Non-Stationary and Nonlinear Data Processing for Automated Computer-Aided Medical Diagnosis

Tripathy, Rajesh Kumar (EDT)/ Pachori, Ram Bilas (EDT)/ Padhy, Sibasankar (EDT)

Academic Press Inc（2026/06発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 444 p.
• 言語 ENG
• 商品コード 9780443314261
• DDC分類 660.6

Full Description

Non-Stationary and Nonlinear Data Processing for Automated Computer-Aided Medical Diagnosis demonstrates the applications of machine learning and deep learning combined with signal processing techniques for human-machine interface applications using EMG signals. The book includes the analysis and classification of various heart diseases based on bio-signals like electrocardiogram (ECG), photoplethysmography (PPG), and phonocardiogram (PCG) signals. Various machine learning approaches, including advanced algorithms like multivariate signal processing, time-frequency analysis, and nonlinear signal processing are covered for CAD of neural, muscular, and cardiovascular diseases. The methods for CAD of various brain disorders are also included.

Presented techniques utilize advanced non-stationary and nonlinear signal processing, along with machine learning and deep learning-based classification processes. CAD methods for diagnosing various neurological diseases are based on bio-signals such as electroencephalogram (EEG) and magnetoencephalogram (MEG), as well as medical images like magnetic resonance imaging (MRI) and computerized tomography (CT). Finally, the book addresses various types of medical signals and images, integrating nonlinear and non-stationary signal processing, machine learning, and deep learning within the CAD framework for diagnosing various diseases.

Contents

1. Introduction to Computer-Aided Medical Diagnosis Systems
2. Advanced Signal Processing and Machine Learning Techniques
3. EEG-Based Imagined Speech Recognition for Brain-Computer Applications
4. Automated Emotion Detection Using Multi-Modal Data
5. Visual Cognitive Systems Using EEG and MEG for BCI Applications
6. ECG Sensor-Based Devices for Cardiac Disease Diagnosis
7. Automated Detection of Neurological Disorders via Voiced Speech Patterns
8. EEG-Based Diagnosis Systems for Sleep Disorders
9. Automated Brain Cancer Diagnosis Using MRI
10. Automated Eye Disease Diagnosis Using Ophthalmoscopic Images
11. PPG-Based Diagnosis System for Cardiovascular Disorders
12. EMG Signal-Based Devices for Neuromuscular Diseases
13. Wearable Systems for Real-Time Disease Diagnosis and Predictive Analytics
14. Computer-Aided Detection of Thoracic Diseases Using X-Ray Images
15. Computer-Aided Detection of Kidney Diseases Using Ultrasound Images
16. IoT-Enabled Diagnosis System for Telemedicine Applications