Unit 1 Overview of Biomedical Systems and Computational Intelligence Techniques

5 lecture hours

• Early Biomedical Systems
• Medical Data
• Biological Data
• Medical Instrumentation
• Medical Systems
• Drawbacks of Traditional Approaches
• Numerical vs
• Symbolic Approaches
• The Need for Computational Intelligence in Biomedical Engineering
• Bioelectric Signals and Electrode Theory
• Signal Processing and Feature Extraction
• Unsupervised and Supervised Learning Algorithms
• Fuzzy Logic
• Evolutionary Algorithms

Unit 2 Artificial Neural Networks

• Objectives of Neural Networks
• Biological Foundations of Neural Networks
• Early Neural Models
• Precursor to Current Models: Pattern Classification
• Classes of Neural Networks: Basic Network Properties
• Classification Models
• Association Models
• Optimization Models
• Self- Organization Models
• The Adaline (ALC)
• Perceptron: Structure
• Single-Multi Layer Perceptron
• Activation Function
• The Madaline
• Feedforward Network
• Back Propagation Network: Algorithm
• Modified BP Algorithms
• The XOR problem
• Problem Solving Applications. 11 Subject Code: 3723118 w.e.f

Unit 3 Support Vector Machines & Alternative Algorithm

10 lecture hours

• Support Vector Classifier
• Support Vector Regression
• Training the Support Vector Machine
• Genetic Algorithm: Foundations
• Representation Schemes
• Genetic Operators
• Vision: Pattern recognition
• Case Study: - Classification of Heart Disease database using Learning Vector Quantization ANN, Access Control by Face Recognition using Backpropagation Neural Network
• Analysis of Electromyography Signals
• Gait and Movement Pattern Analysis

Unit 4 Fuzzy Sets and Fuzzy Logic

8 lecture hours

• The Case for Imprecision
• The Utility & Limitations of Fuzzy Systems
• Statistics and Random Processes
• Uncertainty and Information
• Fuzzy Sets and Membership
• Chance versus Fuzziness
• Fuzzy Set Operations
• Properties of Fuzzy Sets
• Fuzzy Relations: Cardinality of Fuzzy Relations
• Operations on Fuzzy Relations
• Properties of Fuzzy Relations
• Tolerance and Equivalence Relations: Crisp Equivalence & Crisp Tolerance Relations
• Fuzzy Tolerance & Equivalence Relations

Unit 5 Fuzzy Logic Systems

8 lecture hours

• Properties of Membership Functions
• Fuzzification, and Defuzzification: Features of the Membership Function
• Various Forms Fuzzification-Defuzzification to Crisp Sets
• λ-cuts for Fuzzy Relations
• Defuzzification to Scalars
• Hybrid Systems: ANFIS: Adaptive Neuro-Fuzzy Inference System
• Fuzzy Support Vector Machines