Cardio-Respiratory Signal Processing and Classification
Description
Non-invasive measurements of cardio-respiratory signals has improved diagnosis and prognosis in human health. This edited book invites original theoretical, practical, and review chapters aimed at proposing advancements in cardio-respiratory signal processing methods for healthcare applications. Exemplary themes of interest covered in this title include cardio-respiratory signal processing challenges using complex physiological data, novel HRV analysis, and data extracted from modern wearables and biosensors.
Features:
- Provides a detailed explanation and signal processing analysis of cardio-respiratory signals.
- Reports novel signal processing and time-frequency methods for cardio-respiratory signals.
- Presents the theoretical basis of cardio-respiratory analysis and state-of-the-art applications.
- Explains new and improved techniques and theories related to cardio-respiratory signal analysis.
- Combines the primary knowledge of cardio-respiratory signal analysis and processing, from theory and applications.
This book is aimed at researchers and graduate students in biomedical signal processing, signal processing, and electrical engineering.
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Person
Ganesh R. Naik is a globally recognized biomedical engineer and signal processing expert, ranked among the top 2% of researchers worldwide by Stanford University. He holds a PhD from RMIT University and currently serves as an Associate Professor at Torrens University Australia. A highly prolific researcher, he has edited 16 books, authored two books, and published more than 175 scientific papers. Dr. Naik is an associate editor for several prestigious journals, including IEEE Access. His career spans influential research roles at Flinders University, Western Sydney University, and the University of Technology Sydney, where he made significant contributions to sleep health and wearable technologies. His achievements have been recognized through numerous competitive fellowships, including those awarded by the Royal Academy of Engineering (UK), the Australian Government, and Germany's Baden-Württemberg Scholarship program.
Content
Chapter 1. Time-Frequency Analysis of ECG Signals. Chapter 2. Detection of Cardiac Signals Abnormalities using MUSIC and Random Subspace Methods. Chapter 3. ECG Signal Analysis Using Dual-Tree Complex Wavelet Transform and Bagging Ensemble Machine Learning. Chapter 4. Heart and Respiratory Sound Expert. Chapter 5. Individual Discriminating Power Assessment of ECG Multi-Scale Entropies for Cardiovascular Disease Detection. Chapter 6. Development of a Far Infrared Thermal Sensor-based Contactless Breath Rate Measuring System with a Constraint on Resources. Chapter 7. Electrocardiographic Signature Assessment of Post-COVID-19 Syndrome via Machine Learning Algorithms in Patients with Comorbid Cardiovascular Conditions. Chapter 8. Real-time Deep Learning Pipeline for ECG Anomaly Detection. Chapter 9. Analysis of Ballistocardiography for Cardiac and Respiratory Monitoring in Sleep. Chapter 10. PhysioBot: A Deep Learning Chatbot for ECG-PPG Anomaly Detection. Chapter 11. LLM-Facilitated Differential Diagnosis of Cardiovascular Conditions from ECG and PPG
