Cardio-Respiratory Signal Processing and Classification
Trends, Applications, and Future Directions
Series: Biomedical Signal and Image Processing;
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Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 20 August 2026
- ISBN 9781032797038
- Binding Hardback
- No. of pages248 pages
- Size 234x156 mm
- Language English
- Illustrations 75 Illustrations, black & white; 13 Halftones, black & white; 62 Line drawings, black & white; 22 Tables, black & white 700
Categories
Short description:
The 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, and novel HRV analysis.
MoreLong 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.
MoreTable of Contents:
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
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