Full Description
Machine Learning Models and Architectures for Biomedical Signal Processing presents the fundamental concepts of machine learning techniques for bioinformatics in an interactive way. The book investigates how efficient machine and deep learning models can support high-speed processors with reconfigurable architectures like graphic processing units (GPUs), Field programmable gate arrays (FPGAs), or any hybrid system. This great resource will be of interest to researchers working to increase the efficiency of hardware and architecture design for biomedical signal processing and signal processing techniques.
Contents
Section 1: Introduction to bioinformatics
1.1 Recent trends of bioinformatics
1.2 Biomedical signal processing technique
1.3 Transfer Learning based Arrhythmia classification using Electrocardiogram
Section 2: Machine learning models for biomedical signal processing
2.1 Exploring Machine Learning Models for Biomedical Signal Processing: A Comprehensive Review
2.2 Machine Learning for Audio Processing: From Feature Extraction to Model Selection
2.3 Pre-processing of MRI images suitable for Artificial Intelligence-based Alzheimer's Disease classification
2.4 Machine Learning Models for Text and Image Processing
2.5 Assistive Technology for Neuro-rehabilitation Applications Using Machine Learning Techniques
2.6 Deep Learning Architectures in Computer Vision based Medical Imaging Applications with Emerging Challenges
2.7 Relevance of Artificial Intelligence, Machine Learning, and Biomedical Devices to Healthcare Quality and patient Outcomes
2.8 AI-Based ECG Signal processing applications
2.9 Deep Learning Approach for the Prediction of Skin Diseases
Section 3: Brain computer interfaces (BCI)
3.1 Brain-Computer Interface
3.2 Analysis on Types of Brain-Computer Interfaces for Disabled Person
3.3 Brain Computer Interfaces for elderly and disabled person
Section 4: Real time architecture design for biomedical signals
4.1 Machine learning model implementation with FPGA'S
4.2 Smart Biomedical Devices for Smart Healthcare
4.3 FPGA implementation for explainable machine learning and deep learning models to real time problems
Section 5: Software and Hardware-based Applications for biomedical Informatics
5.1 Software Applications for Biometric Informatics
5.2 Smart Medical Devices: Making Health Care More Intelligent
5.3 Security modules for biomedical signal processing
5.4 Artificial intelligence-based diagnostic tool for cardiovascular risk prediction
5.5 Machine Learning Algorithm approach in risk prediction of Liver Cancer
