnosing Chest-Related Abnormalities Using Medical Image Processing through Convolutional Neural Network
Vignessh B., Reena Raj, and Balakrishnakumar
11. Recent Trends in Healthcare System for Diagnosis of Three Diseases Using Health Informatics
Shawni Dutta and Samir Kumar Bandyopadhyay
12. Nursing Care System Based on Internet of Medical Things (IoMT) through Integrating Non-Invasive Blood Sugar (BS) and Blood Pressure (BP) Combined Monitoring
Patrali Pradhan, Subham Ghosh, and Biswarup Neogi
13. Eye Disease Detection from Retinal Fundus Image Using CNN
Padma Selvaraj and Pugazendi Rajagopal
Dr. Varun Bajaj has been working as a faculty in the discipline of Electronics and Communication Engineering, at Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Jabalpur, India since 2014.
G R Sinha is Adjunct Professor at International Institute of Information Technology Bangalore (IIITB) and currently deputed as Professor at Myanmar Institute of Information Technology (MIIT) Mandalay Myanmar.
Dr. Chinmay Chakraborty is working as an Assistant Professor (Sr.) in the Dept. of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, India.